Updates provided to the D-STOP Business Advisory Council at the 2017 Symposium and Board Meeting: https://ctr.utexas.edu/2018/04/12/d-stop-2017-symposium-archive/
The document summarizes an optimization program that airlines can use to determine the right freight capacity, operating frequency, and fleet positioning to minimize costs and maximize profits. The program takes in data on routes, yields, demands, and costs. It then runs integer programming models and U-curve techniques to find the optimum solution. A case study on Yemenia airline shows how the program can determine the best aircraft types for its network and maximize profits on a multi-stop route from Sana'a to Singapore.
Route optimization algorithm are the mathematical formula that solve routing problems..
Some types of routing:
1) Vehicle Routing Problem (VRP)
2) Traveling Salesman Problem (TSP)
3) Ant Colony Optimization (ACO)
The document discusses various formulations of the Vehicle Routing Problem with Backhauls (VRPB). It begins by providing background on the VRPB and its history. It then describes several common variants of the VRPB that have been studied in literature, including the Vehicle Routing Problem with Backhauls (VRPB), Mixed Vehicle Routing Problem with Backhauls (MVRPB), Multiple Depot Mixed Vehicle Routing Problem with Backhauls (MDMVRPB), Vehicle Routing Problem with Backhauls and Time Windows (VRPBTW), and others. For each variant, the document outlines key characteristics and constraints and references relevant literature and studies.
This document discusses vehicle routing and scheduling models and algorithms. It introduces basic models like the Traveling Salesman Problem (TSP), Vehicle Routing Problem (VRP), and Pickup and Delivery Problem with Time Windows (PDPTW). Construction heuristics like savings, insertion, and set covering algorithms are presented to find initial feasible solutions that can then be improved using local search methods. The document outlines practical considerations and recent variants like dynamic and stochastic routing problems.
Replacing Manhattan Subway Service with On-demand transportationChristian Moscardi
1) The document proposes replacing subway service in Manhattan with on-demand ridesharing during overnight repair periods to reduce costs.
2) A simulation would be used to model routing on-demand vehicles to service subway trips between 12AM-5AM using demand data and routing algorithms.
3) Key metrics like vehicle needs, passenger wait times, and repair costs vs transportation costs would be compared to evaluate the alternative. The simulation aims to answer if on-demand ridesharing can adequately replace subway service during repairs.
Presentation by Dr James Tate at Institute of Air Quality Management (IAQM) Dispersion Modellers User Group December 2014.
www.its.leeds.ac.uk/people/j.tate
http://iaqm.co.uk/event/dmug-2014/
The document provides an overview of the Greater Brisbane Transport Demand Model (BNE Model). It summarizes the model's key components, including demand segments, demography, networks, volume delay functions, parking and toll models, and public transport modeling. It also describes the model's calibration process and convergence criteria. The model uses a discrete choice approach and is the first of its kind for an Australian transport demand model.
The document summarizes an optimization program that airlines can use to determine the right freight capacity, operating frequency, and fleet positioning to minimize costs and maximize profits. The program takes in data on routes, yields, demands, and costs. It then runs integer programming models and U-curve techniques to find the optimum solution. A case study on Yemenia airline shows how the program can determine the best aircraft types for its network and maximize profits on a multi-stop route from Sana'a to Singapore.
Route optimization algorithm are the mathematical formula that solve routing problems..
Some types of routing:
1) Vehicle Routing Problem (VRP)
2) Traveling Salesman Problem (TSP)
3) Ant Colony Optimization (ACO)
The document discusses various formulations of the Vehicle Routing Problem with Backhauls (VRPB). It begins by providing background on the VRPB and its history. It then describes several common variants of the VRPB that have been studied in literature, including the Vehicle Routing Problem with Backhauls (VRPB), Mixed Vehicle Routing Problem with Backhauls (MVRPB), Multiple Depot Mixed Vehicle Routing Problem with Backhauls (MDMVRPB), Vehicle Routing Problem with Backhauls and Time Windows (VRPBTW), and others. For each variant, the document outlines key characteristics and constraints and references relevant literature and studies.
This document discusses vehicle routing and scheduling models and algorithms. It introduces basic models like the Traveling Salesman Problem (TSP), Vehicle Routing Problem (VRP), and Pickup and Delivery Problem with Time Windows (PDPTW). Construction heuristics like savings, insertion, and set covering algorithms are presented to find initial feasible solutions that can then be improved using local search methods. The document outlines practical considerations and recent variants like dynamic and stochastic routing problems.
Replacing Manhattan Subway Service with On-demand transportationChristian Moscardi
1) The document proposes replacing subway service in Manhattan with on-demand ridesharing during overnight repair periods to reduce costs.
2) A simulation would be used to model routing on-demand vehicles to service subway trips between 12AM-5AM using demand data and routing algorithms.
3) Key metrics like vehicle needs, passenger wait times, and repair costs vs transportation costs would be compared to evaluate the alternative. The simulation aims to answer if on-demand ridesharing can adequately replace subway service during repairs.
Presentation by Dr James Tate at Institute of Air Quality Management (IAQM) Dispersion Modellers User Group December 2014.
www.its.leeds.ac.uk/people/j.tate
http://iaqm.co.uk/event/dmug-2014/
The document provides an overview of the Greater Brisbane Transport Demand Model (BNE Model). It summarizes the model's key components, including demand segments, demography, networks, volume delay functions, parking and toll models, and public transport modeling. It also describes the model's calibration process and convergence criteria. The model uses a discrete choice approach and is the first of its kind for an Australian transport demand model.
Multiobjective load flow problem by whale optimizationRohit vijay
The document discusses using the whale optimization algorithm to solve the multiobjective load flow problem. The multiobjective load flow problem aims to minimize generating cost, transmission losses, and power plant emissions while satisfying operational constraints. The whale optimization algorithm is inspired by humpback whales' bubble-net feeding strategy and is used to find optimal solutions to the non-linear, constrained multiobjective load flow problem. The algorithm updates potential solutions based on either the best solution found so far or a randomly selected potential solution to balance exploration and exploitation in finding optimal results.
This document discusses facility location decisions and methods for analyzing location strategies. It begins with an overview of what can be located, such as plants, warehouses, retail outlets, and key questions to consider around location. Common methods for solving single and multiple facility location problems are then presented, including the center-of-gravity (COG) method and optimization approaches. The document concludes with examples of applying COG and discussing other techniques like simulation and weighted checklists for analyzing retail location decisions.
Energy Service Demands projections in transport sector for SSPsIEA-ETSAP
The document summarizes a study on generating energy service demand projections for the transport sector. It presents a neural network stacking algorithm called Trebuchet that was developed to improve upon linear regression models. Test results on different transport modes like aviation, rail and road show the Trebuchet method significantly reduces error compared to regression, with improvements ranging from 67-97%. Freight and passenger projections generated for different socioeconomic pathways are also presented. The study demonstrates the Trebuchet method performs better than basic neural networks and can capture non-linear behavior.
This document discusses new features that will enhance the Galileo satellite navigation system, including navigation message authentication (OS-NMA) and a free high accuracy service. OS-NMA will help authenticate navigation data and protect against spoofing attacks. The high accuracy service will provide decimeter-level positioning accuracy without additional ground communications. The document also outlines upcoming tests of these features and an initiative to fund development of user terminals that can take advantage of them.
Presented at the 2014 ICCVE 3-7 Nov 2014, Vienna, Austria
S3 summit session:
Satellite navigation and positioning in a connected / automated vehicle environment
The document provides an update on the status of Galileo in May 2018. It discusses Galileo deployment progress including satellites launched and planned launches. It summarizes the Galileo downstream value chain and efforts to stimulate user adoption. Open service performance is outlined achievement of targets for ranging accuracy, availability, timing and synchronization. Dual frequency capability is highlighted as important for applications requiring higher precision. The document outlines services including navigation message authentication, high accuracy, and search and rescue. It discusses the evolution of Galileo including enhanced message design and new potential services.
This document discusses multi-modal journey planning and describes a proposed solution approach. It summarizes the multi-modal journey planning problem, characteristics, previous work, and proposes a hybrid approach using a mathematical programming model combined with heuristic methods like Dijkstra's algorithm. The approach involves using the programming model to solve the multi-modal journey planning problem after applying Dijkstra's algorithm and graph techniques to pre-process the data.
Investigating Time-of-Use as a Factor in Dynamic Wireless Charging Infrastruc...Joseph Chow
1) The document investigates modeling optimal locations for dynamic wireless charging infrastructure for electric buses by accounting for time-of-use factors like varying energy costs and vehicle speeds throughout the day.
2) Previous models for single and multi-route infrastructure planning are reviewed and a new time-of-use model is proposed to determine if the optimal solution changes with daily energy and traffic patterns.
3) The model is validated using real-world New York City bus network and energy cost data, finding the solutions are nearly identical with and without accounting for time-of-use, likely due to limitations in the model and data simplifications.
Swisscom Mobile deployed AIRCOM's ADVANTAGE automated cell planning tool and Wavecall's WaveSight propagation module to optimize its 3G network coverage in urban areas like Basel and Zurich. This allowed engineers to accurately predict coverage and maximize network capacity while maintaining quality of service. Initial results showed an 8% improvement in Basel's network quality and over 3% in Zurich's larger network, without requiring additional capital expenditure. The combined solution delivered significant operational and capital expenditure savings for Swisscom Mobile.
1. Estimate the Rain Fade for earth-to-satellite microwave Down links for the following frequency bands (LP-V, LP-H, CP):
a. C-band (4 GHz)
b. Ku-band (12 GHz)
c. Ka-band (20 GHz)
d. V-band (30 GHz)
2. Make a table and compare the estimated rain fades for above four bands with three different polarizations.
3. Design and estimate the downlink budget for the above frequency bands by highlighting the following two parameters:
C/N ratio during clear air
C/N ratio during rain
4. Predict the BER for QPSK modulation and above environmental conditions.
Multi depot Time-dependent Vehicle Routing Problem with Heterogeneous FleetArian Razmi Farooji
The document summarizes a study comparing NSGA II and MOSA algorithms for solving a multi-depot vehicle routing problem with time-dependent travel times and a heterogeneous fleet. The problem involves routing vehicles from multiple depots to serve customers within time windows while minimizing costs and number of routes. NSGA II and MOSA were tested on randomly generated small, medium, and large problems. Results showed that on average, MOSA performed better than the model on small problems, while NSGA II performed comparably to the model.
In this presentation, a new routing model was introduced in the form of integer linear programming by combining the concepts of time windows and multiple demands and by considering the two contradictory goals of minimizing travel: cost and maximizing demand coverage.
The document discusses vehicle routing problems and algorithms for solving them. It defines the vehicle routing problem, describes several variants including the traveling salesman problem and vehicle routing problem with time windows. It also outlines common route generation algorithms like savings, nearest neighbor and discusses how to evaluate solution quality based on vehicle utilization and load factors. An example problem is presented demonstrating the application of nearest neighbor and savings algorithms to find routes for a fleet of vehicles servicing customer demands from a depot.
The document discusses vehicle routing problems and algorithms. It defines the vehicle routing problem, describes common variants like the traveling salesman problem and vehicle routing problem with time windows. It also covers routing algorithms like nearest neighbor and Clarke and Wright savings heuristic. An example is provided to illustrate key concepts like computing an initial savings matrix and updating it during the serial savings algorithm.
The document discusses vehicle routing problems and algorithms for solving them. It defines the vehicle routing problem, describes several variants including the traveling salesman problem and vehicle routing problem with time windows. It also outlines common route generation algorithms like savings, nearest neighbor and discusses how to evaluate solution quality based on vehicle utilization and load factors. An example problem is presented demonstrating the application of nearest neighbor and savings algorithms to find routes for a fleet of vehicles.
SCS Global Certification of CTC Global ACCC ConductorDave Bryant
CTC Global's ACCC Conductor was certified by SCS Global for its ability to reduce transmission line losses and associated greenhouse gas emissions including CO2 to help mitigate global warming and climate change
The document discusses long range kinematic (LRK) positioning techniques for marine surveying applications. It describes where LRK is used, the key components which include a rover, base, data exchange, and communication. It also discusses trends toward using additional GNSS signals like GLONASS to improve accuracy over long distances. Examples are given of LRK being used for hydrographic surveys over hundreds of kilometers and dredging projects in China over 100 km.
The Future of Mixed-Autonomy Traffic (AIS302) - AWS re:Invent 2018Amazon Web Services
How will self-driving cars change urban mobility patterns? This talk examines scientific contributions in the field of reinforcement learning, presented in the context of enabling mixed-autonomy mobility—the gradual and complex integration of autonomous vehicles into existing traffic systems. We explore the potential impact of a small fraction of autonomous vehicles on low-level traffic flow dynamics, using novel techniques in model-free deep reinforcement learning. We share examples in the context of a new open-source computational platform and state-of-the-art microsimulation tools with deep-reinforcement libraries.
This document summarizes a presentation about planning for a world with connected and automated vehicles. Some key points include:
- Automated vehicles will have major impacts on traffic such as increased road capacity through platooning and new traffic control strategies.
- Models are needed to simulate these impacts at large regional scales given technological and regulatory uncertainties.
- Reservation-based intersections show potential to dramatically reduce delays, but their impacts depend on factors like route choice and asymmetric demand.
- Allowing empty automated vehicles to reposition could smooth traffic flows compared to not repositioning empty vehicles.
"Using step-by-step Bayesian updating to better estimate the reinforcement lo...TRUSS ITN
Probabilistic assessment of ageing structures has become an important research area as it attracts the interest from not only researchers but also investors, municipalities, and governments. The most commonly used material for many important structures and infrastructure is reinforced concrete. Various degradations of such structures are manifest in the form of direct loss of reinforcement area. In this study, a time-dependent stochastic model of the reinforcement loss (in [%]) due to corrosion is presented, which has a crucial role in the estimation of the lifetime and the time-dependent health state of the structure. Bayesian updating is applied in multiple steps during the lifetime of the structure in order to improve the estimate of the reinforcement loss. An example application is shown where updating is applied in two steps.
Simulation Based Assignment in PTV Visum - TRB 2017Michael Oliver
A brief introduction to the new Simulation-Based dynamic Assignment (SBA) released in PTV Visum 16, as presented at the Virginia and Washington DC Joint SimCap Meeting, TRB 2017.
Multiobjective load flow problem by whale optimizationRohit vijay
The document discusses using the whale optimization algorithm to solve the multiobjective load flow problem. The multiobjective load flow problem aims to minimize generating cost, transmission losses, and power plant emissions while satisfying operational constraints. The whale optimization algorithm is inspired by humpback whales' bubble-net feeding strategy and is used to find optimal solutions to the non-linear, constrained multiobjective load flow problem. The algorithm updates potential solutions based on either the best solution found so far or a randomly selected potential solution to balance exploration and exploitation in finding optimal results.
This document discusses facility location decisions and methods for analyzing location strategies. It begins with an overview of what can be located, such as plants, warehouses, retail outlets, and key questions to consider around location. Common methods for solving single and multiple facility location problems are then presented, including the center-of-gravity (COG) method and optimization approaches. The document concludes with examples of applying COG and discussing other techniques like simulation and weighted checklists for analyzing retail location decisions.
Energy Service Demands projections in transport sector for SSPsIEA-ETSAP
The document summarizes a study on generating energy service demand projections for the transport sector. It presents a neural network stacking algorithm called Trebuchet that was developed to improve upon linear regression models. Test results on different transport modes like aviation, rail and road show the Trebuchet method significantly reduces error compared to regression, with improvements ranging from 67-97%. Freight and passenger projections generated for different socioeconomic pathways are also presented. The study demonstrates the Trebuchet method performs better than basic neural networks and can capture non-linear behavior.
This document discusses new features that will enhance the Galileo satellite navigation system, including navigation message authentication (OS-NMA) and a free high accuracy service. OS-NMA will help authenticate navigation data and protect against spoofing attacks. The high accuracy service will provide decimeter-level positioning accuracy without additional ground communications. The document also outlines upcoming tests of these features and an initiative to fund development of user terminals that can take advantage of them.
Presented at the 2014 ICCVE 3-7 Nov 2014, Vienna, Austria
S3 summit session:
Satellite navigation and positioning in a connected / automated vehicle environment
The document provides an update on the status of Galileo in May 2018. It discusses Galileo deployment progress including satellites launched and planned launches. It summarizes the Galileo downstream value chain and efforts to stimulate user adoption. Open service performance is outlined achievement of targets for ranging accuracy, availability, timing and synchronization. Dual frequency capability is highlighted as important for applications requiring higher precision. The document outlines services including navigation message authentication, high accuracy, and search and rescue. It discusses the evolution of Galileo including enhanced message design and new potential services.
This document discusses multi-modal journey planning and describes a proposed solution approach. It summarizes the multi-modal journey planning problem, characteristics, previous work, and proposes a hybrid approach using a mathematical programming model combined with heuristic methods like Dijkstra's algorithm. The approach involves using the programming model to solve the multi-modal journey planning problem after applying Dijkstra's algorithm and graph techniques to pre-process the data.
Investigating Time-of-Use as a Factor in Dynamic Wireless Charging Infrastruc...Joseph Chow
1) The document investigates modeling optimal locations for dynamic wireless charging infrastructure for electric buses by accounting for time-of-use factors like varying energy costs and vehicle speeds throughout the day.
2) Previous models for single and multi-route infrastructure planning are reviewed and a new time-of-use model is proposed to determine if the optimal solution changes with daily energy and traffic patterns.
3) The model is validated using real-world New York City bus network and energy cost data, finding the solutions are nearly identical with and without accounting for time-of-use, likely due to limitations in the model and data simplifications.
Swisscom Mobile deployed AIRCOM's ADVANTAGE automated cell planning tool and Wavecall's WaveSight propagation module to optimize its 3G network coverage in urban areas like Basel and Zurich. This allowed engineers to accurately predict coverage and maximize network capacity while maintaining quality of service. Initial results showed an 8% improvement in Basel's network quality and over 3% in Zurich's larger network, without requiring additional capital expenditure. The combined solution delivered significant operational and capital expenditure savings for Swisscom Mobile.
1. Estimate the Rain Fade for earth-to-satellite microwave Down links for the following frequency bands (LP-V, LP-H, CP):
a. C-band (4 GHz)
b. Ku-band (12 GHz)
c. Ka-band (20 GHz)
d. V-band (30 GHz)
2. Make a table and compare the estimated rain fades for above four bands with three different polarizations.
3. Design and estimate the downlink budget for the above frequency bands by highlighting the following two parameters:
C/N ratio during clear air
C/N ratio during rain
4. Predict the BER for QPSK modulation and above environmental conditions.
Multi depot Time-dependent Vehicle Routing Problem with Heterogeneous FleetArian Razmi Farooji
The document summarizes a study comparing NSGA II and MOSA algorithms for solving a multi-depot vehicle routing problem with time-dependent travel times and a heterogeneous fleet. The problem involves routing vehicles from multiple depots to serve customers within time windows while minimizing costs and number of routes. NSGA II and MOSA were tested on randomly generated small, medium, and large problems. Results showed that on average, MOSA performed better than the model on small problems, while NSGA II performed comparably to the model.
In this presentation, a new routing model was introduced in the form of integer linear programming by combining the concepts of time windows and multiple demands and by considering the two contradictory goals of minimizing travel: cost and maximizing demand coverage.
The document discusses vehicle routing problems and algorithms for solving them. It defines the vehicle routing problem, describes several variants including the traveling salesman problem and vehicle routing problem with time windows. It also outlines common route generation algorithms like savings, nearest neighbor and discusses how to evaluate solution quality based on vehicle utilization and load factors. An example problem is presented demonstrating the application of nearest neighbor and savings algorithms to find routes for a fleet of vehicles servicing customer demands from a depot.
The document discusses vehicle routing problems and algorithms. It defines the vehicle routing problem, describes common variants like the traveling salesman problem and vehicle routing problem with time windows. It also covers routing algorithms like nearest neighbor and Clarke and Wright savings heuristic. An example is provided to illustrate key concepts like computing an initial savings matrix and updating it during the serial savings algorithm.
The document discusses vehicle routing problems and algorithms for solving them. It defines the vehicle routing problem, describes several variants including the traveling salesman problem and vehicle routing problem with time windows. It also outlines common route generation algorithms like savings, nearest neighbor and discusses how to evaluate solution quality based on vehicle utilization and load factors. An example problem is presented demonstrating the application of nearest neighbor and savings algorithms to find routes for a fleet of vehicles.
SCS Global Certification of CTC Global ACCC ConductorDave Bryant
CTC Global's ACCC Conductor was certified by SCS Global for its ability to reduce transmission line losses and associated greenhouse gas emissions including CO2 to help mitigate global warming and climate change
The document discusses long range kinematic (LRK) positioning techniques for marine surveying applications. It describes where LRK is used, the key components which include a rover, base, data exchange, and communication. It also discusses trends toward using additional GNSS signals like GLONASS to improve accuracy over long distances. Examples are given of LRK being used for hydrographic surveys over hundreds of kilometers and dredging projects in China over 100 km.
The Future of Mixed-Autonomy Traffic (AIS302) - AWS re:Invent 2018Amazon Web Services
How will self-driving cars change urban mobility patterns? This talk examines scientific contributions in the field of reinforcement learning, presented in the context of enabling mixed-autonomy mobility—the gradual and complex integration of autonomous vehicles into existing traffic systems. We explore the potential impact of a small fraction of autonomous vehicles on low-level traffic flow dynamics, using novel techniques in model-free deep reinforcement learning. We share examples in the context of a new open-source computational platform and state-of-the-art microsimulation tools with deep-reinforcement libraries.
This document summarizes a presentation about planning for a world with connected and automated vehicles. Some key points include:
- Automated vehicles will have major impacts on traffic such as increased road capacity through platooning and new traffic control strategies.
- Models are needed to simulate these impacts at large regional scales given technological and regulatory uncertainties.
- Reservation-based intersections show potential to dramatically reduce delays, but their impacts depend on factors like route choice and asymmetric demand.
- Allowing empty automated vehicles to reposition could smooth traffic flows compared to not repositioning empty vehicles.
"Using step-by-step Bayesian updating to better estimate the reinforcement lo...TRUSS ITN
Probabilistic assessment of ageing structures has become an important research area as it attracts the interest from not only researchers but also investors, municipalities, and governments. The most commonly used material for many important structures and infrastructure is reinforced concrete. Various degradations of such structures are manifest in the form of direct loss of reinforcement area. In this study, a time-dependent stochastic model of the reinforcement loss (in [%]) due to corrosion is presented, which has a crucial role in the estimation of the lifetime and the time-dependent health state of the structure. Bayesian updating is applied in multiple steps during the lifetime of the structure in order to improve the estimate of the reinforcement loss. An example application is shown where updating is applied in two steps.
Simulation Based Assignment in PTV Visum - TRB 2017Michael Oliver
A brief introduction to the new Simulation-Based dynamic Assignment (SBA) released in PTV Visum 16, as presented at the Virginia and Washington DC Joint SimCap Meeting, TRB 2017.
The lecture outline discusses network models and network flow problems. It introduces key concepts like the maximum flow problem and minimum cost flow problem. It provides examples of solving the maximum flow problem using the Ford-Fulkerson method and concepts like residual networks and augmenting paths. The document also provides a sample problem solving the maximum flow problem on a network transporting water.
BOA analyzes test data from vehicle prototypes to optimize exhaust flex-joint designs. Previously, BOA used multiple software packages which slowed analysis. BOA now uses nCode GlyphWorks and DesignLife, allowing full analysis within a single environment. On-site testing and analysis identifies design issues quickly. GlyphWorks automates data conversion and processing, enabling real-time decisions. This reduces risks and helps accelerate product development.
Inaugural Professorial lecture by Simon Shepherd, Professor of Choice Modelling & Policy Design. Institute for Transport Studies, University of Leeds, 9th September 2014.
For audio recording see: www.its.leeds.ac.uk/about/events/inaugural-lectures2014
www.its.leeds.ac.uk/people/s.shepherd
www.its.leeds.ac.uk/research/themes/dynamicmodelling
In deze lezing worden recent afgeronde TRAIL proefschriften besproken, met focus op de relevantie voor de praktijk. We bespreken recente ontwikkeling in verkeersmanagement en coöperatieve systemen, crowd- en evacuatiemanagement en transport security. We bespreken ook kort de verschuiving van de focus binnen de leerstoel Traffic Operations and Management.
The document discusses an investigation into resolving radiated emission (RE) issues on an Innovia vehicle to comply with CENELEC standards. Measurements found a 105kHz resonance from the vehicle that exceeded limits. The root cause was identified as parasitic capacitance between inverter components coupling high frequency harmonics into power rails and causing line resonance. Modifications like adding shunting capacitors and common mode chokes to both propulsion and auxiliary inverters were able to block and contain harmonics, eliminating line resonance and bringing the vehicle into compliance.
The document summarizes the status and achievements of the National Fuel Cell Bus Program. It discusses the goals of developing fuel cell buses and components through multiple technology pathways. It provides updates on demonstration projects involving fuel cell buses and components with AC Transit and other partners. It highlights improvements in fuel cell reliability, durability and public acceptance. Technical hurdles around balance of plant components and further durability improvements are also noted.
Presentation from NORTHMOST - a new biannual series of meetings on the topic of mathematical modelling in transport.
Hosted at its.leeds.ac.uk, NORTHMOST 01 focussed on academic research, to encourage networking and collaboration between academics interested in the methodological development of mathematical modelling applied to transport.
The focus of the meetings will alternate; NORTHMOST 02 - planned for Spring 2017 - will be led by practitioners who are modelling experts. Practitioners will give presentations, with academic researchers in the audience. In addition to giving a forum for expert practitioners to meet and share best practice, a key aim of the series is to close the gap between research and practice, establishing a feedback loop to communicate the needs of practitioners to those working in university research.
Introducing the Centre for Railway EngineeringColin Cole
The Centre for Railway Engineering (CRE) is a research centre hosted by CQUniversity that focuses on conducting high-quality research, education, and consulting services for the rail industry. It has expertise in areas like train dynamics, wagon dynamics, track-vehicle interaction, erosion control, and simulation/testing. CRE works on projects for government agencies and rail industry clients to conduct research and develop innovations in areas important to the rail sector.
Describe the main characteristics of the Sydney Coordinated
Adaptive Traffic System (SCATS) and its use in 3 worldwide
cities. Clarification and explanation about the system and
making a comparison between three large cities that use
this system and detailing the advantages and
disadvantages of this system in each city that used it.
Data pre-processing plays a key role in a data analytics process (e.g., supervised learning). It encompasses a broad range of activities that span from correcting errors to selecting the most relevant features for the analysis phase. There is no clear evidence, or rules defined, on how pre-processing transformations (e,g., normalization, discretization, etc.) impact the final results of the analysis. The problem is exacerbated when transformations are combined into pre-processing pipeline prototypes. Data scientists cannot easily foresee the impact of pipeline prototypes and hence require a method to discriminate between them and find the most relevant ones (e.g., with highest positive impact) for their study at hand. Once found, these pipelines can be optimized using AutoML in order to generate executable pipelines (i.e., with parametrized operators for each transformation). In this work, we study the impact of transformations in general, and the impact of transformations when combined together into pipelines. We develop a generic method that allows to find effective pipeline prototypes. Evaluated using Scikit-learn, our effective pipeline prototypes, when optimized, provide results that get 90% of the optimal predictive accuracy in the median, but with a cost that is 24 times smaller.
The document discusses the evolution of traffic modeling for the Newcastle Light Rail project in Newcastle, Australia. It summarizes how moving to a catenary-free, in-station charging system for the light rail vehicles required innovative modeling approaches to analyze impacts to traffic and ensure project requirements for journey times were achieved. Additional simulation runs and alternative output definitions were needed to obtain sufficient resolution and confidence in results given the technology changes. The modeling demonstrated acceptable traffic and light rail performance with the project.
This study examines the immediate impacts of implementing Select Bus Service (SBS) on the Bx41 bus route in New York City. Key performance indicators such as dwell time per passenger, wait assessment, on-time performance, running time, and bus bunching are analyzed using data collected before and after the SBS launch. The results show significant improvements in all indicators, such as a 68% reduction in dwell time per passenger and a 28% increase in on-time performance for the SBS route. This supports SBS as an effective way to improve bus performance and the customer experience on important corridors.
Transport Network Analysis for Smart Open FleetsMiguel Rebollo
Extension of a framework to organize open fllets for last-mile delivery. It includes a module to analyze the transport network of a city as a complex network. A sample of the bike rental service is shown.
Similar to Real-time Signal Control and Traffic Stability / Improved Models for Managed Lanes Operations (20)
Updates provided to the D-STOP Business Advisory Council at the 2017 Symposium and Board Meeting: https://ctr.utexas.edu/2018/04/12/d-stop-2017-symposium-archive/
Updates provided to the D-STOP Business Advisory Council at the 2017 Symposium and Board Meeting: https://ctr.utexas.edu/2018/04/12/d-stop-2017-symposium-archive/
Updates provided to the D-STOP Business Advisory Council at the 2017 Symposium and Board Meeting: https://ctr.utexas.edu/2018/04/12/d-stop-2017-symposium-archive/
This document discusses ongoing research projects related to collaborative sensing and heterogeneous networking leveraging vehicular fleets. Specifically, it discusses:
1) How increased cluster density of vehicles improves overall data rates and reduces variability in individual user rates.
2) Modeling what collaborative sensing systems can "see" or be aware of in obstructed environments and how coverage benefits scale with increased penetration of collaborative vehicles.
3) Developing optimal information sharing policies to maximize situational awareness for autonomous nodes in resource-constrained network environments.
Updates provided to the D-STOP Business Advisory Council at the 2017 Symposium and Board Meeting: https://ctr.utexas.edu/2018/04/12/d-stop-2017-symposium-archive/
Updates provided to the D-STOP Business Advisory Council at the 2017 Symposium and Board Meeting: https://ctr.utexas.edu/2018/04/12/d-stop-2017-symposium-archive/
Updates provided to the D-STOP Business Advisory Council at the 2017 Symposium and Board Meeting: https://ctr.utexas.edu/2018/04/12/d-stop-2017-symposium-archive/
Updates provided to the D-STOP Business Advisory Council at the 2017 Symposium and Board Meeting: https://ctr.utexas.edu/2018/04/12/d-stop-2017-symposium-archive/
Updates provided to the D-STOP Business Advisory Council at the 2017 Symposium and Board Meeting: https://ctr.utexas.edu/2018/04/12/d-stop-2017-symposium-archive/
Online platforms are emerging as a powerful mechanism for matching resources to requests. In the setting of freight, the requests arrive from shippers, who have a diverse collection of goods. The resources are supplied by shippers (trucks), and have various physical constraints (driver’s route preferences, carrying capacity, geographic preferences, etc.). Online platforms are emerging that (a) learn the characteristics of shippers and carriers, and (b) efficiently match goods to trucks based on such learning.
Our project will develop algorithms for such online resource allocation. This is a challenging problem, due to the complexity of the learning tasks. Such algorithms can have considerable impact on efficiently using trucking resources.
Through this project, the research team will leverage the computing resources and expertise at UT to develop a “data discovery environment” for transportation data to aid decision-making. Many efforts focus on leveraging transportation data to help travelers make decisions, but less thought has gone into a framework for using big data to help transportation agency staff and decision makers. The team will start by building the DDE for the Central Texas region, in collaboration with the local MPO, the City of Austin, and the local transit agency. Initially, the project will focus on creating more meaning from existing data sources, and as the project progresses, it will grow to include more novel data sources and methods. The data platform will be web-based and part of the research includes not only building the tool but developing appropriate protocols for access and governance.
This document discusses modeling strategies for autonomous and connected vehicles. It proposes modifying traditional four-step transportation models to account for autonomous vehicle adoption rates and different trip types. Autonomous vehicle passenger car equivalents and flow ratios are modeled based on vehicle speed, market penetration, and other factors. The document also describes plans for a 4G deployment test bed to demonstrate connected vehicle technologies on managed lanes in Dallas-Fort Worth and Virginia.
Advanced driver assistance systems (ADAS) are a key technology for improving road safety. But both current and proposed ADAS are limited in important ways. Vision- and lidar-based ADAS performs poorly in heavy rain, snow, or fog. Lack of vehicle situational awareness due to these sensing limitations will unfortunately be the cause of many accidents, including fatalities, for connected and automated vehicles in the years to come. The goal of this research is to develop and test a sensing strategy with robust perception: No blind spots, applicable to all driveable environments, and available in all weather conditions. We believe there are three key requirements for collaborative all-weather sensing:
– Precise vehicle positioning within a common reference frame
– Decimeter-accurate vision and radar mapping
– A means of quantifying the benefits of collaborative sensing
Vehicular radar and communication are the two primary means of using radio frequency (RF) signals in transportation systems. Automotive radars provide high-resolution sensing using proprietary waveforms in millimeter wave (mmWave) bands and vehicular communications allow vehicles to exchange safety messages or raw sensor data. Both the techniques can be used for applications such as forward collision warning, cooperative adaptive cruise control, and pre-crash applications.
Many areas of machine learning and data mining focus on point estimates of key parameters. In transportation, however, the inherent variance, and, critically, the need to understand the limits of that variance and the impact it may have, have long been understood to be important. Indeed, variance and other risk measures that capture the cost of the spread around the mean, are critical factors in understanding how people act. Thus they are critical for prediction, as well as for purposes of long term planning, where controlling risk may be equally important to controlling the mean (the point estimate).
There has been tremendous progress on large scale optimization techniques to enable the solution of large scale machine learning and data analytics problems. Stochastic Gradient Descent and its variants is probably the most-used large-scale optimization technique for learning. This has not yet seen an impact on the problem of statistical inference — namely, obtaining distributional information that might allow us to control the variance and hence the risk of certain solutions.
Investigation and findings on reservation-based intersections and managed lanes
Real-Time Signal Control and Traffic Stability
Congestion on urban arterials is largely centered around intersection control. Traditional traffic signal schemes are limited in their ability to adapt in real time to traffic conditions or by their ability to coordinate with each other to ensure adequate performance. Specifically, there is a tension between adaptivity (as with actuated signals) and coordination through pre-timed signals (signal progression). We propose to investigate whether routing protocols in telecommunications networks can be applied to resolve these problems. Specifically, the backpressure algorithm of Tassiulas & Emphremides (1992) can ensure system stability through decentralized control under relatively weak regularity conditions. It is as yet unknown whether this algorithm can be adapted to traffic signal systems, and if so, what modifications are needed. Traffic systems differ in several significant ways from telecommunication networks: each intersection approach has relatively few queues (lanes) that must be shared among traffic to various definitions. First-in, first-out constraints lead to head-of-line blocking effects, traffic waves move at a much slower speed than data packets, and traffic queues are tightly limited by physical space (finite buffers). Determining whether (and how) the backpressure concept can be adapted to traffic networks requires significant research, and has the potential to dramatically improve signal performance.
Improved Models for Managed Lane Operations
Managed lanes (ML) are increasingly being considered as a tool to mitigate congestion on highways with limited areas for capacity expansion. Managed lanes are dynamically priced based on the congestion level, and can be set either with the objective of maximum utilization (e.g., a public operator) or profit maximization (e.g., a private operator). Optimization models for determining these pricing policies make restrictive assumptions about the layout of these corridors (often a single entrance and exit) or knowledge of traveler characteristics on behalf of the modeler (e.g., distribution of willingness to pay). Developing new models to address these issues would allow for better utilization of these facilities.
Professor Robert W. Heath Jr. is the director of UT SAVES (Situation-Aware Vehicular Engineering Systems), which combines expertise in wireless communications, signal processing, and transportation research. UT SAVES collaborates with automotive companies like Honda R&D Americas on projects involving sensing, communication, and analytics for applications such as automated driving. Membership provides access to UT SAVES research and facilities, including graduate research assistants and experimental capabilities in areas like millimeter wave communication and sensor fusion. Current research projects focus on cooperative sensing, vehicle-to-everything communication, and applying 5G cellular networks to driving assistance technologies.
The Business Advisory Council meeting covered the following topics in 3 sentences or less:
The meeting covered updates on education and workforce development programs at the Engineering Education and Research Center including summer internships and distinguished lectures. Research updates were provided on 30 completed projects and 18 ongoing projects covering topics like connected corridors and autonomous vehicles. New proposed research was presented on topics such as video data analytics, traffic signal optimization, and modeling willingness to share trips in autonomous vehicles.
The document discusses managing mobility during the design-build reconstruction of the Dallas Horseshoe highway interchange project. It describes the project's high traffic volumes and constraints. It highlights the contractor's successes in maintaining access and maximizing work during limited closures. It stresses the importance of collaboration between the agency and contractor in developing traffic control plans and finding solutions to difficult situations.
The document summarizes research on the use of natural pozzolans and reclaimed/remediated fly ashes in concrete. Key findings include:
1) Natural pozzolans like pumice and metakaolin reduced heat of hydration and provided good strength and ASR resistance, while zeolites and shale also performed well.
2) Reclaimed and remediated fly ashes reduced heat of hydration and met ASTM standards, with fineness impacting performance.
3) Future research will assess blended fly ashes and develop rapid screening tests for supplementary cementitious materials.
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Real-time Signal Control and Traffic Stability / Improved Models for Managed Lanes Operations
1. Real-time signal control and traffic stability
Improved models for managed lanes operations
Stephen D. Boyles
Associate Professor
The University of Texas at Austin
April 11, 2018
DSTOP updates Boyles
3. The reservation-based intersection
Connected and automated vehicles provide new opportunities for
improving intersection throughput.
DSTOP updates
Project 125: Real-time signal control and
traffic stability Boyles
4. Earlier DSTOP research showed that improving capacity locally may not
improve throughput globally.
DSTOP updates
Project 125: Real-time signal control and
traffic stability Boyles
5. This project explores better ways to prioritize vehicles at reservation-based
intersections.
Conversations with DSTOP colleagues raised the possibility of
backpressure-based intersection priority.
DSTOP updates
Project 125: Real-time signal control and
traffic stability Boyles
6. How to adapt the backpressure concept for roadway networks?
Physical queues: Roadway links have a finite (and often binding) “buffer”
for storing vehicles, which bounds pressure terms.
DSTOP updates
Project 125: Real-time signal control and
traffic stability Boyles
7. How to adapt the backpressure concept for roadway networks?
Physical queues: Roadway links have a finite (and often binding) “buffer”
for storing vehicles, which bounds pressure terms. Solution:
“Chain” queues that spill back by recursively adding pressure
terms for links upstream.
DSTOP updates
Project 125: Real-time signal control and
traffic stability Boyles
8. How to adapt the backpressure concept for roadway networks?
Physical queues: Roadway links have a finite (and often binding) “buffer”
for storing vehicles, which bounds pressure terms. Solution:
“Chain” queues that spill back by recursively adding pressure
terms for links upstream.
Route choice: Vehicles can choose routes independently and selfishly
DSTOP updates
Project 125: Real-time signal control and
traffic stability Boyles
9. How to adapt the backpressure concept for roadway networks?
Physical queues: Roadway links have a finite (and often binding) “buffer”
for storing vehicles, which bounds pressure terms. Solution:
“Chain” queues that spill back by recursively adding pressure
terms for links upstream.
Route choice: Vehicles can choose routes independently and selfishly
Solution: Add equilibrium principle and iterate with
updated routes.
DSTOP updates
Project 125: Real-time signal control and
traffic stability Boyles
11. Granting reservations according to the backpressure principle reduced
delays significantly beyond earlier (FCFS) protocols.
DSTOP updates
Project 125: Real-time signal control and
traffic stability Boyles
12. Future work
Test alternative way to address finite buffer (nonlinear transformation
of pressure term)
DSTOP updates
Project 125: Real-time signal control and
traffic stability Boyles
13. Future work
Test alternative way to address finite buffer (nonlinear transformation
of pressure term)
Compare with P0 policy developed by M. J. Smith
DSTOP updates
Project 125: Real-time signal control and
traffic stability Boyles
15. Managed lanes
Dynamic toll lanes present new opportunities and challenges for traffic
management.
DSTOP updates
Project 140: Improved models for managed
lane operations Boyles
16. Earlier research is largely confined to single-entrance, single-exit facilities.
DSTOP updates
Project 140: Improved models for managed
lane operations Boyles
17. In facilities with multiple entrances and exits, the number of paths through
the corridor grows exponentially with the number of access points.
DSTOP updates
Project 140: Improved models for managed
lane operations Boyles
19. By reformulating the route choice model to operate at diverge node, we
can represent all possible paths with a polynomial set of variables.
DSTOP updates
Project 140: Improved models for managed
lane operations Boyles
20. We used approximate dynamic programming to identify toll policies which
maximize throughput (public facility) and maximize revenue (private
facility).
DSTOP updates
Project 140: Improved models for managed
lane operations Boyles
22. Future work
Use field data to calibrate/validate model.
Explore methods for estimating value-of-time distributions from data.
Further explore “jam-and-harvest” phenomenon
DSTOP updates
Project 140: Improved models for managed
lane operations Boyles