1
Intermodal Autonomous Mobility-on-Demand
Mauro Salazar1,2, Nicolas Lanzetti1,2, Federico Rossi2, Maximilian Schiffer2,3, and Marco Pavone2
Abstract—In this paper we study models and coordination poli-
cies for intermodal Autonomous Mobility-on-Demand (AMoD),
wherein a fleet of self-driving vehicles provides on-demand
mobility jointly with public transit. Specifically, we first present
a network flow model for intermodal AMoD, where we capture
the coupling between AMoD and public transit and the goal is
to maximize social welfare. Second, leveraging such a model,
we design a pricing and tolling scheme that allows the system
to recover a social optimum under the assumption of a perfect
market with selfish agents. Third, we present real-world case
studies for the transportation networks of New York City and
Berlin, which allow us to quantify the general benefits of
intermodal AMoD, as well as the societal impact of different
vehicles. In particular, we show that vehicle size and powertrain
type heavily affect intermodal routing decisions and, thus, system
efficiency. Our studies reveal that the cooperation between AMoD
fleets and public transit can yield significant benefits compared
to an AMoD system operating in isolation, whilst our proposed
tolling policies appear to be in line with recent discussions for
the case of New York City.
I. INTRODUCTION
TRAFFIC congestion is soaring all around the world. Besidesmere discomfort for passengers, congestion causes severe
economic and environmental harm, e.g., due to the loss of
working hours and pollutant emissions such as CO2, partic-
ulate matter, and NOx [1]. In 2013, traffic congestion cost
U.S. citizens 124 Billion USD [2]. Notably, transportation
remains one of a few sectors in which emissions are still
increasing [3]. Governments and municipalities are struggling
to find sustainable ways of transportation that can match
mobility needs and reduce environmental harm as well as
congestion.
To achieve sustainable modes of transportation, new mobil-
ity concepts and technology changes are necessary. However,
the potential to realize such concepts in urban environments is
limited, since upgrades to available infrastructures (e.g., roads
and subway lines) and their capacity are often extremely costly
and require decades-long planning timelines. Thus, mobility
concepts that use existing infrastructure in a more efficient way
are especially attractive. In this course, mobility-on-demand
services appear to be particularly promising. Herein, two main
concepts exist. On the one hand, free floating car sharing
systems strive to reduce the total number of private vehicles
in city centers. However, these systems offer limited flexibility
and are generally characterized by low adoption rates that
result from low vehicle availabilities due to the difficulty of
1Institute for Dynamic Systems and Control ETH Zürich, Zurich (ZH),
Switzerland {samauro,lnicolas}@ethz.ch
2Department of Aeronautics and Astro.
This document provides an overview of traffic flow modeling and simulation methods for intelligent transportation systems. It discusses both macroscopic and microscopic modeling approaches. Macroscopic models view traffic as a continuous flow and use partial differential equations involving density, speed, and flow rate over time and space. Microscopic models treat each vehicle individually using ordinary differential equations to model driver behavior and car-following dynamics. The document also reviews several traffic simulation software tools and concludes that modeling and simulation can help design and evaluate new transportation control strategies before implementation.
A New Paradigm in User Equilibrium-Application in Managed Lane PricingCSCJournals
Ineffective use of the High-Occupancy-Vehicle (HOV) lanes has the potential to decrease the overall roadway throughput during peak periods. Excess capacity in HOV lanes during peak periods can be made available to other types of vehicles, including single occupancy vehicles (SOV) for a price (toll). Such dual use lanes are known as “Managed Lanes.” The main purpose of this research is to propose a new paradigm in user equilibrium to predict the travel demand for determining the optimal fare policy for managed lane facilities. Depending on their value of time, motorists may choose to travel on Managed Lanes (ML) or General Purpose Lanes (GPL). In this study, the features in the software called Toll Pricing Modeler version 4.3 (TPM-4.3) are described. TPM-4.3 is developed based on this new user equilibrium concept and utilizes it to examine various operating scenarios. The software has two built-in operating objective options: 1) what would the ML operating speed be for a specified SOV toll, or 2) what should the SOV toll be for a desired minimum ML operating speed. A number of pricing policy scenarios are developed and examined on the proposed managed lane segment on Interstate 30 (I-30) in Grand Prairie, Texas. The software provides quantitative estimates of various factors including toll revenue, emissions and system performance such as person movement and traffic speed on managed and general purpose lanes. Overall, among the scenarios examined, higher toll rates tend to generate higher toll revenues, reduce overall CO and NOx emissions, and shift demand to general purpose lanes. On the other hand, HOV preferential treatments at any given toll level tend to reduce toll revenue, have no impact on or reduce system performance on managed lanes, and increase CO and NOx emissions.
This document presents a case study on designing an automated mobility-on-demand system to replace all personal transportation in Singapore. It first discusses shared vehicle systems and challenges like determining optimal fleet sizes. It then formulates the problems of minimum and performance-driven fleet sizing to meet demand. For minimum sizing, it shows fleet size must exceed the trip generation rate divided by the average trip speed. It also notes the impact of origin-destination imbalance, quantified by the Earth Mover's Distance between distributions. The case study applies these techniques using Singapore transportation data to estimate feasible fleet sizes.
A Review on Road Traffic Models for Intelligent Transportation System (ITS)IJSRD
Traffic flow models seek to describe the interaction of vehicles with their drivers and the infrastructure. Almost all the models directly or indirectly characterize the relationship among the traffic variables: the position, the speed, the flow, and the density of vehicles. These relationships can be based on either the behavior of individual vehicles in a traffic network in relation to the dynamics of other vehicles, the overall characteristics of the flow of vehicles in a traffic network, or a combination of the behavior of individual vehicles in a traffic network and the overall traffic flow characteristics. This paper describes the different models for automatic Traffic control system.
A Simulation-Based Dynamic Traffic Assignment Model With Combined ModesAllison Koehn
This document presents a simulation-based dynamic traffic assignment model for an urban transportation network with multiple transportation modes. The model uses a mesoscopic simulation approach with separate modules for vehicle movement simulation and time-dependent demand simulation. It considers four transportation modes (private car, bus, subway, bicycle) and allows travelers to choose between modes and routes based on travel time and costs. The model is tested using a case study area in Beijing to evaluate its performance under different scenarios like changes in demand levels, bus frequencies, parking fees, and information provision.
Amulti-Agent Architecture for a Co-Modal Transport SystemIJMER
Improving the co-modal transport and introducing systems for traveler information is becoming
more and more urgent in our society in order to guarantee a high level of mobility in the long term. The
goal of this research is to develop a distributed co-modal transport system that takes into account all
possible means of transport including carpooling, vehicles on service and public transport and satisfies
traveler’s queries, constraints and preferences. The main contribution of this work is to propose an
innovative multi-agent approach to solve problems in wide co-modal transport networks. First, we propose
a multi-agent architecture to model the system. Then we use a method to construct a co-modal transport
network representation by categorizing the transport services and using transfer links and a distributed
algorithm in order to resolve the shortest paths problem. We test our model and algorithms based on a
case study in Lille, France. The experiments results on theoretical graphs as well as on real transport
networks are very promising
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Mobile: 9791938249
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No :66,4th cross,Venkata nagar,
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Puducherry.
Email Id: praveen@nexgenproject.com
Mobile: 9791938249
Telephone: 0413-2211159
www.nexgenproject.com
This document provides an overview of traffic flow modeling and simulation methods for intelligent transportation systems. It discusses both macroscopic and microscopic modeling approaches. Macroscopic models view traffic as a continuous flow and use partial differential equations involving density, speed, and flow rate over time and space. Microscopic models treat each vehicle individually using ordinary differential equations to model driver behavior and car-following dynamics. The document also reviews several traffic simulation software tools and concludes that modeling and simulation can help design and evaluate new transportation control strategies before implementation.
A New Paradigm in User Equilibrium-Application in Managed Lane PricingCSCJournals
Ineffective use of the High-Occupancy-Vehicle (HOV) lanes has the potential to decrease the overall roadway throughput during peak periods. Excess capacity in HOV lanes during peak periods can be made available to other types of vehicles, including single occupancy vehicles (SOV) for a price (toll). Such dual use lanes are known as “Managed Lanes.” The main purpose of this research is to propose a new paradigm in user equilibrium to predict the travel demand for determining the optimal fare policy for managed lane facilities. Depending on their value of time, motorists may choose to travel on Managed Lanes (ML) or General Purpose Lanes (GPL). In this study, the features in the software called Toll Pricing Modeler version 4.3 (TPM-4.3) are described. TPM-4.3 is developed based on this new user equilibrium concept and utilizes it to examine various operating scenarios. The software has two built-in operating objective options: 1) what would the ML operating speed be for a specified SOV toll, or 2) what should the SOV toll be for a desired minimum ML operating speed. A number of pricing policy scenarios are developed and examined on the proposed managed lane segment on Interstate 30 (I-30) in Grand Prairie, Texas. The software provides quantitative estimates of various factors including toll revenue, emissions and system performance such as person movement and traffic speed on managed and general purpose lanes. Overall, among the scenarios examined, higher toll rates tend to generate higher toll revenues, reduce overall CO and NOx emissions, and shift demand to general purpose lanes. On the other hand, HOV preferential treatments at any given toll level tend to reduce toll revenue, have no impact on or reduce system performance on managed lanes, and increase CO and NOx emissions.
This document presents a case study on designing an automated mobility-on-demand system to replace all personal transportation in Singapore. It first discusses shared vehicle systems and challenges like determining optimal fleet sizes. It then formulates the problems of minimum and performance-driven fleet sizing to meet demand. For minimum sizing, it shows fleet size must exceed the trip generation rate divided by the average trip speed. It also notes the impact of origin-destination imbalance, quantified by the Earth Mover's Distance between distributions. The case study applies these techniques using Singapore transportation data to estimate feasible fleet sizes.
A Review on Road Traffic Models for Intelligent Transportation System (ITS)IJSRD
Traffic flow models seek to describe the interaction of vehicles with their drivers and the infrastructure. Almost all the models directly or indirectly characterize the relationship among the traffic variables: the position, the speed, the flow, and the density of vehicles. These relationships can be based on either the behavior of individual vehicles in a traffic network in relation to the dynamics of other vehicles, the overall characteristics of the flow of vehicles in a traffic network, or a combination of the behavior of individual vehicles in a traffic network and the overall traffic flow characteristics. This paper describes the different models for automatic Traffic control system.
A Simulation-Based Dynamic Traffic Assignment Model With Combined ModesAllison Koehn
This document presents a simulation-based dynamic traffic assignment model for an urban transportation network with multiple transportation modes. The model uses a mesoscopic simulation approach with separate modules for vehicle movement simulation and time-dependent demand simulation. It considers four transportation modes (private car, bus, subway, bicycle) and allows travelers to choose between modes and routes based on travel time and costs. The model is tested using a case study area in Beijing to evaluate its performance under different scenarios like changes in demand levels, bus frequencies, parking fees, and information provision.
Amulti-Agent Architecture for a Co-Modal Transport SystemIJMER
Improving the co-modal transport and introducing systems for traveler information is becoming
more and more urgent in our society in order to guarantee a high level of mobility in the long term. The
goal of this research is to develop a distributed co-modal transport system that takes into account all
possible means of transport including carpooling, vehicles on service and public transport and satisfies
traveler’s queries, constraints and preferences. The main contribution of this work is to propose an
innovative multi-agent approach to solve problems in wide co-modal transport networks. First, we propose
a multi-agent architecture to model the system. Then we use a method to construct a co-modal transport
network representation by categorizing the transport services and using transfer links and a distributed
algorithm in order to resolve the shortest paths problem. We test our model and algorithms based on a
case study in Lille, France. The experiments results on theoretical graphs as well as on real transport
networks are very promising
Big data analysis and scheduling optimization system oriented assembly proces...nexgentechnology
GET IEEE BIG DATA, JAVA ,DOTNET,ANDROID ,NS2,MATLAB,EMBEDED AT LOW COST WITH BEST QUALITY PLEASE CONTACT BELOW NUMBER
FOR MORE INFORMATION PLEASE FIND THE BELOW DETAILS:
Nexgen Technology
No :66,4th cross,Venkata nagar,
Near SBI ATM,
Puducherry.
Email Id: praveen@nexgenproject.com
Mobile: 9791938249
Telephone: 0413-2211159
www.nexgenproject.com
City lines designing hybrid hub and-spoke transit system with urban big datanexgentechnology
GET IEEE BIG DATA, JAVA ,DOTNET,ANDROID ,NS2,MATLAB,EMBEDED AT LOW COST WITH BEST QUALITY PLEASE CONTACT BELOW NUMBER
FOR MORE INFORMATION PLEASE FIND THE BELOW DETAILS:
Nexgen Technology
No :66,4th cross,Venkata nagar,
Near SBI ATM,
Puducherry.
Email Id: praveen@nexgenproject.com
Mobile: 9791938249
Telephone: 0413-2211159
www.nexgenproject.com
A Framework for Traffic Planning and Forecasting using Micro-Simulation Calib...ITIIIndustries
This paper presents the application of microsimulation for traffic planning and forecasting, and proposes a new framework to model complex traffic conditions by calibrating and adjusting traffic parameters of a microsimulation model. By using an open source micro-simulator package, TRANSIMS, in this study, animated and numerical results were produced and analysed. The framework of traffic model calibration was evaluated for its usefulness and practicality. Finally, we discuss future applications such as providing end users with real time traffic information through Intelligent Transport System (ITS) integration.
Traffic State Estimation and Prediction under Heterogeneous Traffic ConditionsIDES Editor
The recent economic growth in developing countries
like India has resulted in an intense increase of vehicle
ownership and use, as witnessed by severe traffic congestion
and bottlenecks during peak hours in most of the metropolitan
cities. Intelligent Transportation Systems (ITS) aim to reduce
traffic congestion by adopting various strategies such as
providing pre-trip and en-route traffic information thereby
reducing demand, adaptive signal control for area wide
optimization of traffic flow, etc. The successful deployment
and the reliability of these systems largely depend on the
accurate estimation of the current traffic state and quick and
reliable prediction to future time steps. At a macroscopic level,
this involves the prediction of fundamental traffic stream
parameters which include speed, density and flow in spacetime
domain. The complexity of prediction is enhanced by
heterogeneous traffic conditions as prevailing in India due to
less lane discipline and complex interactions among different
vehicle types. Also, there is no exclusive traffic flow model for
heterogeneous traffic conditions which can characterize the
traffic stream at a macroscopic level. Hence, the present study
tries to explore the applicability of an existing macroscopic
model, namely the Lighthill-Whitham-Richards (LWR) model,
for short term prediction of traffic flow in a busy arterial in
the city of Chennai, India, under heterogeneous traffic
conditions. Both linear and exponential speed-density
relations were considered and incorporated into the
macroscopic model. The resulting partial differential
equations are solved numerically and the results are found to
be encouraging. This model can ultimately be helpful for the
implementation of ATIS/ATMS applications under
heterogeneous traffic environment.
This document provides a literature review of charging management and infrastructure planning methodologies for electrified demand-responsive transport systems. It summarizes recent developments in mathematical modeling approaches for problems including dynamic EV-DRT optimization, fleet sizing, and charging infrastructure planning. The review identifies current research gaps and discusses future research directions. Key aspects of EV-DRT systems and charging operations are described, including characteristics of different charging infrastructures, energy consumption modeling, and considerations for charging policies, costs, and waiting times. Methodological approaches are categorized based on whether they address deterministic or stochastic problems, and public versus private charging infrastructure planning.
This document provides recommendations for estimating transport congestion and scarcity costs to implement efficient pricing based on social marginal cost. For road transport, it recommends using traffic simulation models to estimate the impact of additional vehicles on journey times and unreliability for other traffic. Where models are unavailable, it suggests using link-based speed-flow relationships. For rail transport, it states that the major issue is scarcity value of slots when capacity is reached, and that negotiation between infrastructure managers and operators is the most practical way to estimate these values. It also provides guidance on monetizing the effects of congestion and scarcity through values of travel time and vehicle operating costs.
Introduction to Traffic Flow theory modellingchatgptplus305
Understanding traffic flow is essential for urban planners, transportation engineers, and policymakers. It involves the study of how vehicles interact on roadways, the factors that influence their movement, and the impact on the overall transportation system. This section will explore the various aspects of traffic flow and its significance in urban environments.
Help the Genetic Algorithm to Minimize the Urban Traffic on IntersectionsIJORCS
This document summarizes a research paper that uses genetic algorithms to optimize traffic light timing at intersections to minimize traffic. It first describes modeling traffic light intersections using Petri nets. It then explains how genetic algorithms can be used for optimization by coding the problem variables in chromosomes, defining a fitness function to evaluate populations over generations, and using operators like mutation and crossover. The fitness function aims to minimize average traffic light cycle times based on 14 parameters related to light timing and vehicle wait times at two intersections. The genetic algorithm optimization of traffic light timing parameters is found to improve traffic flow at intersections.
A Computational Study Of Traffic Assignment AlgorithmsNicole Adams
The document summarizes a study comparing algorithms for solving traffic assignment problems. It classified algorithms as link-based (using link flows), path-based (using path flows), or origin-based (using link flows from origins). It reviewed literature on algorithms like Frank-Wolfe (link-based), path equilibration (path-based), and origin-based algorithm. It chose to implement representative algorithms from each class: Frank-Wolfe, conjugate Frank-Wolfe, bi-conjugate Frank-Wolfe (link-based), path equilibration, gradient projection, projected gradient, improved social pressure (path-based), and Algorithm B (origin-based) to compare their performance on benchmark problems.
A Computational Study Of Traffic Assignment AlgorithmsAlicia Buske
This document summarizes a research study that compares different algorithms for solving traffic assignment problems. The study performs a literature review of prominent traffic assignment algorithms, classifying them based on how the solution is represented (link-based, path-based, origin-based). It then implements representative algorithms from each class and conducts computational tests on benchmark networks of varying sizes. The results are analyzed to compare algorithm performance and identify the impact of different algorithm components on running time.
The document presents a review of optimal speed traffic flow models. It discusses that continuous construction of new roads is not a sustainable solution to traffic congestion. The optimal speed (OS) traffic flow model is proposed as an alternative, where vehicles travel at an optimal speed based on distance to the next vehicle. The OS model can help reduce congestion, accidents, and travel costs. Further research is recommended to develop more realistic car-following models that avoid collisions and consider human errors.
CREATING DATA OUTPUTS FROM MULTI AGENT TRAFFIC MICRO SIMULATION TO ASSIMILATI...csandit
interface for communication between agents.
class for communication management.
Agent Factory: class for agent creation.
Agent Directory: class for agent registration.
Agent Behavior: abstract class for agent behavior definition.
Concrete Agent: concrete agent implementation.
The core of the architecture is based on three main classes:
- Manager - represents the highest level of hierarchy, manages lower level agents.
- Agent - represents basic autonomous entity, encapsulates behavior and communication.
- Structure - represents geographical area, contains reference to lower level agents.
Agents are organized hierarchically according to geographical areas they represent. Manager is
the root of hierarchy, structures represent areas and agents are located
Adaptive traffic lights based on traffic flow prediction using machine learni...IJECEIAES
This document discusses using machine learning algorithms to predict traffic flow and reduce congestion at intersections. It compares linear regression, random forest regressor, decision tree regressor, gradient boosting regressor, and K-neighbor regressor models on a UK road traffic dataset. All models performed well according to evaluation metrics, indicating they are suitable for an adaptive traffic light system. The system was implemented using a random forest regressor model and simulations showed it reduced traffic congestion by 30.8%, justifying its effectiveness.
This document provides a review of fuzzy microscopic traffic models. It begins with an introduction describing the importance of traffic models and limitations of existing microscopic models. It then outlines the aim, objectives, and justification of integrating fuzzy logic into microscopic traffic models. Key aspects summarized include a review of existing microscopic car-following models and their limitations, an overview of fuzzy logic and how it can describe driver behavior more realistically, and directions for future research.
A Unified Framework For Traffic Assignment Deriving Static And Quasi-Dynamic...Nicole Heredia
This document summarizes a research paper that presents a unified theoretical framework to derive static and quasi-dynamic traffic assignment models from general first order dynamic traffic assignment models. The framework allows for consistency between static, quasi-dynamic, and dynamic models while using any fundamental diagram, turn restrictions, and route choice assumptions. The authors demonstrate how to derive static and quasi-dynamic models that explicitly account for queuing and spillback effects. This addresses inconsistencies between static planning models and dynamic operations models used in practice.
DYNAMIC RESOURCE ALLOCATION IN ROAD TRANSPORT SECTOR USING MOBILE CLOUD COMPU...IAEME Publication
Literature review revealed application of various techniques for efficient use of existing resources in road transport sector vehicles, operators and related facilities. This issue assumes bigger dimensions in situations where there are multiple routes and the demand in the routes is highly fluctuating over the day. The application of the existing techniques as reported in literature addresses above issues to a considerable extent. However the main draw back in existing techniques is lack of
proper uninterrupted information about vehicles and demand available at a central place for allocation of vehicles in different roads and huge computational times required for processing. Cloud computing is a recently developed processing tool that is used in effective utilization of resources in transport sector under dynamic resource allocation.
A User-Operator Assignment Game With Heterogeneous User Groups For Empirical ...Emma Burke
This document describes a user-operator assignment game model to evaluate the impact of different operation policies on a microtransit service in Luxembourg called Kussbus. The model extends previous stable matching models to consider heterogeneous user preferences. It formulates the problem as an assignment game with two subproblems: 1) an optimal user-route matching problem and 2) a stable outcome problem to determine cost allocations. The model is applied empirically to real data from Kussbus to analyze the effects of changes in route costs, travel times, and access distances on ridership and profits. Sensitivity analysis found that reducing route costs by 50% could increase ridership by 10% and reducing travel times by 20% could significantly increase profits.
A User-Operator Assignment Game With Heterogeneous User Groups For Empirical ...Karen Gomez
This document describes a user-operator assignment game model to evaluate the impact of different operation policies on a microtransit service in Luxembourg called Kussbus. The model extends previous stable matching models to consider heterogeneous user preferences. It formulates the problem as an assignment game with two subproblems: 1) an optimal user-route matching problem and 2) a stable outcome problem to determine cost allocations. The model is applied empirically to real data from Kussbus to analyze the effects of changes in route costs, travel times, and access distances on ridership and profits. Sensitivity analysis found that reducing route costs by 50% could increase ridership by 10% and reducing travel times by 20% could significantly increase profits.
This document describes a multi-agent model used to develop and assess urban forms in terms of sustainability, focusing on transportation, land use distribution, and vehicle emission pollution minimization. Two city forms are examined - a compact city and a multi-nuclear city. The model generates land use maps for each city form based on transportation networks and user preferences. An activity-based transportation model then simulates travel patterns and evaluates total travel, trips, and accessibility to determine pollution emissions. Planners can provide input to adjust the computer-generated maps. The goal is to understand the planner's options for developing sustainable cities and determine the optimal city form.
CREATING DATA OUTPUTS FROM MULTI AGENT TRAFFIC MICRO SIMULATION TO ASSIMILATI...cscpconf
The intensive development of traffic engineering and technologies that are integrated into vehicles, roads and their surroundings, bring opportunities of real time transport mobility modeling. Based on such model it is then possible to establish a predictive layer that is capable of predicting short and long term traffic flow behavior. It is possible to create the real time model of traffic mobility based on generated data. However, data may have different geographical, temporal or other constraints, or failures. It is therefore appropriate to develop tools that artificially create missing data, which can then be assimilated with real data. This paper presents a mechanism describing strategies of generating artificial data using microsimulations. It describes traffic microsimulation based on our solution of multiagent framework over which a system for generating traffic data is built. The system generates data of a structure corresponding to the data acquired in the real world.
2/21/2020 Soil Colloids (Chapter 8) Notes - AGRI1050R50: Introduction to Soil Science (2020S)
https://gotoclass.tnecampus.org/d2l/le/content/8094442/viewContent/60403389/View 1/12
Soil Colloids (Chapter 8) Notes
Soil Colloids (Chapter 8) Notes
Did you know ....
Did you know soil fertility or the ability for a soil to provide nutrients is seated in the type of minerals it
contains? Chapter 8 will cover the various types of soil colloids including all the layer and non-layer
silicates, cation exchange, anion exchange, and sorption.
Lecture content notes are accompanied by videos listed below the notes in each submodule (e.g. Soil
Colloids (Chapter 8) Videos A though H). Print or download lecture notes then view videos in
succession alongside lecture content and add additional notes from each video. The start of each
video is noted in parenthesis (e.g. Content for Video A) within each lecture note set and contains
lecture content through the note for the next video (e.g. Content for Video B).
Figures and tables unless specifically referrenced are from the course text, Nature and Property of
Soils, 14th Edition, Brady and Weil.
Content Video A
Soil Colloids
Smallest soil particles < 1 µm
Surface area - LARGE
Surface charge - CEC
Adsorb water
AGRI1050R50: Introduction to Soil Science (2020S) LH
https://gotoclass.tnecampus.org/d2l/le/content/8094442/navigateContent/176/Previous?pId=60403304
https://gotoclass.tnecampus.org/d2l/le/content/8094442/navigateContent/176/Next?pId=60403304
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https://gotoclass.tnecampus.org/d2l/home/8094442
2/21/2020 Soil Colloids (Chapter 8) Notes - AGRI1050R50: Introduction to Soil Science (2020S)
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Types of Colloids
Crystalline Silicate clays: ordered, crystalline, layers
Non-crystalline silicate clays: non-ordered, layers, volcanic
Iron/Aluminum Oxides – weathered soils, less CEC
Humus – OM, not mineral or crystalline, high CEC
Soil Colloids
Content Video B
Layer Silicates - Construction
Phyllosillicates
Tetrahedral Sheets
1 Si with 4 Oxygen
Share basal oxygen
Form sheets
Octahedral Sheets
6 Oxygen with Al3+ or Mg 2+
Di T i O t h d l b d # f di ti i
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2/21/2020 Soil Colloids (Chapter 8) Notes - AGRI1050R50: Introduction to Soil Science (2020S)
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Di or Tri Octahedral based on # of coordinating ions
http://web.utk.edu/~drtd0c/Soil%20Colloids.pdf
http://web.utk.edu/~drtd0c/Soil%20Colloids.pdf
2/21/2020 Soil Colloids (Chapter 8) Notes - AGRI1050R50: Introduction to Soil Science (2020S)
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Size .
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This paper presents the application of microsimulation for traffic planning and forecasting, and proposes a new framework to model complex traffic conditions by calibrating and adjusting traffic parameters of a microsimulation model. By using an open source micro-simulator package, TRANSIMS, in this study, animated and numerical results were produced and analysed. The framework of traffic model calibration was evaluated for its usefulness and practicality. Finally, we discuss future applications such as providing end users with real time traffic information through Intelligent Transport System (ITS) integration.
Traffic State Estimation and Prediction under Heterogeneous Traffic ConditionsIDES Editor
The recent economic growth in developing countries
like India has resulted in an intense increase of vehicle
ownership and use, as witnessed by severe traffic congestion
and bottlenecks during peak hours in most of the metropolitan
cities. Intelligent Transportation Systems (ITS) aim to reduce
traffic congestion by adopting various strategies such as
providing pre-trip and en-route traffic information thereby
reducing demand, adaptive signal control for area wide
optimization of traffic flow, etc. The successful deployment
and the reliability of these systems largely depend on the
accurate estimation of the current traffic state and quick and
reliable prediction to future time steps. At a macroscopic level,
this involves the prediction of fundamental traffic stream
parameters which include speed, density and flow in spacetime
domain. The complexity of prediction is enhanced by
heterogeneous traffic conditions as prevailing in India due to
less lane discipline and complex interactions among different
vehicle types. Also, there is no exclusive traffic flow model for
heterogeneous traffic conditions which can characterize the
traffic stream at a macroscopic level. Hence, the present study
tries to explore the applicability of an existing macroscopic
model, namely the Lighthill-Whitham-Richards (LWR) model,
for short term prediction of traffic flow in a busy arterial in
the city of Chennai, India, under heterogeneous traffic
conditions. Both linear and exponential speed-density
relations were considered and incorporated into the
macroscopic model. The resulting partial differential
equations are solved numerically and the results are found to
be encouraging. This model can ultimately be helpful for the
implementation of ATIS/ATMS applications under
heterogeneous traffic environment.
This document provides a literature review of charging management and infrastructure planning methodologies for electrified demand-responsive transport systems. It summarizes recent developments in mathematical modeling approaches for problems including dynamic EV-DRT optimization, fleet sizing, and charging infrastructure planning. The review identifies current research gaps and discusses future research directions. Key aspects of EV-DRT systems and charging operations are described, including characteristics of different charging infrastructures, energy consumption modeling, and considerations for charging policies, costs, and waiting times. Methodological approaches are categorized based on whether they address deterministic or stochastic problems, and public versus private charging infrastructure planning.
This document provides recommendations for estimating transport congestion and scarcity costs to implement efficient pricing based on social marginal cost. For road transport, it recommends using traffic simulation models to estimate the impact of additional vehicles on journey times and unreliability for other traffic. Where models are unavailable, it suggests using link-based speed-flow relationships. For rail transport, it states that the major issue is scarcity value of slots when capacity is reached, and that negotiation between infrastructure managers and operators is the most practical way to estimate these values. It also provides guidance on monetizing the effects of congestion and scarcity through values of travel time and vehicle operating costs.
Introduction to Traffic Flow theory modellingchatgptplus305
Understanding traffic flow is essential for urban planners, transportation engineers, and policymakers. It involves the study of how vehicles interact on roadways, the factors that influence their movement, and the impact on the overall transportation system. This section will explore the various aspects of traffic flow and its significance in urban environments.
Help the Genetic Algorithm to Minimize the Urban Traffic on IntersectionsIJORCS
This document summarizes a research paper that uses genetic algorithms to optimize traffic light timing at intersections to minimize traffic. It first describes modeling traffic light intersections using Petri nets. It then explains how genetic algorithms can be used for optimization by coding the problem variables in chromosomes, defining a fitness function to evaluate populations over generations, and using operators like mutation and crossover. The fitness function aims to minimize average traffic light cycle times based on 14 parameters related to light timing and vehicle wait times at two intersections. The genetic algorithm optimization of traffic light timing parameters is found to improve traffic flow at intersections.
A Computational Study Of Traffic Assignment AlgorithmsNicole Adams
The document summarizes a study comparing algorithms for solving traffic assignment problems. It classified algorithms as link-based (using link flows), path-based (using path flows), or origin-based (using link flows from origins). It reviewed literature on algorithms like Frank-Wolfe (link-based), path equilibration (path-based), and origin-based algorithm. It chose to implement representative algorithms from each class: Frank-Wolfe, conjugate Frank-Wolfe, bi-conjugate Frank-Wolfe (link-based), path equilibration, gradient projection, projected gradient, improved social pressure (path-based), and Algorithm B (origin-based) to compare their performance on benchmark problems.
A Computational Study Of Traffic Assignment AlgorithmsAlicia Buske
This document summarizes a research study that compares different algorithms for solving traffic assignment problems. The study performs a literature review of prominent traffic assignment algorithms, classifying them based on how the solution is represented (link-based, path-based, origin-based). It then implements representative algorithms from each class and conducts computational tests on benchmark networks of varying sizes. The results are analyzed to compare algorithm performance and identify the impact of different algorithm components on running time.
The document presents a review of optimal speed traffic flow models. It discusses that continuous construction of new roads is not a sustainable solution to traffic congestion. The optimal speed (OS) traffic flow model is proposed as an alternative, where vehicles travel at an optimal speed based on distance to the next vehicle. The OS model can help reduce congestion, accidents, and travel costs. Further research is recommended to develop more realistic car-following models that avoid collisions and consider human errors.
CREATING DATA OUTPUTS FROM MULTI AGENT TRAFFIC MICRO SIMULATION TO ASSIMILATI...csandit
interface for communication between agents.
class for communication management.
Agent Factory: class for agent creation.
Agent Directory: class for agent registration.
Agent Behavior: abstract class for agent behavior definition.
Concrete Agent: concrete agent implementation.
The core of the architecture is based on three main classes:
- Manager - represents the highest level of hierarchy, manages lower level agents.
- Agent - represents basic autonomous entity, encapsulates behavior and communication.
- Structure - represents geographical area, contains reference to lower level agents.
Agents are organized hierarchically according to geographical areas they represent. Manager is
the root of hierarchy, structures represent areas and agents are located
Adaptive traffic lights based on traffic flow prediction using machine learni...IJECEIAES
This document discusses using machine learning algorithms to predict traffic flow and reduce congestion at intersections. It compares linear regression, random forest regressor, decision tree regressor, gradient boosting regressor, and K-neighbor regressor models on a UK road traffic dataset. All models performed well according to evaluation metrics, indicating they are suitable for an adaptive traffic light system. The system was implemented using a random forest regressor model and simulations showed it reduced traffic congestion by 30.8%, justifying its effectiveness.
This document provides a review of fuzzy microscopic traffic models. It begins with an introduction describing the importance of traffic models and limitations of existing microscopic models. It then outlines the aim, objectives, and justification of integrating fuzzy logic into microscopic traffic models. Key aspects summarized include a review of existing microscopic car-following models and their limitations, an overview of fuzzy logic and how it can describe driver behavior more realistically, and directions for future research.
A Unified Framework For Traffic Assignment Deriving Static And Quasi-Dynamic...Nicole Heredia
This document summarizes a research paper that presents a unified theoretical framework to derive static and quasi-dynamic traffic assignment models from general first order dynamic traffic assignment models. The framework allows for consistency between static, quasi-dynamic, and dynamic models while using any fundamental diagram, turn restrictions, and route choice assumptions. The authors demonstrate how to derive static and quasi-dynamic models that explicitly account for queuing and spillback effects. This addresses inconsistencies between static planning models and dynamic operations models used in practice.
DYNAMIC RESOURCE ALLOCATION IN ROAD TRANSPORT SECTOR USING MOBILE CLOUD COMPU...IAEME Publication
Literature review revealed application of various techniques for efficient use of existing resources in road transport sector vehicles, operators and related facilities. This issue assumes bigger dimensions in situations where there are multiple routes and the demand in the routes is highly fluctuating over the day. The application of the existing techniques as reported in literature addresses above issues to a considerable extent. However the main draw back in existing techniques is lack of
proper uninterrupted information about vehicles and demand available at a central place for allocation of vehicles in different roads and huge computational times required for processing. Cloud computing is a recently developed processing tool that is used in effective utilization of resources in transport sector under dynamic resource allocation.
A User-Operator Assignment Game With Heterogeneous User Groups For Empirical ...Emma Burke
This document describes a user-operator assignment game model to evaluate the impact of different operation policies on a microtransit service in Luxembourg called Kussbus. The model extends previous stable matching models to consider heterogeneous user preferences. It formulates the problem as an assignment game with two subproblems: 1) an optimal user-route matching problem and 2) a stable outcome problem to determine cost allocations. The model is applied empirically to real data from Kussbus to analyze the effects of changes in route costs, travel times, and access distances on ridership and profits. Sensitivity analysis found that reducing route costs by 50% could increase ridership by 10% and reducing travel times by 20% could significantly increase profits.
A User-Operator Assignment Game With Heterogeneous User Groups For Empirical ...Karen Gomez
This document describes a user-operator assignment game model to evaluate the impact of different operation policies on a microtransit service in Luxembourg called Kussbus. The model extends previous stable matching models to consider heterogeneous user preferences. It formulates the problem as an assignment game with two subproblems: 1) an optimal user-route matching problem and 2) a stable outcome problem to determine cost allocations. The model is applied empirically to real data from Kussbus to analyze the effects of changes in route costs, travel times, and access distances on ridership and profits. Sensitivity analysis found that reducing route costs by 50% could increase ridership by 10% and reducing travel times by 20% could significantly increase profits.
This document describes a multi-agent model used to develop and assess urban forms in terms of sustainability, focusing on transportation, land use distribution, and vehicle emission pollution minimization. Two city forms are examined - a compact city and a multi-nuclear city. The model generates land use maps for each city form based on transportation networks and user preferences. An activity-based transportation model then simulates travel patterns and evaluates total travel, trips, and accessibility to determine pollution emissions. Planners can provide input to adjust the computer-generated maps. The goal is to understand the planner's options for developing sustainable cities and determine the optimal city form.
CREATING DATA OUTPUTS FROM MULTI AGENT TRAFFIC MICRO SIMULATION TO ASSIMILATI...cscpconf
The intensive development of traffic engineering and technologies that are integrated into vehicles, roads and their surroundings, bring opportunities of real time transport mobility modeling. Based on such model it is then possible to establish a predictive layer that is capable of predicting short and long term traffic flow behavior. It is possible to create the real time model of traffic mobility based on generated data. However, data may have different geographical, temporal or other constraints, or failures. It is therefore appropriate to develop tools that artificially create missing data, which can then be assimilated with real data. This paper presents a mechanism describing strategies of generating artificial data using microsimulations. It describes traffic microsimulation based on our solution of multiagent framework over which a system for generating traffic data is built. The system generates data of a structure corresponding to the data acquired in the real world.
Similar to 1Intermodal Autonomous Mobility-on-DemandMauro Salazar1,.docx (20)
2/21/2020 Soil Colloids (Chapter 8) Notes - AGRI1050R50: Introduction to Soil Science (2020S)
https://gotoclass.tnecampus.org/d2l/le/content/8094442/viewContent/60403389/View 1/12
Soil Colloids (Chapter 8) Notes
Soil Colloids (Chapter 8) Notes
Did you know ....
Did you know soil fertility or the ability for a soil to provide nutrients is seated in the type of minerals it
contains? Chapter 8 will cover the various types of soil colloids including all the layer and non-layer
silicates, cation exchange, anion exchange, and sorption.
Lecture content notes are accompanied by videos listed below the notes in each submodule (e.g. Soil
Colloids (Chapter 8) Videos A though H). Print or download lecture notes then view videos in
succession alongside lecture content and add additional notes from each video. The start of each
video is noted in parenthesis (e.g. Content for Video A) within each lecture note set and contains
lecture content through the note for the next video (e.g. Content for Video B).
Figures and tables unless specifically referrenced are from the course text, Nature and Property of
Soils, 14th Edition, Brady and Weil.
Content Video A
Soil Colloids
Smallest soil particles < 1 µm
Surface area - LARGE
Surface charge - CEC
Adsorb water
AGRI1050R50: Introduction to Soil Science (2020S) LH
https://gotoclass.tnecampus.org/d2l/le/content/8094442/navigateContent/176/Previous?pId=60403304
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2/21/2020 Soil Colloids (Chapter 8) Notes - AGRI1050R50: Introduction to Soil Science (2020S)
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Types of Colloids
Crystalline Silicate clays: ordered, crystalline, layers
Non-crystalline silicate clays: non-ordered, layers, volcanic
Iron/Aluminum Oxides – weathered soils, less CEC
Humus – OM, not mineral or crystalline, high CEC
Soil Colloids
Content Video B
Layer Silicates - Construction
Phyllosillicates
Tetrahedral Sheets
1 Si with 4 Oxygen
Share basal oxygen
Form sheets
Octahedral Sheets
6 Oxygen with Al3+ or Mg 2+
Di T i O t h d l b d # f di ti i
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2/21/2020 Soil Colloids (Chapter 8) Notes - AGRI1050R50: Introduction to Soil Science (2020S)
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Di or Tri Octahedral based on # of coordinating ions
http://web.utk.edu/~drtd0c/Soil%20Colloids.pdf
http://web.utk.edu/~drtd0c/Soil%20Colloids.pdf
2/21/2020 Soil Colloids (Chapter 8) Notes - AGRI1050R50: Introduction to Soil Science (2020S)
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Size .
20 Other Conditions That May Be a Focus of Clinical AttentionV-c.docxRAJU852744
20 Other Conditions That May Be a Focus of Clinical Attention
V-codes and z-codes
V-codes and Z-codes are conditions that may be the focus of clinical attention but are not considered mental disorders. They correspond to International Classification of Diseases, Ninth Revision, Clinical Modification ICD-9-CM (V-codes) and International Classification of Diseases, Tenth Revision, Clinical Modification ICD-10-CM (Z-codes that become effective in 2015. In most instances, third-party payers do not cover charges for delivering services to an individual if the diagnosis is solely a V- or Z-code alone. If the V- or Z-code is not the primary diagnosis then it should be documented following the primary diagnosis. In addition, when writing the psychosocial assessment any psychosocial and cultural factors that might impact the client's diagnosis should be documented. The psychosocial stressors reflected in these diagnoses are widespread across all classes and cultures and have been shown to impact all aspects of an individual's life from the physical and psychological to the financial. Furthermore, these conditions have been shown to significantly impact the diagnosis and outcome for a multitude of mental and medical disorders. V- and Z-codes are grouped into numerous categories including: relational problems, problems related to abuse/neglect, educational and occupational problems, housing and economic problems, problems related to the social environment, problems related to the legal system, other counseling services, other psychosocial, personal and environmental problems, and problems of personal history (APA, 2013).
Broadly speaking, the category “Relational Problems” describes interactional problems between family members (e.g., parent/caregiver-child) or partners that result in significant impairment of family functioning or development of symptoms in the distressed individual, spouses, siblings, or other family members. Relational problems are broken down into two categories, Problems Related to Family Upbringing and Other Problems Related to Primary Support Group. For example, in the first category a Parent-Child Relational Problem involves interactional problems between one or both parents and a child that lead to dysfunction in behavioral (e.g., inadequate protection, overprotection), cognitive (e.g., antagonism toward or blaming of the other) or affective (e.g., feeling sad and angry) realms. Here, the critical factor is the quality of the parent-child relationship or when the dysfunction in this relationship is impacting the course and outcome of a psychological or medical condition. Other examples include Sibling Relational Problem, Upbringing Away from Parents, and Child Affected by Parental Relationship Distress. Similarly, family relationships and interactional patterns leading to problems related to primary support group include Partner Relational Problem, Disruption of Family by Separation/Divorce, High Expressed Emotion Level with.
223 Case 53 Problems in Pasta Land by Andres Sous.docxRAJU852744
1) The pasta factory is facing increasing customer demand that exceeds its production capacity due to outdated equipment.
2) New technology allows for higher production capacity using lower quality ingredients, but requires different skills and labor than the current factory's outdated equipment.
3) Introducing new technology and expanding production would require overcoming resistance from employees accustomed to current methods and addressing concerns about job losses in the local community.
2
2
2
1
1
1
Organization Name: Insta-Buy
Insta-Buy is an E-Commerce Multinational American company. It was founded in 2010 and is based in Atlanta, Georgia. It mainly operates with grocery delivery and pick up and it offers services through web application and mobile application to various states in United States. It is one of the major online marketplaces for grocery delivery. The company is valued at $1 billion worth and has partnership with over 150 retailers. It is known for its fresh produce and timely delivery and pickup.
Predictive Analysis at Insta-Buy:
The predictive analytics is termed as what is likely to happen in the future. The predictive analytics is based on statistical and data mining technique. The aim of this technique is to predict the future of the project such as what would be the customer reaction on project, financial need, etc. In developing predictive analytical application, a number of techniques are used such as classification algorithms. The classification techniques are logistic regression, decision tree models and neural network. Clustering algorithms are used to segment customers in different groups which helps to target specific promotions to them. To estimate the relationship between different purchasing behavior, association mining technique is used (Mehra, 2014). As an example, for any product on Amazon.com results in the retailer also suggesting similar products that a customer might be interested in. Predictive analytics can be used in E-commerce to solve the following problems
1. Improve customer engagement and increase revenue
1. Launch promotions that target specific customer group
1. Optimizing prices to generate maximum profits
1. Keep proper inventory and reduce over stalking
1. Minimizing fraud happenings and protecting privacy
1. Provide batter customer service at low cost
1. Analyze data and make decision in real time
TOPICS:
Student: Ahmed
Topic: Bayesian Networks (Predicting Sales In E-commerce Using Bayesian Network Model)
Student: Meet
Topic: Predictive Analysis
Student: Peter
Topic: Privacy and Confidentiality in an e-Commerce World: Data Mining, Data Warehousing, Matching and Disclosure Limitation
Student: Nayeem
Topic: Ensemble Modeling
Student: Shek
Topic: L.Jack & Y.D. Tsai, Using Text Mining of Amazon Reviews to Explore User-Defined Product Highlights and Issues.
Student: Suma
Topic: Deep Neural Networks
REFERENCES:
Olufunke Rebecca Vincent, A. S. (2017). A Cognitive Buying Decision-Making Process in B2B E-Commerce Using Analytic-MLP. Elsevier.
https://www.researchgate.net/publication/319278239_A_Cognitive_Buying_Decision-Making_Process_in_B2B_E-Commerce_Using_Analytic-MLP
Wan, C. C. (2017). Forcasting E-commerce Key Performance Indicators
https://beta.vu.nl/nl/Images/stageverslag-wan_tcm235-867619.pdf
Fienberg, S. (2006). Privacy and Confidentiality in an e-Commerce World: Data Mining, Data Warehousing, Matching and Disclosure Limitation. Statistical Science, .
22-6 Reporting the Plight of Depression FamiliesMARTHA GELLHOR.docxRAJU852744
22-6 | Reporting the Plight of Depression Families
MARTHA GELLHORN, Field Report to Harry Hopkins (1934)
1. From Martha Gellhorn to Harry Hopkins, Report, Gaston County, North Carolina, November 11, 1934, Franklin D. Roosevelt Library, Harry Hopkins Papers, Box 66. Online transcript available at http://newdeal.feri.org/hopkins/hop08.htm.
Journalist and novelist Martha Gellhorn’s heartrending field report describing impoverished Gastonia, North Carolina, families vividly captures the desperate hope of depression-era families. Hired by Harry Hopkins, Franklin Roosevelt’s point man for federal relief efforts, Gellhorn detailed the enormous challenge facing the administration. Compounding the epic humanitarian crisis she encountered was the political opposition, which she singled out as one among many obstacles hampering relief efforts.
All during this trip [to North Carolina] I have been thinking to myself about that curious phrase “red menace,” and wondering where said menace hid itself. Every house I visited — mill worker or unemployed — had a picture of the President. These ranged from newspaper clippings (in destitute homes) to large colored prints, framed in gilt cardboard. The portrait holds the place of honour over the mantel. . . . He is at once God and their intimate friend; he knows them all by name, knows their little town and mill, their little lives and problems. And, though everything else fails, he is there, and will not let them down.
I have been seeing people who, according to almost any standard, have practically nothing in life and practically nothing to look forward to or hope for. But there is hope; confidence, something intangible and real: “the president isn’t going to forget us.”
Let me cite cases: I went to see a woman with five children who was living on relief ($3.40 a week). Her picture of the President was a small one, and she told me her oldest daughter had been married some months ago and had cried for the big, coloured picture as a wedding present. The children have no shoes and that woman is terrified of the coming cold as if it were a definite physical entity. There is practically no furniture left in the home, and you can imagine what and how they eat. But she said, suddenly brightening, “I’d give my heart to see the President. I know he means to do everything he can for us; but they make it hard for him; they won’t let him.” I note this case as something special; because here the faith was coupled with a feeling (entirely sympathetic) that the President was not entirely omnipotent.
I have been seeing mill workers; and in every mill when possible, the local Union president. There has been widespread discrimination in the south; and many mills haven’t re-opened since the strike. Those open often run on such curtailment that workers are getting from 2 to 3 days work a week. The price of food has risen (especially the kind of food they eat: fat-back bacon, flour, meal, sorghum) as high as 100%. It is getting cold;.
2018 4th International Conference on Green Technology and Sust.docxRAJU852744
2018 4th International Conference on Green Technology and Sustainable Development (GTSD)
130
�
Abstract - The Vietnamese government have plan to develop the
wind farms with the expected capacity of 6 GW by 2030. With the
high penetration of wind power into power system, wind power
forecasting is essentially needed for a power generation
balancing in power system operation and electricity market.
However, such a tool is currently not available in Vietnamese
wind farms as well as electricity market. Therefore, a short-term
wind power forecasting tool for 24 hours has been created to fill
in this gap, using artificial neural network technique. The neural
network has been trained with past data recorded from 2015 to
2017 at Tuy Phong wind farm in Binh Thuan province of Viet
Nam. It has been tested for wind power prediction with the input
data from hourly weather forecast for the same wind farm. The
tool can be used for short-term wind power forecasting in
Vietnamese power system in a foreseeable future.
Keywords: power system; wind farm; wind power forecasting;
neural network; electricity market.
I. NECESITY OF WIND POWER FORECASTING
Today, the integration of wind power into the existing
grid is a big issue in power system operation. For the system
operators, power generation curve of wind turbines is a
necessary information in the power sources balancing. From
the dispatchers’ point of view, wind power forecast errors
will impact the system net imbalances when the share of
wind power increases, and more accurate forecasts mean less
regulating capacity will be activated from the real time
electricity market [1]. In the deregulated market, day-ahead
electricity spot prices are also affected by day-ahead wind
power forecasting [2]. Wind power forecasting is also
essential in reducing the power curtailment, supporting the
ancillary service. However, due to uncertainty of wind speed
and weather factors, the wind power is not easy to predict.
In recent years, many wind power forecasting methods
have been proposed. In [3], a review of different approaches
for short-term wind power forecasting has been introduced,
including statistical and physical methods with different
models such as WPMS, WPPT, Prediktor, Zephyr, WPFS,
ANEMOS, ARMINES, Ewind, Sipreolico. In [4], [5], the
methods, models of wind power forecasting and its impact on
*Research supported by Gesellschaft fuer Internationale
Zusammenarbeit GmbH (GIZ).
D. T. Viet is with the University of Danang, Vietnam (email:
[email protected]).
V. V. Phuong is with the University of Danang, Vietnam (email:
[email protected]).
D. M. Quan is with the University of Danang, Vietnam (email:
[email protected]).
A. Kies is with the Frankfurt Institute for Advanced Studies, Germany
(email: [email protected] uni-frankfurt.de).
B. U. Schyska is with the Carl von Ossietzky Universität Oldenburg,
Germany (email: [email protected]).
Y. K. Wu i.
202 S.W.3d 811Court of Appeals of Texas,San Antonio.PROG.docxRAJU852744
202 S.W.3d 811
Court of Appeals of Texas,
San Antonio.
PROGRESSIVE COUNTY MUTUAL INSURANCE
COMPANY, Appellant,
v.
Hector Raul TREVINO and Mario Moyeda,
Appellees.
No. 04–05–00113–CV.
|
June 28, 2006.
|
Rehearing Overruled July 31, 2006.
.
200 wordsResearch Interest Lack of minorities in top level ma.docxRAJU852744
200 words
Research Interest: Lack of minorities in top level management positions
Describe why and how a qualitative approach may be appropriate for your area of interest for your research. Include a rationale for each proposed use of qualitative inquiry.
.
2019 14th Iberian Conference on Information Systems and Tech.docxRAJU852744
2019 14th Iberian Conference on Information Systems and Technologies (CISTI)
19 – 22 June 2019, Coimbra, Portugal
ISBN: 978-989-98434-9-3
How ISO 27001 can help achieve GDPR compliance
Isabel Maria Lopes
Polytechnic Institute of Bragança, Bragança, Portugal
UNIAG, Polytechnic Institute of Bragança, Portugal
ALGORITMI Centre, Minho University, Guimarães,
Portugal
[email protected]
Pedro Oliveira
Polytechnic Institute of Bragança, Bragança, Portugal
[email protected]
Teresa Guarda
Universidad Estatal Península de Santa Elena – UPSE, La
Libertad, Ecuador
Universidad de las Fuerzas Armadas – ESPE, Sangolqui,
Quito, Equador
ALGORITMI Centre, Minho University, Guimarães,
Portugal
[email protected]
Abstract — Personal Data Protection has been among the most
discussed topics lately and a reason for great concern among
organizations. The EU General Data Protection Regulation
(GDPR) is the most important change in data privacy regulation
in 20 years. The regulation will fundamentally reshape the way in
which data is handled across every sector. The organizations had
two years to implement it. As referred by many authors, the
implementation of the regulation has not been an easy task for
companies. The question we aim to answer in this study is how far
the implementation of ISO 27001 standards might represent a
facilitating factor to organizations for an easier compliance with
the regulation. In order to answer this question, several websites
(mostly of consulting companies) were analyzed, and the aspects
considered as facilitating are listed in this paper.
Keywords - regulation (EU) 2016/679; general data protection
regulation; ISO/IEC 27001.
I. INTRODUCTION
In recent years, data protection has become a forefront issue
in cyber security. The issues introduced by recurring
organizational data breaches, social media and the Internet of
Things (IoT) have raised the stakes even further [1, 2]. The EU
GDPR, enforced from May 25 2018, is an attempt to address
such data protection. The GDPR makes for stronger, unified data
protection throughout the EU.
The EU GDPR states that organizations must adopt
appropriate policies, procedures and processes to protect the
personal data they hold.
The International Organization for Standardization (ISO)
/International Electrotechnical Commission (IEC) 27000 series
is a set of information security standards that provide best-
practice recommendations for information security management
[3].
This international standard for information security, ISO
27001, provides an excellent starting point for achieving the
technical and operational requirements necessary to reduce the
risk of a breach.
Not all data is protected by the GDPR, since it is only
applicable to personal data. This is defined in Article 4 as
follows [4]:
“personal data” means any information relating to an
identified or identifiable natural person (’data subject’); an
identifiable.
200520201ORG30002 – Leadership Practice and Skills.docxRAJU852744
This document provides information on cross-cultural leadership, including readings and topics for the week. It discusses cross-cultural leadership, the GLOBE study on cultural dimensions, universally desirable and undesirable leadership attributes across cultures, and developing cultural intelligence. It also covers implications of cross-cultural leadership for organizations, traditional vs inclusive models of leadership, and developing global leadership competencies.
2/18/2020 Sample Content Topic
https://purdueglobal.brightspace.com/d2l/le/content/115691/viewContent/9226875/View 1/1
Trouble at 3Forks
Introduction: The foreclosure process can differ for deeds
versus mortgages. You will conduct research to determine
these differences since it is not only covered in the real estate
exam, but it is important to know this process in professional
practice.
Scenario: Henri and Lila own a restaurant which the
government has caused to close due to widening the road in
front of their establishment. Since this is the main source of
their income, and has caused Lila and Henri to stop payments
on their mortgage, address the following questions.
Checklist:
Explain the action that Henri and Lila should expect from the
bank regarding their property.
Describe how the banks actions would differ if it was a deed of
trust rather than a mortgage.
Respond in a minimum of 600–850-word essay with additional
title and reference pages using APA format and citation style.
Access the Unit 4 Assignment grading rubric.
Submit your response to the Unit 4 Assignment Dropbox.
Assignment Details
https://kapextmediassl-a.akamaihd.net/business/MT431/1904c/rubrics/u4_rubric.pdf
Mitchell, Taylor N.
Donaldson, Jayda N
Recommended Presentation Outline
My Name is …
The title of my article is…
I found it in…
My article is relevant and interesting because….
The Economics Article
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Economics
The study of the allocation of scarce resources: implies a cost to every action
Basic assumption
People are rational
People act to maximize their happiness
Economics is predictive
5
Economic Modeling
"The theory of economics does not furnish a body of settled conclusions immediately applicable to policy. It is a method rather than a doctrine, an apparatus of the mind, a technique of thinking which helps its possessor to draw correct conclusions." (John Maynard Keynes)
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Prices of Compliments
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N= I/Pn - (Pf / Pn) F
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Theory of the Firm
Firm Maximizes profits
Max: p = Revenue - Costs
Max: p = P(Q)* Q- C(Q)
First Order Conditions:
dp/dQ = P’(Q)*P + P(Q) - C’(Q) =0
P’(Q)*P + P(Q) = C’(Q)
Marginal Revenue = Marginal Costs
17
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$
0
AC
MC
P1
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X1
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Assumptions of Perfect Competition
Free Entr.
21 hours agoMercy Eke Week 2 Discussion Hamilton Depression.docxRAJU852744
21 hours ago
Mercy Eke
Week 2 Discussion: Hamilton Depression Rating Scale
COLLAPSE
Top of Form
Depression or Major Depressive Disorder is considered as a mental health disorder that negatively impacts how an individual feel, think and behave. Individuals who suffer from depression exhibit feelings of sadness and loss in interest in once enjoyed activities (Parekh. 2017). It can cause different kinds of emotional and physical problems and can minimize an individual’s ability to be functional in their daily routines. Annually, approximately 6.7% of adults are impacted by depression. It is estimated that 16.6% of individuals will experience depression at some time in their life (Parekh. 2017). Depression is said to manifest at any time, but on average, the first manifestation occurs during the late teens to mid-20s. The female population is susceptible to experience depression than the male population. Some research indicated that one-third of the female population would experience a major depressive episode in their lifetime (Parekh. 2017).
Among all the mental disorders, depression is one of the most treatable. It is estimated that between 80-90 % of individuals suffering from depression respond well to treatment and experienced remission of their symptoms (Parekh. 2017). As a mental health professional, prior to deciphering diagnosis and initiating diagnosis, it is paramount to conduct a complete diagnostic evaluation, which includes an interview and, if necessary, a physical examination (Parekh. 2017). Blood tests can be conducted to ascertain that depression is not precipitated by a medical condition like thyroid dysfunction. The evaluation is to identify specific symptoms, medical and family history, cultural factors, and environmental factors to derive a diagnosis and establish a treatment plan (Parekh. 2017). One of the assessment tools for depression is the Hamilton Depression Rating Scale. In this discussion, I will be discussing the psychometric properties of the Hamilton Depression Rating Scale and elaborate on when it is appropriate to utilize this assessment tool with clients, including whether the tool can be utilized to evaluate the efficacy of psychopharmacologic medications.
The Hamilton Depression Rating Scale (HDRS) was introduced in early 1960. It has been considered as a gold standard in depression studies and a preferred scale in the evaluation of depression treatment. It is the most vastly utilized observer-rated depression scale worldwide (Vindbjerg.et.al., 2019). The HDRS was initially created to measure symptoms severity in depressed inpatient; however, the 17-item HAM-D has advanced in over five decades into 11 modified versions that have been administered to various patient populations in an array of psychiatric, medical, and other research settings (Rohan.et.al., 2016). There are two most common versions with either 17 or 21 items and is scored between 0-4 points. Each item assists mental health professionals or c.
2/19/2020 Originality Report
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Spring 2020 - InfoTech Import in Strat Plan (ITS-831-08) - First Bi-Term • Week 4 Assignment
%81Total Score: High riskMohana Murali Krishna Karnati
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Running Head: SERVER VIRTUALIZATION 1
SERVER VIRTUALIZATION 8
Week 4 Assignment
Technet Case Study for Virtualization Mohana Murali Krishna Karnati
University of the Cumberlands
Technet Case Study for Virtualization
Technet is a hypothetical business in the storage manufacturing industry. This paper intend to elaborate the server virtualization concept using Microsoft
virtualization software from Windows server 2012R2. Organization’s Preparedness for Virtualization. As of now, the IT system design is a mishmash of old
frameworks that were obtained through various acquisitions of different providers in the storage industry. In any case, these old frameworks are aging and will soon
need to be upgraded. Generally, these old frameworks support applications that have been in service for about 10 years. The IT system situated in one of Technet
branch in Asia for instance comprise of old servers that have been in service for the last 5 years. These old servers were launched to support production and
productivity applications. The expense for permit of these old applications are presently being inspected to check whether they can be dropped and the
information moved to current Technet Enterprise Resource Planning (ERP) applications. Consequently, since several IT related components are potential
contender for upgrading, this makes the likelihood of changing over current physical server farms into virtualized computing resources appropriate. Microsoft
Licensing of Virtualized Environments
Datacenter and the Standard edition are the two license version for Windows Server 2012R2 offered by Microsoft. There is likewise a free version called
Hyper-V Server which is an independent system that only contains the Windows hypervisor, a driver model as well as virtualization modules. Every window
version underpins Hyper-V, which is Microsoft's Type-1 hypervisor offering, likewise referred to as a bare-metal installation, and each Hyper-V server is known as a
Host (Portnoy, 2012). The Windows Server.
20810chapter Information Systems Sourcing .docxRAJU852744
208
10
chapter Information Systems
Sourcing
After 13 years, Kellwood, an American apparel maker, ended its soups!to!nuts IS outsourcing
arrangement with EDS . The primary focus of the original outsourcing contract was to integrate
12 individually acquired units with different systems into one system. Kellwood had been satis-
" ed enough with EDS ’ s performance to renegotiate the contract in 2002 and 2008, even though
at each renegotiation point, Kellwood had considered bringing the IS operations back in house,
or backsourcing. The 2008 contract iteration resulted in a more # exible $105 million contract that
EDS estimated would save Kellwood $2 million in the " rst year and $9 million over the remaining
contract years. But the situation at Kellwood had changed drastically. In 2008, Kellwood had been
purchased by Sun Capital Partners and taken private. The chief operating of" cer (COO), who was
facing a mountain of debt and possibly bankruptcy, wanted to consolidate and bring the operations
back in house to give some order to the current situation and reduce costs. Kellwood was suffering
from a lack of IS standardization as a result of its many acquisitions. The chief information of" cer
(CIO) recognized the importance of IS standardization and costs, but she was concerned that the
transition from outsourcing to insourcing would cause serious disruption to IS service levels and
project deadlines if it went poorly. Kellwood hired a third!party consultant to help it explore the
issues and decided that backsourcing would save money and respond to changes caused by both the
market and internal forces. Kellwood decided to backsource and started the process in late 2009. It
carefully planned for the transition, and the implementation went smoothly. By performing stream-
lined operations in house, it was able to report an impressive $3.6 million savings, or about 17% of
annual IS expenses after the " rst year. 1
The Kellwood case demonstrates a series of decisions made in relation to sourcing. Both the
decision to outsource IS operations and then to bring them back in house were based on a series of
This chapter is organized around decisions in the Sourcing Decision Cycle. The ! rst question
regarding information systems (IS) in the cycle relates to the decision to make (insource) or
buy (outsource) them. This chapter ’ s focus is on issues related to outsourcing whereas issues
related to insourcing are discussed in other chapters of this book. Discussed are the critical
decisions in the Sourcing Decision Cycle: how and where (cloud computing, onshoring,
offshoring). When the choice is offshoring, the next decision is where abroad (farshoring,
nearshoring, or captive centers). Explored next in this chapter is the ! nal decision in the
cycle, keep as is or change in which case the current arrangements are assessed and modi-
! cations are made to the outsourcing arrangem.
21720201Chapter 14Eating and WeightHealth Ps.docxRAJU852744
2/17/2020
1
Chapter 14
Eating and Weight
Health Psychology (PSYC 172)
Professor: Andrea Cook, PhD
February 18, 2020
The Digestive System
– Food nourishes the body by providing energy for
activity
– Digestion begins in the mouth
• Salivary glands provide moisture that allows food to
have taste
• Importance of good mastication
The Digestive System
The Digestive System
– Food is swallowed and then moves through the
pharynx and esophagus
– Peristalsis moves food through the digestive
system
– In the stomach, food is mixed with gastric juices
so it can be absorbed by the small intestine
– Most nutrients are digested in the small intestine
– Digestion process is complete when waste is
eliminated
The Digestive System, Continued
2/17/2020
2
Microbiome
4YouTube: What is the human microbiome?
Supporting the Gut Microbiome
Dysbiosis = unbalanced gut microbiome
• associated with weight gain, insulin resistance,
inflammation
Probiotics
• contain live microorganisms
• maintain or improve the "good" bacteria (normal microflora)
in the body
• e.g., fermented foods, yogurt, sauerkraut, kimchi
Prebiotics
• act as food for human microflora
• helps improve microflora balance
• e.g., whole grains, bananas, greens, onions, garlic
5
https://www.mayoclinic.org/healthy-lifestyle/consumer-health/expert-
answers/probiotics/faq-20058065
Supporting the Gut Microbiome
Medication overuse
• anti-inflammatories, antibiotics, acid blocking drugs, and
steroids damage gut or block normal digestive function
Stress
• chronic stress alters the normal bacteria in the gut
Lifestyle
• plenty of fiber, water, exercise and rest
Healthy Defecation
• three bowel movements a day to three each week
• no intestinal pain or bloating
• no straining
6
https://drhyman.com/blog/2014/10/10/tend-inner-garden-gut-flora-may-
making-sick/
2/17/2020
3
Bristol Stool Chart
7
Factors in Weight Maintenance
– Stable weight occurs when calories eaten equal those
expended for body metabolism and physical exercise
[OLD THINKING]
– Complicated interplay of nutrients, hormones, and
inflammation
• Metabolic rates differ from person to person
• Ghrelin, a hormone, stimulates appetite
• Leptin, a protein, signals satiation and fat storage
• Insulin, a hormone produced in pancreas
– unlocks cells for glucose use for energy
– cues hypothalamus for satiation and decreased appetite
Factors in Weight Maintenance
What is obesity?
– Overeating is not the sole cause of obesity
– Various methods to assess body fat
• Skin-fold technique
• Percentage body fat
• Body mass index (BMI)
– Can also be thought of in terms of social and
cultural standards
– ideal body = thinner in past 50 years
What is Obesity?
2/17/2020
4
BMI
10
– Obesity rates have increased, especially
“extreme” obesity
• past 30 years obesity rates have nearly doubled to
600 million
• 37.8% of US adults are obese and an additional 32.6%
are over.
2020/2/21 Critical Review #2 - WebCOM™ 2.0
https://smc.grtep.com/index.cfm/smcc/page/2criticalreviews 1/10
Santa Monica College Democracy and Di�erence Through the Aesthetics
of Film
Tahvildaran
Assignment Objectives: Enhance and/or improve critical thinking and
media literacy skills by:
1. Developing a clear and concise thesis statement (an
argument) in response to the
following question: Does the �lm have the power to
transform political sensibilities?
2. Writing an outline for a �ve paragraph analytical essay
building on a clear and
concise thesis statement, including topic sentences and
secondary supports.
3. Identifying and explaining three scenes from the �lm text in
support of the thesis
statement/argument.
4. Writing an introductory paragraph for the outlined analytical
essay
Be sure to read thoroughly the writing conventions below before beginning this
assignment.
Note: You are NOT writing a full essay; rather, you are outlining an analytical
essay by completing the dialogue in the boxes below.
Writing a Critical Review (analytical) Essay
2020/2/21 Critical Review #2 - WebCOM™ 2.0
https://smc.grtep.com/index.cfm/smcc/page/2criticalreviews 2/10
1. Every essay that you write for this course must have a clear thesis, placed
(perhaps) somewhere near the end of the introductory paragraph. Simply
stated, a THESIS (or ARGUMENT) expresses, preferably in a single sentence,
the point you want to make about the text that is the subject of your essay. A
THESIS should be an opinion or interpretation of the text, not merely a fact or
observation. The best possible THESIS will answer some speci�c questions
about the text. Very often the THESIS contains an outline of the major points
to be covered in the essay. A possible thesis for an essay on character in
Perry Henzell’s The Harder They Come might read somewhat as follows:
The protagonist of THTC is not a hero in the epic sense of the word, but a
self-centered young man bred of economic oppression and cultural
dependency. The characters in this �lm have no real psychological depth, but
are markers for a society of consumption and momentary glory.
(You might then go on to exemplify from the text and argue in favor or
against this interpretation: your essay need not hold to only one perspective.)
What single, clear QUESTION does the above THESIS attempt to answer?
2. Each essay should be organized into �ve (5) paragraphs, each based on one
of two to four major ideas, which will comprise the BODY of the essay. Each
paragraph must have a topic sentence, often (but not always) towards the
beginning of the paragraph, which clearly states the ARGUMENT or point to
be made in the paragraph. Following the thesis set forth.
Beyond Degrees - Empowering the Workforce in the Context of Skills-First.pptxEduSkills OECD
Iván Bornacelly, Policy Analyst at the OECD Centre for Skills, OECD, presents at the webinar 'Tackling job market gaps with a skills-first approach' on 12 June 2024
How to Fix the Import Error in the Odoo 17Celine George
An import error occurs when a program fails to import a module or library, disrupting its execution. In languages like Python, this issue arises when the specified module cannot be found or accessed, hindering the program's functionality. Resolving import errors is crucial for maintaining smooth software operation and uninterrupted development processes.
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...PECB
Denis is a dynamic and results-driven Chief Information Officer (CIO) with a distinguished career spanning information systems analysis and technical project management. With a proven track record of spearheading the design and delivery of cutting-edge Information Management solutions, he has consistently elevated business operations, streamlined reporting functions, and maximized process efficiency.
Certified as an ISO/IEC 27001: Information Security Management Systems (ISMS) Lead Implementer, Data Protection Officer, and Cyber Risks Analyst, Denis brings a heightened focus on data security, privacy, and cyber resilience to every endeavor.
His expertise extends across a diverse spectrum of reporting, database, and web development applications, underpinned by an exceptional grasp of data storage and virtualization technologies. His proficiency in application testing, database administration, and data cleansing ensures seamless execution of complex projects.
What sets Denis apart is his comprehensive understanding of Business and Systems Analysis technologies, honed through involvement in all phases of the Software Development Lifecycle (SDLC). From meticulous requirements gathering to precise analysis, innovative design, rigorous development, thorough testing, and successful implementation, he has consistently delivered exceptional results.
Throughout his career, he has taken on multifaceted roles, from leading technical project management teams to owning solutions that drive operational excellence. His conscientious and proactive approach is unwavering, whether he is working independently or collaboratively within a team. His ability to connect with colleagues on a personal level underscores his commitment to fostering a harmonious and productive workplace environment.
Date: May 29, 2024
Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
-------------------------------------------------------------------------------
Find out more about ISO training and certification services
Training: ISO/IEC 27001 Information Security Management System - EN | PECB
ISO/IEC 42001 Artificial Intelligence Management System - EN | PECB
General Data Protection Regulation (GDPR) - Training Courses - EN | PECB
Webinars: https://pecb.com/webinars
Article: https://pecb.com/article
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For more information about PECB:
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Slideshare: http://www.slideshare.net/PECBCERTIFICATION
it describes the bony anatomy including the femoral head , acetabulum, labrum . also discusses the capsule , ligaments . muscle that act on the hip joint and the range of motion are outlined. factors affecting hip joint stability and weight transmission through the joint are summarized.
Reimagining Your Library Space: How to Increase the Vibes in Your Library No ...Diana Rendina
Librarians are leading the way in creating future-ready citizens – now we need to update our spaces to match. In this session, attendees will get inspiration for transforming their library spaces. You’ll learn how to survey students and patrons, create a focus group, and use design thinking to brainstorm ideas for your space. We’ll discuss budget friendly ways to change your space as well as how to find funding. No matter where you’re at, you’ll find ideas for reimagining your space in this session.
Chapter wise All Notes of First year Basic Civil Engineering.pptxDenish Jangid
Chapter wise All Notes of First year Basic Civil Engineering
Syllabus
Chapter-1
Introduction to objective, scope and outcome the subject
Chapter 2
Introduction: Scope and Specialization of Civil Engineering, Role of civil Engineer in Society, Impact of infrastructural development on economy of country.
Chapter 3
Surveying: Object Principles & Types of Surveying; Site Plans, Plans & Maps; Scales & Unit of different Measurements.
Linear Measurements: Instruments used. Linear Measurement by Tape, Ranging out Survey Lines and overcoming Obstructions; Measurements on sloping ground; Tape corrections, conventional symbols. Angular Measurements: Instruments used; Introduction to Compass Surveying, Bearings and Longitude & Latitude of a Line, Introduction to total station.
Levelling: Instrument used Object of levelling, Methods of levelling in brief, and Contour maps.
Chapter 4
Buildings: Selection of site for Buildings, Layout of Building Plan, Types of buildings, Plinth area, carpet area, floor space index, Introduction to building byelaws, concept of sun light & ventilation. Components of Buildings & their functions, Basic concept of R.C.C., Introduction to types of foundation
Chapter 5
Transportation: Introduction to Transportation Engineering; Traffic and Road Safety: Types and Characteristics of Various Modes of Transportation; Various Road Traffic Signs, Causes of Accidents and Road Safety Measures.
Chapter 6
Environmental Engineering: Environmental Pollution, Environmental Acts and Regulations, Functional Concepts of Ecology, Basics of Species, Biodiversity, Ecosystem, Hydrological Cycle; Chemical Cycles: Carbon, Nitrogen & Phosphorus; Energy Flow in Ecosystems.
Water Pollution: Water Quality standards, Introduction to Treatment & Disposal of Waste Water. Reuse and Saving of Water, Rain Water Harvesting. Solid Waste Management: Classification of Solid Waste, Collection, Transportation and Disposal of Solid. Recycling of Solid Waste: Energy Recovery, Sanitary Landfill, On-Site Sanitation. Air & Noise Pollution: Primary and Secondary air pollutants, Harmful effects of Air Pollution, Control of Air Pollution. . Noise Pollution Harmful Effects of noise pollution, control of noise pollution, Global warming & Climate Change, Ozone depletion, Greenhouse effect
Text Books:
1. Palancharmy, Basic Civil Engineering, McGraw Hill publishers.
2. Satheesh Gopi, Basic Civil Engineering, Pearson Publishers.
3. Ketki Rangwala Dalal, Essentials of Civil Engineering, Charotar Publishing House.
4. BCP, Surveying volume 1
A review of the growth of the Israel Genealogy Research Association Database Collection for the last 12 months. Our collection is now passed the 3 million mark and still growing. See which archives have contributed the most. See the different types of records we have, and which years have had records added. You can also see what we have for the future.
1. 1
Intermodal Autonomous Mobility-on-Demand
Mauro Salazar1,2, Nicolas Lanzetti1,2, Federico Rossi2,
Maximilian Schiffer2,3, and Marco Pavone2
Abstract—In this paper we study models and coordination poli-
cies for intermodal Autonomous Mobility-on-Demand (AMoD),
wherein a fleet of self-driving vehicles provides on-demand
mobility jointly with public transit. Specifically, we first
present
a network flow model for intermodal AMoD, where we capture
the coupling between AMoD and public transit and the goal is
to maximize social welfare. Second, leveraging such a model,
we design a pricing and tolling scheme that allows the system
to recover a social optimum under the assumption of a perfect
market with selfish agents. Third, we present real-world case
studies for the transportation networks of New York City and
Berlin, which allow us to quantify the general benefits of
intermodal AMoD, as well as the societal impact of different
vehicles. In particular, we show that vehicle size and powertrain
type heavily affect intermodal routing decisions and, thus,
system
efficiency. Our studies reveal that the cooperation between
AMoD
fleets and public transit can yield significant benefits compared
to an AMoD system operating in isolation, whilst our proposed
tolling policies appear to be in line with recent discussions for
the case of New York City.
I. INTRODUCTION
2. TRAFFIC congestion is soaring all around the world.
Besidesmere discomfort for passengers, congestion causes
severe
economic and environmental harm, e.g., due to the loss of
working hours and pollutant emissions such as CO2, partic-
ulate matter, and NOx [1]. In 2013, traffic congestion cost
U.S. citizens 124 Billion USD [2]. Notably, transportation
remains one of a few sectors in which emissions are still
increasing [3]. Governments and municipalities are struggling
to find sustainable ways of transportation that can match
mobility needs and reduce environmental harm as well as
congestion.
To achieve sustainable modes of transportation, new mobil-
ity concepts and technology changes are necessary. However,
the potential to realize such concepts in urban environments is
limited, since upgrades to available infrastructures (e.g., roads
and subway lines) and their capacity are often extremely costly
and require decades-long planning timelines. Thus, mobility
concepts that use existing infrastructure in a more efficient way
are especially attractive. In this course, mobility-on-demand
services appear to be particularly promising. Herein, two main
concepts exist. On the one hand, free floating car sharing
systems strive to reduce the total number of private vehicles
in city centers. However, these systems offer limited flexibility
and are generally characterized by low adoption rates that
result from low vehicle availabilities due to the difficulty of
1Institute for Dynamic Systems and Control ETH Zürich, Zurich
(ZH),
Switzerland {samauro,lnicolas}@ethz.ch
2Department of Aeronautics and Astronautics, Stanford
University, Stanford
(CA), United States {samauro,frossi2,pavone}@stanford.edu
3. 3TUM School of Management, Technical University of Munich,
80333
Munich, Germany [email protected]
Fig. 1. The I-AMoD network consists of a road digraph, a
walking digraph
and a public transportation digraph. The colored dots denote
intersections or
stops and the black arrows represent road links, pedestrian
pathways or public
transit arcs. The grey dotted lines denote geographically close
nodes while
the grey arrows are the mode-switching arcs connecting them.
rebalancing empty vehicles to counter asymmetric customer
demand [4], [5]. On the other hand, ride-hailing systems aim
to enhance and extend the service of taxi fleets. However,
current studies show that ride-hailing services can worsen
traffic congestion significantly due to the induced demand
and the vehicle-miles traveled by empty vehicles. Moreover,
additional demand may also result from a shift in the modal
split as ride-hailing operators offer a low cost point-to-point
connection. Indeed, recent studies for the Manhattan area
revealed the massive magnitude of such effects: from 2013 to
2018, the number of for-hire vehicles exploded from 47,000
to 103,000, 68,000 of which are employed for ride-hailing
services. Due to this increase, the average traffic speed dropped
to 4.7 mph, which equals a brisk walk [6].
Autonomous Mobility-on-Demand (AMoD) systems hold
promise as a future mobility concept in urban environments.
They comprise a fleet of robotic, self-driving vehicles that
transport passengers between their origins and destinations.
A central operator runs such systems by assigning passenger
requests to vehicles and computing rebalancing routes for the
unassigned empty vehicles, in order to re-align their geo-
graphical distribution with demand for transportation. Thus, an
4. AMoD system can replace a conventional taxi, car sharing, or
ride-hailing fleet, while offering several advantages compared
to the previously discussed concepts: First, no relocation costs
for drivers arise; second, due to continuous rebalancing, much
higher vehicle utilization rates can be achieved; third, the
centralized control of the complete fleet allows for more op-
erational flexibility and efficiency compared to ride-hailing or
taxi fleets where a central operator can suggest, but not directly
control, vehicle routes. This enables the operator to adopt
global fleet-wide routing strategies to mitigate congestion.
2
However, despite these benefits, AMoD systems operating
in isolation might still worsen congestion due to shifts in
the modal share. To secure sustainable and congestion-free
urban mobility, an AMoD system should rather interact with
and complement existing mass transit options. Against this
backdrop, our study develops modeling and optimization tools
to assess the benefits of an intermodal transportation system
that combines public transit with AMoD (Fig. 1).
Related literature: Our work contributes to three different
research streams, namely: AMoD systems, congestion pricing,
and multimodal passenger transport, which we review in the
following.
A number of approaches to characterize and control AMoD
systems in isolation are available, ranging from queuing-
theoretical models [7]–[9] to simulation-based models [10]–
[12] and multi-commodity network flow models [13]–[15].
Queueing-theoretical models capture the stochasticity of the
customer arrival process and are amenable to efficient control
synthesis. However, their complex structure makes it difficult
5. to capture the interaction with other modes of transportation.
Simulation-based models capture transportation systems with
very high fidelity, incorporating complex choice models and
microscopic interactions, but are generally not amenable to
efficient optimization. Network flow models are amenable to
efficient optimization and allow for the inclusion of a variety of
complex constraints. Accordingly, they have seen wide use in
problems ranging from control of AMoD systems in congested
road networks [13], [16], [17], to cooperative control of AMoD
systems and the electric power network [18], and control of
human-operated MoD systems [19].
Congestion pricing in general has been widely investigated,
and a body of theoretical work [20]–[23] and experimental
results [24]–[26] are available. However, only few approaches
focus on pricing in the context of AMoD: Specifically, [27]
focuses on congestion pricing for self-driving vehicles by
incentivizing socially and environmentally aware travel modes,
while [28] proposes pricing schemes to foster the use of
AMoD systems. However, these studies comprise logit mod-
eling approaches and rely on agent-based simulations that
assess the performance of pre-determined intermodal AMoD
(I-AMoD) routing policies. In contrast, our optimization-based
approach identifies the best achievable performance of an
I-AMoD system and enables the synthesis of policies that steer
a system towards such an optimum.
Literature on intermodal passenger transportation including
Mobility-on-Demand (MoD) and AMoD is still sparse. First,
studies on the interplay between AMoD and public trans-
portation exist, focusing either on fluidic [29] or simulation-
based [11], [30], [31] models. However, these studies focus
on the analysis of specific scenarios, as opposed to the
optimization of joint control policies for AMoD systems and
public transit. In general, to the best of the authors’ knowledge,
only descriptive analyses of intermodal passenger transport
6. including MoD exist [32].
In summary, some optimization approaches and control
policies for AMoD systems are available. These approaches,
however, do not capture the interaction between AMoD and
public transit. Focusing on pricing schemes, existing studies
address individual externalities (e.g., congestion), but no study
captures the interplay between multiple externalities arising
from the synchronization of different modes of transportation.
To date, there exist no optimization frameworks that capture
optimal coordination policies for I-AMoD systems whilst
assessing their achievable performance.
Statement of contributions: The goal of this paper is to
introduce a mesoscopic optimization approach for I-AMoD
systems. Specifically, the contribution of this paper is fourfold:
First, we develop a multi-commodity network flow optimiza-
tion model that captures the joint operations of AMoD systems
and public transit. In our model, the objective is to maximize
the social welfare, i.e., to minimize the customers’ travel time
together with the operational costs of different transportation
modes. Herein, we also consider energy consumption, pollu-
tion, and congestion effects. Second, we propose a pricing and
tolling scheme that helps to realize the social optimum in the
presence of selfish customers and AMoD operators. Third, we
present real-world case studies for New York City (NYC) and
Berlin accounting for the impact of the urban transportation
network and of the AMoD vehicles’ characteristics on the
achievable societal costs, including travel times and emissions.
Fourth, we derive managerial insights on the benefits of
I-AMoD systems: our results show that an I-AMoD system
can significantly reduce travel times, pollutant emissions, total
number of cars, and overall costs compared to an AMoD
system operating in isolation. Interestingly, our pricing and
tolling scheme is aligned with recently proposed congestion
7. surcharges for ride-hailing vehicles [33].
A preliminary version of this paper was presented at the
2018 Intelligent Transportation Systems Conference [34]. In
this revised and extended version, we broaden the discussion
of the literature, detail models for travel time, road congestion,
and energy consumption, present a rigorous optimality proof
for the proposed pricing scheme, and discuss new numerical
results for the central neighborhoods of NYC and Berlin.
Organization: The remainder of this paper is structured as
follows: In Section II we present a flow optimization model
for I-AMoD. Section III derives a pricing and tolling scheme
to steer self-interested agents towards a social optimum. Sec-
tion IV presents case studies for NYC and Berlin, which are
complementary in terms of spatial displacement, road network
structure, and public transit. Finally, Section V concludes the
paper with a short summary and an outlook on future research.
II. NETWORK FLOW MODEL FOR I-AMOD
This section presents a network flow optimization approach
for intermodal AMoD. In particular, we consider 1) the as-
signment of customer requests to transport flows, 2) different
modes of transportation, 3) road capacity limits, 4) vehicle
based energy consumption models for the AMoD fleet, and 5)
rebalancing operations to reposition empty vehicles according
to mobility demand. Assuming a centrally controlled system,
we introduce a network flow model in Section II-A, and
discuss the representation of travel time and road congestion
in Section II-B. Section II-C presents our energy consumption
model. Section II-D details the I-AMoD optimization problem
3
8. and its objective. Finally, Section II-E discusses our modeling
assumptions. Readers not familiar with basic elements from
graph theory are referred to Appendix A.
A. Multi-commodity Flow Model
We model the transportation system and its different trans-
portation modes on a digraph G = (V ,A ) as shown in Fig. 1.
It consists of a set of nodes V and a set of arcs A ⊆ V ×V ,
containing a road network layer GR = (VR,AR), a public
transportation layer GP = (VP,AP), and a walking layer GW =
(VW,AW). The road network consists of intersections i ∈ VR
and road links (i, j) ∈ AR. We model public transportation,
i.e., tram and subway lines as distinct trees, using a set of
station nodes i ∈ VP and a set of line segments (i, j) ∈ AP.
The walking network comprises walkable streets (i, j) ∈ AW
between intersections i∈ VW. Finally, we model the possibility
of customers switching transportation modes (e.g., exiting
the subway or hailing an AMoD ride) by connecting the
pedestrian layer to the road and public transportation layers
with a set of mode-switching arcs AC ⊆ VR ×VW ∪ VP ×VW,
whereby VR ∩VP = /0. Accordingly, V = VW ∪ VR ∪ VP and
A = AW∪ AR∪ AP∪ AC hold. Given the structural properties
of road and walking networks in urban environments, we make
the following assumption without loss of generality.
Assumption 1. The graphs G , GR, and GW are strongly
connected.
Traversing an arc (i, j) of length si j takes on average ti j time
units. For mode-switching arcs, ti j denotes the time needed
to switch between two means of transportation. We represent
travel requests as follows.
Definition II.1 (Requests). A request r is a triple (o,d,α) ∈
9. V ×V ×R+, given by its origin node o, its destination node
d, and its request rate α > 0. We denote a set of M requests
by R :={rm}m∈ M , where M :={1,...,M}.
Assumption 2. All requests appear on the walking digraph,
i.e., om,dm ∈ VW, ∀ m ∈ M .
Considering the different transportation modes, fm (i, j) de-
notes the flow (i.e., the number of customers per unit time)
traversing arc (i, j)∈ A for a certain travel request m. To
account for rebalancing flows of AMoD vehicles between a
customer’s drop-off and the next customer’s pick-up, f0 (i, j)
denotes the flow of empty vehicles on road arcs (i, j) ∈ AR.
For the customers and rebalancing flows it holds that
∑
i:(i, j)∈ A
fm(i, j)+1 j=om ·αm = ∑
k:( j,k)∈ A
fm( j,k)+1 j=dm ·αm
∀ m ∈ M , j ∈ V (1a)
∑
i:(i, j)∈ AR
(
f0 (i, j)+ ∑
m∈ M
fm(i, j)
)
= ∑
10. k:( j,k)∈ AR
(
f0 ( j,k)+ ∑
m∈ M
fm( j,k)
)
∀ j ∈ VR (1b)
fm (i, j)≥ 0 ∀ m ∈ M , (i, j)∈ A (1c)
f0 (i, j)≥ 0 ∀ (i, j)∈ AR, (1d)
where 1 j=x is the boolean indicator function. Specifically, we
preserve flow conservation for every transportation demand
in (1a). Analogously, we guarantee flow conservation for ve-
hicles on every road node with (1b) and ensure non-negativity
of flows by (1c) and (1d).
B. Travel Time and Road Congestion
The modeling of travel times and road congestion heavily
affects the tractability of our solution approach but also the
accuracy of its results. We choose a modeling approach that
is a good trade-off between accuracy and tractability for
mesoscopic analysis.
We assume a constant walking speed for pedestrian arcs,
infer travel times for public transit from the public transit
schedules, and use constant average values for mode-switching
arcs. On roads, congestion strongly influences travel times. For
network flow models it is common practice to scale the nomi-
nal travel time at free-flow speed with a volume delay function.
11. In this paper, we use the Bureau of Public Roads (BPR)
function [35], which has the form FBPR(x) = 1+0.15·x4, with
x representing the ratio between the nominal capacity of the
road and the vehicles’ flow traversing it. With this model, the
travel time for the exogenous traffic flow uRi j in the absence
of
AMoD vehicles on a road arc (i, j)∈ AR results to
ti j = t
N
i j ·FBPR
(
uRi j/c
R
i j
)
, (2)
with the travel time at free-flow speed denoted as t Ni j and the
nominal road capacity cRi j depending on the free-flow speed,
the number of lanes, and the space occupied by one vehicle.
We model the travel time at free-flow speed on urban road
arcs with the following rationale: We assume a car to traverse
an arc starting from idling, accelerating with a maximum
acceleration amax until reaching the free-flow speed vmax,i j ,
driving with this speed until almost the end of the arc, and
finally decelerating with maximum deceleration −amax, in
order to stop at the end of the arc. Capturing acceleration
and deceleration in this way allows to account for the driving
behaviour also in between crossroads. Consider a road arc with
total length si j . The car reaches the maximum velocity at time
t∗ i j = vmax,i j/amax such that, assuming the total travel time
to
12. be larger than the total acceleration and deceleration time, the
total travel distance on the road arc must satisfy
si j =−
v2max,i j
amax
+ vmax,i j ·t Ni j ∀ (i, j)∈ AR,
yielding the free-flow travel time on road arcs
t Ni j =
vmax,i j
amax
+
si j
vmax,i j
∀ (i, j)∈ AR. (3)
We compare our model with the guidelines provided by the
Transportation Research Board (TRB) [36, Ch. 15] in Fig. 2,
showing that the travel time computed with our model for
different values of link length si j and free-flow speed vmax,i j
is in good agreement with the TRB guidelines.
In order to limit the endogenous impact of the AMoD
vehicles on road traffic and travel time, we impose a maximum
capacity threshold cR,thi j as
f0 (i, j)+ ∑
m∈ M
fm (i, j)+ u
R
13. i j ≤ c
R,th
i j ∀ (i, j)∈ AR. (4)
4
T
ra
v
e
l
T
im
e
D
if
fe
re
n
c
e
[
%
]
Link Length [m]
14. S
p
ee
d
L
im
it
[k
m
/
h
]
200 400 600 800 1000 1200 1400 1600
40
50
60
70
80
90
Fig. 2. Relative free-flow travel time difference from the TRB
model for road
arcs with different length and free-flow speed.
Specifically, we choose the capacity threshold so that the
presence of endogenous AMoD traffic does not increase travel
time by more than ∆rtime ·t Ni j . That is, for each arc (i, j)∈
AR
the capacity threshold cR,thi j satisfies
15. ti j + ∆rtime ·t Ni j = t
N
i j ·FBPR
(
cR,thi j /c
R
i j
)
. (5)
Choosing a sufficiently small ∆rtime, the total travel time
results from the combination of exogenous and endogenous
vehicle flows and can be approximated with its upper bound
at full capacity as
ti j = t
N
i j ·FBPR
(
cR,thi j /c
R
i j
)
∀ (i, j)∈ AR. (6)
C. Energy Consumption of AMoD Vehicles
We compute the energy consumption for AMoD vehicles
by applying the general approach from [37] to the New
16. York City urban driving cycle [38]. To account for differ-
ent mean velocities, we multiply the time and divide the
velocity trajectory of the driving cycle by the scaling factor
rscale =
ti j
si j
· scycletcycle , where scycle and tcycle represent the nominal
spatial and temporal length of the driving cycle. Assuming the
vehicles to be powered either by gasoline engines with start
and stop capabilities or by electrical motors, we compute the
energy consumption E in terms of fuel energy Ef or electrical
battery energy Eb, respectively, and assign it to the road arc
(i, j)∈ AR as ei j = E ·si j/scycle.
For a driving cycle consisting of a velocity, an acceleration
and a road inclination trajectory over time
(
v(t),a(t),ϑ (t)
)
,
the requested power at the wheels is
Preq(t) =
(
mv ·a(t)+ mv ·g·sin
(
ϑ (t)
)
+ cr ·mv ·g·cos
17. (
ϑ (t)
)
+
ρair
2
·cd ·Af ·v(t)2
)
·v(t),
(7)
where the first term in brackets accounts for the acceleration of
the vehicle, the second one is the gravitational force, the third
one the rolling friction force, and the last one the aerodynamic
drag. Hereby, mv is the mass of the car, g the gravitational
acceleration, cr the rolling friction coefficient, ρair the air
density, cd the aerodynamic drag coefficient, and Af the frontal
area of the car. Most of these parameters vary depending on
the vehicle type. Assuming a constant final drive efficiency
ηfd, the power provided by the propulsion system Pp is
Pp =
{ 1
ηfd
·Preq if Preq ≥ 0
ηfd ·Preq + Pbrk if Preq < 0,
(8)
18. where Pbrk ≥ 0 is the braking power exerted by the hydraulic
brakes. We model the power provided to the auxiliaries (e.g.,
heating, ventilation, air-conditioning, ECU, hydraulic brakes,
etc.) with a vehicle-size-dependent constant power Paux. Fi-
nally, we compute the energy consumption of Internal Com-
bustion Engine Vehicle (ICEV) and Battery Electric Vehicle
(BEV) as follows.
1) ICEV: In this case, the car is powered by an internal
combustion engine which power needs to match the propulsive
power defined in (8), i.e., Pe = Pp. We model the engine using a
power Willans approximation [39] as Pe = ηe ·Pf−Pe,0, where
Pe is the engine power, ηe the internal efficiency of the engine,
Pf the fuel power, and Pe,0 the engine friction power. Assuming
start and stop capabilities, the fuel power is
Pf =
{
0 if Preq ≤ 0
1
ηe
·
(
Pe + Pe,0 +
ton
ttot
·Paux
)
if Preq > 0,
(9)
19. where ttot is the length of the driving cycle and ton captures
the amount of time the ICE is on as
∫ ttot
0 1Preq(t)>0 dt. The fuel
energy consumption is then
Ef =
∫ ttot
0
Pf(t)dt. (10)
2) BEV: In this case, the car is powered solely by an
electrical motor such that the mechanical power needs to
match the propulsive power (8) as Pm = Pp. We model the
efficiency of the electric motor in a piecewise affine manner,
to distinguish motor and generator operation. Specifically, the
electrical motor power Pel is related to the mechanical motor
power as
Pel =
{ 1
ηm
·Pm if Pm ≥ 0
ηg ·Pm if Pm < 0,
(11)
where ηm and ηg represent the electrical motor and generator
efficiency, respectively. The power delivered by the battery Pb
is the sum of the electrical motor power and the auxiliary
power as Pb = Pel + Paux. We model the internal power drawn
from the battery Pi in a piecewise affine manner, distinguishing
20. between battery charge and discharge. In particular, we have
that
Pi =
{ 1
ηdis
·Pb if Pb ≥ 0
ηchg ·Pb if Pb < 0,
(12)
where ηchg and ηdis represent the battery charging and dis-
charging efficiency, respectively. Finally, the electrical energy
consumption is
Eb =
∫ ttot
0
Pi(t)dt. (13)
D. I-AMoD Objective and Optimization Problem
Our goal is to maximize the social welfare by minimizing
the customers’ travel time together with the operational costs
incurred by the I-AMoD system. We define commuting costs
that depend on the customers’ value of time and on operational
costs for the AMoD fleet and the public transportation. We
assume customers to have the same value of time VT; we
define the costs for the AMoD fleet as mileage-dependent costs
VD,R to account for maintenance, depreciation, and the AMoD
operators’ normal profits (i.e., the profits which compensate
21. 5
them for their risk and opportunity cost [40]), as well as energy
costs VE to account for fuel or electricity consumption. We
cumulate all operational costs for the public transportation
network as VD,P. Finally, we add a quadratic regularization
term with a very small weight VQ. While this term does
not appreciably influence the total cost, it does ensure strict
convexity for the problem – a key property that enables the
design of a socially-optimal pricing and tolling scheme in
Section III. The social cost is then
JM
(
{ fm (·,·)}m, f0 (·,·)
)
= VT ·∑
m∈ M ,(i, j)∈ A
ti j · fm (i, j)
+∑
(i, j)∈ AR
(VD,R ·si j +VE ·ei j)·
(
f0 (i, j)+ ∑
m∈ M
fm (i, j)
)
+VD,P ·∑
22. (i, j)∈ AP
si j ·∑
m∈ M
fm (i, j)
+VQ ·
(
∑
m∈ M
∑
(i, j)∈ A
fm ( j, j)
2
+ ∑
(i, j)∈ AR
f0 (i, j)
2
)
.
(14)
We state the I-AMoD optimization problem as follows.
Problem 1 (I-AMoD Optimization Problem). Given the set
of transportation demands R, the optimal customer flows
{ fm (·,·)}m and rebalancing flows f0 (·,·) result from
23. min
{ fm(·,·)}m, f0(·,·)
JM
(
{ fm (·,·)}m, f0 (·,·)
)
s.t. Eq. (1), Eq. (4), Eq. (14).
(15)
For ease of notation, we reformulate Problem 1 in matrix
form. With a slight abuse of notation, let a denote the arc label
of an arbitrary arc (i, j). We define
[xR,m]a := fm (i, j), [x0]a := f0 (i, j),
[cR]a := VT ·ti j +VD,R ·si j +VE ·ei j, [c0]a := VD,R ·si j +VE
·ei j,
[hR]a := c
R,th
i j −u
R
i j ∀ (i, j)∈ AR (16a)
[xW,m]a := fm (i, j), [cW]a := VT ·ti j
∀ (i, j)∈ AW (16b)
[xP,m]a := fm (i, j), [cP]a := VT ·ti j +VD,P ·si j
∀ (i, j)∈ AP (16c)
[xC,m]a := fm (i, j), [cC]a := VT ·ti j
∀ (i, j)∈ AC, (16d)
24. and denote by BR ∈ {−1,0,1}|VR|×|AR| the inci-
dence matrix of the road graph GR. Finally, we
define xm := (xR,m,xW,m,xP,m,xC,m) and denote by
B ∈ {−1,0,1}|V |×|A | the incidence matrix of the full
graph G . With this notation, we reformulate Problem 1 using
the incidence matrix to express flow conservation constraints.
Problem 2 (I-AMoD Optimization Problem Revisited). Given
the set of transportation demands R, the optimal customer
flows {xm}m and rebalancing flows x0 result from the quadratic
optimization problem
min
{xR,m,xW,m,
xP,m,xC,m}m,x0
∑
i∈ M
VQ ·x>R,mxR,m + c
>
R xR,m +VQ·x
>
W,mxW,m + c
>
WxW,m
+VQ ·x>P,mxP,m + c
>
P xP,m +VQ ·x
>
C,mxC,m + c
25. >
C xC,m
+VQ ·x>0 x0 + c
>
0 x0 (17a)
s.t. Bxm = bm ∀ m ∈ M (17b)
BR
(
∑
m∈ M
xR,m + x0
)
= 0 (17c)
∑
m∈ M
xR,m + x0 ≤ hR (17d)
xm ≥ 0, x0 ≥ 0 ∀ m ∈ M , (17e)
where [bm]i = −αm for the origin node, [bm]i = +αm for the
destination node, and [bm]i = 0 otherwise. The linear terms
in (17a) account for the cost incurred when travelling on the
corresponding digraph by customers and rebalancing vehicles.
The quadratic terms act as regularizers. Conservation of
customers and vehicles is guaranteed by (17b) and (17c)
respectively. Constraint (17d) captures road congestion.
26. Lemma II.2. Problem 1 and Problem 2 are solution equiva-
lent, i.e., the optimal solution of Problem 1 denotes the optimal
solution of Problem 2 and vice versa.
Proof. Substituting cost vectors and variables in (17a) directly
shows the equivalence. With
[Bxm]i = ∑
i:(i, j)∈ A
fm (i, j)− ∑
j:(i, j)∈ A
fm (i, j) =
+αm if i = dm
−αm if i = om
0 else,
(1a) is equivalent to (17b) and (17c)–(17e) follow analogously.
Lemma II.3. Problems 1 is feasible and has a unique solution.
Proof. Feasibility: The proof is constructive. By Assumption
2, requests originate and end on nodes in the walking graph.
By Assumption 1 the walking graph is strongly connected,
therefore, there always exists a path connecting any pair of
nodes o,d ∈ VW. Also, the capacity of all arcs in AW is infinite.
For each customer request r = (o,d,α) wishing to travel from
node o to node d, select a path p connecting o to d containing
only arcs in the pedestrian graph, and set flows flows fm (·,·)
equal to α for all arcs in the path and zero otherwise. The
resulting flow is a feasible solution. Uniqueness:
27. Solution
uniqueness follows directly from strict convexity, the affinity
of the constraints of Problem 2, and Lemma II.2.
E. Discussion
A few comments are in order. First, we consider a time-
invariant transportation demand. This assumption is in order if
the requests change slowly compared to the average travel time
of individual trips, as is often observed in densely populated
urban environments [41]. Second, we do not explicitly account
for the stochastic nature of exogenous traffic and of the
customer arrival process. Given the mesoscopic perspective
of our study, this deterministic representation is in order as it
captures such stochastic processes on average [9]. Third, we
6
allow fractional customer and vehicle flows. We show in Sec-
28. tion IV-B1, that the resulting accuracy loss is negligible for the
mesoscopic perspective of our study. Moreover, Problem 2 can
be solved in polynomial time with off-the-shelf optimization
algorithms providing global optimality guarantees for the so-
lution found. For real-time applications, randomized sampling
methods can be used to compute integer-valued flows from
fractional flows, yielding near-optimal routes for individual
vehicles and customers [42, Ch. 4], whilst new information can
be accounted for as it is revealed through a receding-horizon
framework. Fourth, we adopt a threshold model to capture
road congestion. On the one hand, such a model allows us to
contain the impact of AMoD vehicles on road traffic, whilst
on the other hand it can be expressed as a linear inequality
constraint. Methods to account for the impact of endogenous
traffic on travel time via volume-delay functions such as the
BPR function [35] have been presented in [17] for the AMoD-
only problem, whereas convex approximations and relaxations
readily applicable to the I-AMoD case can be found in [16].
In this paper, we focus on scenarios where the AMoD traffic
cannot exceedingly impact the road network in order to devise
road tolling schemes that provide these conditions. Choosing
a sufficiently small ∆rtime allows us to use the upper bound
of (6) as an estimate travel time for road arcs. Fifth, we
assume exogenous traffic not to be affected by the endogenous
AMoD vehicle routes. This assumption is also acceptable for
29. small values of ∆rtime, capturing the fact that vehicles follow
similar routes under similar traffic conditions. We leave the
game-theoretical extension accounting for reactive exogenous
traffic to future research work. Sixth, we allow AMoD vehicles
to transport one customer at a time. Such an assumption is
in line with current trends in mobility-on-demand systems,
such as taxis, Lyft and Uber. The extension to ride-sharing
AMoD requires the adoption of integer-valued flows and time-
expansions of the whole transportation network, resulting in
prohibitory trade-offs between computational times and model
accuracy [43]. Finally, for the sake of simplicity, we consider
customers to have identical preferences in terms of value of
time and travel comfort. However, the model proposed in
this paper can be readily extended to capture distinct classes
of customers, each characterized by a different network flow
associated with specific preferences.
III. A PRICING AND TOLLING SCHEME FOR I-AMOD
In Section II, we assume that the objectives of all
stakeholders are aligned with the global objective of maxi-
mizing social welfare. In reality, stakeholders are selfish, i.e.,
customers maximize their private welfare, whilst AMoD fleet
operators maximize their profits. In this section, we propose
a road tolling scheme to align the goals of self-interested
30. agents with the objective of maximizing social welfare (cf.
Section II-D). Section III-A formally introduces the self-
interested agents participating in the I-AMoD market, while
Section III-B details our road tolling scheme and Section III-C
proves its alignment with the social optimum.
A. Self-interested Agents
We model the I-AMoD market as a perfect market with
three types of agents: The municipal transportation authority,
I-AMoD customers, and AMoD operators. Assuming a perfect
I-AMoD market, neither individual customers nor AMoD
operators can unilaterally influence the transportation prices
which result from the market equilibrium [40].
The municipal transportation authority sets fares in the
subway system and road tolls in the road network aiming at
maximal social welfare. Prices in the public transportation net-
work are set to cover the operational cost of the transportation
system, whereas road tolls can be interpreted as congestion
surcharges. Specifically, the transportation authority sets a fare
pP(i, j) for each arc (i, j) ∈ AP in the public transportation
network and a toll τR(i, j) for each arc (i, j)∈ AR in the road
network.
31. I-AMoD customers serve their mobility requests m ∈ M
by selecting an intermodal route from their origins to their
destinations. From our mesoscopic perspective, route selection
consists of choosing a commodity flow fm (·,·) satisfying con-
tinuity as in Eq. (1a). We neglect common user-centric mod-
eling approaches that account for individual cost functions:
In line with current practice [44], we assume that customers
select their routes by using navigation apps which compute
routes by considering an aggregate model of the customers’
preferences. Specifically, we set a customer’s objective as
the maximization of her welfare, defined as the sum of the
travel time multiplied by the value of time VT and the cost
of her trip as the cumulative sum of the fares paid along the
route: pP(i, j) for each traversed arc (i, j) ∈ AP in the public
transportation network, as set by the municipal authority, and
pR(i, j) for each road arc (i, j)∈ AR traveled with an AMoD
vehicle. Considering a negligible quadratic regularization term
(cf. Eq. 14) for the sake of consistency, a customer’s I-AMoD
navigation app solves the following problem.
Problem 3 (I-AMoD Customers Optimization Problem).
Given a transportation request rm = (om,dm,αm) ∈ R,
I-AMoD customers’ routes result from
min
32. fm(·,·)
VT ·∑
(i, j)∈ A
ti j · fm (i, j)+VQ ·∑
(i, j)∈ A
fm ( j, j)
2
+ ∑
(i, j)∈ AR
pR(i, j)· fm (i, j)+ ∑
(i, j)∈ AP
pP(i, j)· fm (i, j)
s.t. Eq. (1a), Eq. (1c), (18)
The first term in the cost function corresponds to the cus-
tomer’s value of time, the third term denotes the arc-based
charge in the network, and the fourth term is the fare paid to
the subway network.
33. AMoD operators service customers and control the rebal-
ancing vehicles’ routes to ensure that vehicles are available
to service customer requests. Without loss of generality, we
fold the AMoD operators into a unique operator, paying tolls
τR(i, j) to the municipal authority and levying fares pR(i, j)
from the customers for each road arc traversed. As the AMoD
operator is unable to influence the AMoD prices pR(i, j) in a
perfect market, the goal of maximizing revenue is equivalent to
7
the goal of minimizing operating expenses. Again, we include
a negligible quadratic regularization term to ensure strict
convexity such that the AMoD operator solves the following
problem.
Problem 4 (AMoD Operator Optimization Problem). Given
the customer flows { fm (·,·)}m, the optimal AMoD rebalancing
flows f0 (·,·) result from
min
f0(·,·)
34. ∑
(i, j)∈ AR
(VD,R ·si j +VE ·ei j + τR(i, j))· f0 (i, j)+VQ · f0 (i, j)
2
s.t. Eq. (1b), Eq. (1c). (19)
Analogous to Section II-D, we reformulate Problems 3 and
4 in matrix notation. Let [c̃R]a := VT ·ti j + pR(i, j) and [c̄ 0]a
:=
VD,R ·si j +VE ·ei j +τR(i, j)∀(i, j)∀AR, [c̃W]a := VT ·ti j ∀ (i,
j)∈
AW, [c̃P]a := VT · ti j ∀(i, j) ∀ AP, and [c̃C]a := VT · ti j ∀ (i,
j) ∈
AC.
Problem 5 (I-AMoD Customers Optimization Problem Revis-
ited). Given a transportation request rm = (om,dm,αm) ∈ R,
I-AMoD a customer’s route results from
min
xR,m,xW,m,
xP,m,xC,m
36. where the linear terms in (20a) capture travelling cost, the
quadratic terms comprise the regularization, and the matrix
equality constraint (20b) ensures flow conservation.
Problem 6 (AMoD Operator Optimization Problem Revis-
ited). Given the customer flows {xm}m, the optimal AMoD
rebalancing flows result from
min
x0
VQ ·x>0 x0 + c̄
>
0 x0 (21a)
s.t. BR
(
∑
m∈ M
xR,m + x0
)
37. = 0 (21b)
x0 ≥ 0. (21c)
While the linear term in the (21a) represents travelling cost,
the quadratic term denotes the regularization, and (21b)
enforces vehicle conservation.
Lemma III.1. Problem 3 and Problem 5 are equivalent.
Lemma III.2. Problem 4 and Problem 6 are equivalent.
The proofs of Lemmas 5 and 6 are identical to the proofs
of Lemma II.2.
Lemma III.3. Problem 3 is feasible and has a unique solution.
Proof. Let
{
{ fm (·,·)}?m, f0 (·,·)
?
}
be a guaranteed feasible
38. solution to Problem 1 (cf. Lemma II.3). Then, fm (·,·)? is
a feasible, yet suboptimal, solution to Problem 3. Hence,
Problem 3 is feasible. Uniqueness follows analogously to
Lemma II.3 .
Lemma III.4. Problem 4 is feasible and has a unique solution.
Proof. Since there are no capacity constraints, the proof
follows analogously to Lemma II.3 and III.3.
B. A Pricing and Tolling Scheme
The subway fares pP(i, j) and the road tolls τR(i, j) are
control variables that the welfare-minded municipal authority
can adjust to steer self-interested customers and the AMoD
operator towards maximizing social welfare as defined in
Section II-D.
We denote the dual multipliers associated with the road
capacity constraint (17d) as µcR, and the dual multipliers
associated with the vehicle balance constraints (17b) as λR. To
ease the notation we let µcR(i, j) := [µcR]a and λR(i) := [λR]i.
We propose the following pricing and tolling scheme: The
subway fares are set equal to the the public transit operational
39. cost as
pP(i, j) = VD,P ·si j, (22)
whilst the road tolls are chosen equal to the road congestion
multipliers as
τR(i, j) = µcR(i, j). (23)
C. A General Equilibrium
Given the market presented in Section III-A, we can define
its general economic equilibrium as follows.
Definition III.5 (General Economic Equilibrium). A
solution
{
{ fm (·,·)}?m, f0 (·,·)
?
}
and a set of prices
{pP(i, j),τR(i, j), pR(i, j)} form a general economic
40. equilibrium if and only if 1) fm (·,·)? is a solution to
Problem 3 for all m ∈ M , 2) f0 (·,·)
? is a solution to Problem
4, and 3) the economic profit of each AMoD operator is zero
(that is, the operator’s revenue equals its costs).
Remark III.6. The requirement that, at equilibrium, the
economic profit of each AMoD operator equals the costs
is characteristic of perfect markets, where sellers have no
economic surplus [40]. The condition does not imply that
AMoD operators receive no profit whatsoever; indeed, the cost
VD,R captures the operators’ normal profits, which compensate
them for their risk and opportunity cost. This condition has an
intuitive interpretation. At equilibrium, no operator can lower
its prices, since it would then be better off leaving the market;
at the same time, no operator can increase its prices, since all
customers would prefer to be served by a cheaper competitor.
Assume the AMoD road prices pR(i, j) equal to the sum of
the vehicles’ operating costs, the road tolls, and the origin and
destination prices as
pR(i, j) = VD,R ·si j +VE ·ei j + τR(i, j)+ λR(i)−λR( j). (24)
41. Remark III.7. The AMoD origin-destination prices
∑(i, j)∈ AR (λR(i)− λR( j)) fm (i, j) for a request r = (o,d,α)
simplify to λR(o) − λR(d), capturing the marginal cost
incurred to rebalance the system due to the request.
The following theorem shows that the pricing and tolling
scheme proposed in Section III-B ensures that an optimal
solution to the I-AMoD Problem 1 coincides with a general
economic equilibrium for the market.
Theorem III.8 (Optimal Pricing and Tolling Scheme). Con-
sider the optimal solution
{
{ fm (·,·)}?m, f0 (·,·)
?
}
to the
8
42. I-AMoD problem. Also, consider a perfect market where self-
interested customers plan their routes with a navigation app
solving Problem 3, a self-interested AMoD operator plans
rebalancing routes by solving Problem 4, and the munic-
ipal transportation authority sets public transit prices and
road tolls according to (22)–(23). Then, the optimal solution{
{ fm (·,·)}?m, f0 (·,·)
?
}
and the prices (24) are a general
economic equilibrium for the I-AMoD market; that is:
1) fm (·,·)? is an optimal solution to Problem 3;
2) f0 (·,·)
? is an optimal solution to Problem 4;
3) the AMoD operators’s revenue equals its costs (up to
the regularization term).
Proof Sketch. The proof relies on showing that satisfaction
of the KKT conditions for the I-AMoD Problem 2 implies
satisfaction of the KKT conditions for the customers’ optimal
43. routing Problem 5 and the KKT conditions for the AMoD op-
erator’s optimal rebalancing Problem 6. We provide a rigorous
proof in Appendix B.
D. Discussion
A few comments are in order. First, in the setting of a
general equilibrium, we assume that the AMoD operators have
no pricing power, i.e., no individual AMoD operator is able to
single-handedly influence the customers’ fares. This assump-
tion holds if multiple operators of similar size compete for
customers’ transportation demands, and is arguably realistic in
several urban environments. For reference, no fewer than five
app-based mobility-on-demand operators (Uber, Lyft, Juno,
Curb and Arro) currently offer mobility-on-demand services
in Manhattan. Second, in this paper, the operations of all
AMoD operators are captured through a single rebalancing
flow and a single set of customer-carrying flows on road arcs
for simplicity and ease of notation. However, the model does
not assume that a single AMoD operator is present. Indeed,
a treatment where different operators control different subsets
of vehicles, each associated with a rebalancing flow, would
result in the same equilibrium. Note that customers, not AMoD
operators, choose the operator by selecting the customer-
carrying flows { fm (·,·)}?m, and the operators do not compete
44. on prices. Therefore, Theorem III.8 still holds as the operation
of single AMoD operators are not coupled, and Problem 4
can be decomposed into subproblems, one for each operator.
Third, we assume that the routes followed by customer-
carrying AMoD vehicles are set by the customers themselves
through the navigation apps. In practical implementations, the
customers may be able to choose only among a limited set of
possible routes, for example between a direct route that incurs
congestion tolls and a longer, less congested and thus cheaper
route. Such more sophisticated route selection models are left
for future research. Fourth, we use the cost function (18) to
model customers’ behaviour. Although such an approach does
not entail the level of detail of a user-centric approach [30,
Ch. 4], it suffices for the mesoscopic perspective of this study.
Finally, Theorem III.8 shows that the socially optimal solution
can be achieved as a general economic equilibrium. However,
it does not prove that the flows
{
{ fm (·,·)}?m, f0 (·,·)
?
}
and the
45. prices {pP(i, j),τR(i, j), pR(i, j)} are the only equilibrium for
TABLE I
REQUESTS IN BERLIN AND NYC.
NYC Berlin
M 8,658 2,646
∑m∈ M αm 44.943 1/s 3.771 1/s
∑m∈ M αm‖om −dm‖2/∑m∈ M αm 2.4 km 4.0 km
Fig. 3. Measure of the “betweenness” centrality for each node in
the road
digraphs of Berlin (left) and Manhattan (right). The broad and
well-connected
structure of Berlin is reflected in several nodes with a high
degree of centrality,
whereas the elongated shape of Manhattan results in less central
nodes.
the system; indeed, other equilibria may exist that result in
higher societal cost compared to the socially optimal solution.
To overcome this, we envision that the market could be steered
towards the socially optimal equilibrium through the intro-
duction of a non-profit market-making entity whose role is to
46. match supply with demand while steering the system towards
the socially-optimal equilibrium, akin to the role of non-profit
Independent System Operators in electricity markets.
IV. RESULTS
In this section, we assess the benefits of an I-AMoD system
in terms of travel time, costs, and emissions for real-world case
studies of NYC and Berlin. Section IV-A details these case
studies before we present the optimal solution for the I-AMoD
system in Section IV-B. Additionally, we study the change
in global cost for different vehicle architectures. Finally, we
compare the optimal solution for the I-AMoD system and the
AMoD system operating in isolation in Section IV-C.
A. Case Study
We focus on two distinct metropolitan areas, namely, the
Manhattan peninsula in NYC, NY, and the city center of
Berlin, Germany, as they are complementary in the following
parameters: First, the cities differ in the spatial structure of its
road system. Fig. 3 shows the “betweenness” centrality of road
nodes computed as the probability that a shortest path between
two random nodes will traverse the given node. The center of
Berlin covers a broad region with several nodes that show a
47. high centrality, whereas Manhattan has less homogeneously
distributed central nodes and a more elongated and thinner
9
Fig. 4. The public transit network of Berlin (left) and NYC
(right).
urban shape. Second, although their surface is nearly equal
in size, the amount of trip requests in Manhattan exceeds the
amount of trip requests in Berlin by one order of magnitude
(cf. Table I). Finally, the geodesic distance between origin-
destination-pairs in Berlin is on average twice as long as in
NYC.
We derive transportation requests as follows: For Manhattan,
we consider the actual 53,932 taxi rides which took place on
March 1, 2012 between 6 PM and 8 PM (courtesy of the New
York Taxi and Limousine Commission). Although this number
of trips is quite large, it represents only a fraction of the travel
demand: In 2017 the number of ride-sharing vehicles used in
this time period outnumbered yellow cabs by a factor of 5 [45].
Hence, we increase the number of requests by a factor of six
48. to emulate the total demand for ride-hailing services during
this time window, obtaining a total of 8,658 origin-destination
pairs. For Berlin, we use data from the MATSim [46] Berlin
case study [47]. In order to provide a fair comparison, we
scale the demand in order to match the demand of Manhattan
in terms of requests pro capite. For both cities we derive the
road network from OpenStreetMap data [48], and define the
capacity of each street to be proportional to the number of
lanes multiplied by the road’s speed limit [13]. To account
for exogenous vehicles on the road, we perform a parametric
study, varying the scaling factor of the exogenous road usage
uR between 50% and 200% of the nominal road capacity cR.
We set the maximum increase in travel time caused by AMoD
vehicles ∆rtime to 5%. We assume the subway network to be
the only public transportation system in Manhattan, in line
with the fact that the subway network is the dominant public
transit mode of the city, whereas in Berlin we also include
the S-Bahn and the tram lines (Fig. 4). This way, we provide
a first order assessment of I-AMoD. We construct the public
transportation digraph using the geographical location of the
lines and the stops found in the NYC Open Data database [49]
as well as the time schedules of the MTA [50], whereas for
Berlin we directly use GTFS data [51]. We set the time to
transfer from a road node or a subway stop to a walking node,
which models the time required to exit an AMoD vehicle or
49. a subway station, to one minute. We assume that 90 seconds
are required to go from a pedestrian to a road node and get
TABLE II
NUMERICAL DATA FOR THE CASE STUDIES
Parameter Variable Value Source
Maximum time increase ∆rtime 5 %
Value of time VT 24.40 USD/h [55]
Vehicle operational cost VD,R 0.48 USD/mile [56]
NYC Berlin
Subway operational cost VD,P 0.47 USD/mile 0.30 USD/mile
[57], [58]
Cost of electricity VE 0.25 USD/kWh 0.33 USD/kWh [59], [60]
Cost of gasoline VE 0.07 USD/kWh 0.18 USD/kWh
Air density ρair 1.25 kg/m3
Final drive efficiency ηfd 98 % [37]
Motor efficiency ηm/g 90 % [37]
Battery efficiency ηdis/chg 90 % [37]
Engine efficiency ηe 40 % [37]
LWV SUV
50. Engine drag Pe,0 1.6 kW 4 kW [37]
Mass of the vehicle mv 750 kg 2000 kg [37]
Rolling friction coefficient cr 0.008 0.017 [37]
Frontal drag coefficient cd ·Af 0.4 m2 1.2 m2 [37]
into an AMoD vehicle, which is in line with the average
time to hail a ride in Manhattan [52]. The time to transfer
from a walking node to a subway line equals one minute
plus one half of the frequency of the line. We directly relate
the energy consumption to the CO2 emissions based on the
current electricity sources of the state of New York [53] and
Germany [54], whereas we consider molar mass ratio between
CO2 and CH2 for gasoline [39]. We compute the energy
consumption as in Section II-C for the different vehicles
studied, namely, a Lightweight (LW) and a Sport Utility (SU)
vehicle. Table II summarizes the remaining parameters used
in our case studies and their bibliographic sources.
For each of the scenarios presented in the next Sections IV-B
and IV-C, the quadratic optimization Problem 1 was solved
on commodity hardware (Intel Core i7, 16 GB RAM) using
Gurobi 8.1 in less than 5 minutes.
B. Optimal