This document outlines a FlexSim simulation model of an airport security checkpoint. The base model contains one metal detector and x-ray scanner, resulting in average wait times of 80 minutes. An alternative model with two scanners significantly reduces average wait time to 5 minutes while increasing passenger throughput by 67% and luggage throughput by 11%. While the dual scanner model improves performance, the low passenger volume may not justify the increased operating costs compared to the benefits.
ANALYSIS OF EMERGENCY EVACUATION USING LARGE-SCALE SIMULATIONardodul
A SimPy-based discrete event simulation model for large-scale disaster evacuation systems. The model has capability to describe and simulate detailed transportation networks and alternative modes of transportation.
Solving real world delivery problem using improved max-min ant system with lo...ijaia
This paper presents a solution to real-world delive
ry problems (RWDPs) for home delivery services wher
e
a large number of roads exist in cities and the tra
ffic on the roads rapidly changes with time. The
methodology for finding the shortest-travel-time to
ur includes a hybrid meta-heuristic that combines a
nt
colony optimization (ACO) with Dijkstra’s algorithm
, a search technique that uses both real-time traff
ic
and predicted traffic, and a way to use a real-worl
d road map and measured traffic in Japan. We
previously proposed a hybrid ACO for RWDPs that use
d a MAX-MIN Ant System (MMAS) and proposed a
method to improve the search rate of MMAS. Since tr
affic on roads changes with time, the search rate i
s
important in RWDPs. In the current work, we combine
the hybrid ACO method with the improved MMAS.
Experimental results using a map of central Tokyo a
nd historical traffic data indicate that the propos
ed
method can find a better solution than conventional
methods.
Enhanced random walk with choice an empirical studygraphhoc
The Random Walk with d Choice RWC(d ) is a recently proposed variation of the simple Random Walk
that first selects a subset of d neighbor nodes and then decides to move to the node which minimizes the
value of a certain parameter; this parameter captures the number of past visits of the walk to that node. In
this paper, we propose the Enhanced Random Walk with d Choice algorithm ERWC(d, h) which first
selects a subset of d neighbor nodes and then decides to move to the node which minimizes a value H
defined at every node; this H value depends on a parameter h and captures information about past visits
of the walk to that node and - with a certain weight - to its neighbors. Simulations of the Enhanced Random
Walk with d Choice algorithm on various types of graphs indicate beneficial results with respect to Cover
Time and Load Balancing. The graph types used are the Random Geometric Graph, Torus, Grid,
Hypercube, Lollipop and Bernoulli.
Malzeme taşıma si̇stemleri̇ - Üni̇te yük eki̇pmanları / Material Handling Sys...Kazım Anıl AYDIN
Malzeme taşıma si̇stemleri̇ - Üni̇te yük eki̇pmanları / Material Handling Systems - Unit Load Equipment | Malzeme taşıma si̇stemleri̇ dersi sunumu - Material handling systems slide | Kazım Anıl AYDIN < Samsun - 2016 > Başlıklar - İçerikler :
Paletler, Öz taşıma(Self-restraining), Kızaklar, Yükleme levhaları, Tepsiler,Palet/kızak kutuları, Karton kutular, Çantalar, Toplu yük konteynerleri, Sandıklar, Şerit/ Bant/ Yapıştırıcı,Intermodal konteynerler, Küçült-sar / Ger-sar, Paletleyiciler | Etiketler: malzeme taşıma sistemleri, material handling systems, slides, sunum, unit load equipment, ünite yük ekipmanları
This document summarizes a simulation project to optimize the process at a university campus Subway outlet. The current process leads to long wait times during lunch hours. The simulation models the current process and a proposed process with additional resources. Model 2, which adds one employee each to the order counter and billing counter, reduces average wait times and total time in the system based on the simulation results and statistical analysis. Therefore, hiring two new employees is recommended to improve customer experience and satisfaction.
The document summarizes a simulation study conducted on a restaurant called "Canes" to analyze customer waiting times. The original scenario showed long wait times when customers decided orders at the counter. An alternative scenario assumed customers pre-decided orders. Simulation results showed the alternative scenario significantly reduced average wait time, time in system, and queue length while increasing customers served. It was recommended the restaurant display menus by the queue to help customers pre-decide orders.
This document outlines a FlexSim simulation model of an airport security checkpoint. The base model contains one metal detector and x-ray scanner, resulting in average wait times of 80 minutes. An alternative model with two scanners significantly reduces average wait time to 5 minutes while increasing passenger throughput by 67% and luggage throughput by 11%. While the dual scanner model improves performance, the low passenger volume may not justify the increased operating costs compared to the benefits.
ANALYSIS OF EMERGENCY EVACUATION USING LARGE-SCALE SIMULATIONardodul
A SimPy-based discrete event simulation model for large-scale disaster evacuation systems. The model has capability to describe and simulate detailed transportation networks and alternative modes of transportation.
Solving real world delivery problem using improved max-min ant system with lo...ijaia
This paper presents a solution to real-world delive
ry problems (RWDPs) for home delivery services wher
e
a large number of roads exist in cities and the tra
ffic on the roads rapidly changes with time. The
methodology for finding the shortest-travel-time to
ur includes a hybrid meta-heuristic that combines a
nt
colony optimization (ACO) with Dijkstra’s algorithm
, a search technique that uses both real-time traff
ic
and predicted traffic, and a way to use a real-worl
d road map and measured traffic in Japan. We
previously proposed a hybrid ACO for RWDPs that use
d a MAX-MIN Ant System (MMAS) and proposed a
method to improve the search rate of MMAS. Since tr
affic on roads changes with time, the search rate i
s
important in RWDPs. In the current work, we combine
the hybrid ACO method with the improved MMAS.
Experimental results using a map of central Tokyo a
nd historical traffic data indicate that the propos
ed
method can find a better solution than conventional
methods.
Enhanced random walk with choice an empirical studygraphhoc
The Random Walk with d Choice RWC(d ) is a recently proposed variation of the simple Random Walk
that first selects a subset of d neighbor nodes and then decides to move to the node which minimizes the
value of a certain parameter; this parameter captures the number of past visits of the walk to that node. In
this paper, we propose the Enhanced Random Walk with d Choice algorithm ERWC(d, h) which first
selects a subset of d neighbor nodes and then decides to move to the node which minimizes a value H
defined at every node; this H value depends on a parameter h and captures information about past visits
of the walk to that node and - with a certain weight - to its neighbors. Simulations of the Enhanced Random
Walk with d Choice algorithm on various types of graphs indicate beneficial results with respect to Cover
Time and Load Balancing. The graph types used are the Random Geometric Graph, Torus, Grid,
Hypercube, Lollipop and Bernoulli.
Malzeme taşıma si̇stemleri̇ - Üni̇te yük eki̇pmanları / Material Handling Sys...Kazım Anıl AYDIN
Malzeme taşıma si̇stemleri̇ - Üni̇te yük eki̇pmanları / Material Handling Systems - Unit Load Equipment | Malzeme taşıma si̇stemleri̇ dersi sunumu - Material handling systems slide | Kazım Anıl AYDIN < Samsun - 2016 > Başlıklar - İçerikler :
Paletler, Öz taşıma(Self-restraining), Kızaklar, Yükleme levhaları, Tepsiler,Palet/kızak kutuları, Karton kutular, Çantalar, Toplu yük konteynerleri, Sandıklar, Şerit/ Bant/ Yapıştırıcı,Intermodal konteynerler, Küçült-sar / Ger-sar, Paletleyiciler | Etiketler: malzeme taşıma sistemleri, material handling systems, slides, sunum, unit load equipment, ünite yük ekipmanları
This document summarizes a simulation project to optimize the process at a university campus Subway outlet. The current process leads to long wait times during lunch hours. The simulation models the current process and a proposed process with additional resources. Model 2, which adds one employee each to the order counter and billing counter, reduces average wait times and total time in the system based on the simulation results and statistical analysis. Therefore, hiring two new employees is recommended to improve customer experience and satisfaction.
The document summarizes a simulation study conducted on a restaurant called "Canes" to analyze customer waiting times. The original scenario showed long wait times when customers decided orders at the counter. An alternative scenario assumed customers pre-decided orders. Simulation results showed the alternative scenario significantly reduced average wait time, time in system, and queue length while increasing customers served. It was recommended the restaurant display menus by the queue to help customers pre-decide orders.
1) The document summarizes a simulation of shuttle bus operations for the University of Cincinnati's North Route shuttle using Arena simulation software.
2) Three scenarios were modeled - a single large 26-seat bus, two large 26-seat buses, and two smaller 14-seat buses.
3) Based on the statistical analysis of 100 replications of each scenario, using two smaller 14-seat buses achieved the best utilization with the lowest average number of empty seats while maintaining a reasonable average passenger waiting time.
Dispatching taxi cabs with ridesharing – an efficient implementation of popul...Bogusz Jelinski
The article describes how to effectively dispatch hundreds of thousands
ride requests per hour, with thousands of cabs. Not only which cab should pick up
which passenger, but which passengers should share a ride and what is the best pickup
and drop-off order. An automatic dispatching process has been implemented to
verify feasibility of such cab sharing solution, simulation was used to check quality
of routes. Performance of different programming tools and frameworks has been
tested. Thousands of passengers per minute could be dispatched with basic
algorithms and simple hardware and they can be dispatched in a cab sharing scheme
very effectively, at least 11 passengers per cab per hour. The spotlight is on practical
aspects, not well-known theory. The goal is to verify feasibility of a large-scale
dispatcher and to give its benchmark. Implementation of algorithms including a
dispatcher and simulation environment is available as open source on GitHub.
This document proposes a new queue prediction model based on data that can be collected from a single loop detector at a signalized intersection stop line. The model was developed using an enhanced NGSIM vehicle trajectory dataset. Six logistic regression models were developed that correctly predicted whether a vehicle was queued or part of a platoon 83-95% of the time based on variables like speed, headway, spacing and occupancy that could be measured from a single stop line detector. When combined with a logical filter to group sequential vehicles, the models enable estimation of queue length to help optimize traffic signal offset times.
Learning And Inferring Transportation Routinesmstjsw
The document presents a hierarchical activity model to infer a user's location, mode of transportation, and destinations over time using GPS sensor data. It uses a Rao-Blackwellized particle filter algorithm in a hierarchical model to estimate locations, modes, trip segments, and goals at each time step. The model can learn typical transportation modes and goals from data using expectation maximization. An evaluation shows it can accurately model activities and detect errors by identifying novel behaviors. The system is intended to provide predictive notifications and opportunities to users.
Iaetsd modified artificial potential fields algorithm for mobile robot path ...Iaetsd Iaetsd
This document presents a modified artificial potential fields algorithm for mobile robot path planning in unknown and dynamic environments. The algorithm uses artificial potential fields to iteratively find optimal points to form a collision-free path from the start to destination. For static obstacles, potential values are used to identify clusters of points around the start and goal, and find a connecting midpoint. This process is repeated iteratively. For dynamic obstacles, Markov models are used to analyze obstacle behavior from sensor data and predict collision points. The robot's path is replanned as needed to avoid collisions based on feedback from sensors and odometry. Simulation results show the algorithm can efficiently plan paths in unknown environments and avoid both static and dynamic obstacles.
Simulation study of the BART station at Embarcadero in San Francisco using Arena. The model compares the wait time for passengers at the station for multiple scenarios analyzed within.
Data collection scheme for wireless sensor network with mobile collectorijwmn
In this paper, we investigate the problem of designing the minimum number of required mobile elements
tours such that each sensor node is either on the tour or one hop away from the tour, and the length of the
tour to be bounded by pre-determined value L. To address this problem, we propose heuristic-based
solution. This solution works by directing the mobile element tour towards the highly dense area in the
network. The experiment results show that our scheme outperform the benchmark scheme by 10% in most
scenarios.
As-Puma : Anycast Semantics In Parking Using Metaheuristic Approachpijans
The number of vehicle used in the world are increasing day by day resulting in the obvious problem of
parking of these vehicle’s in residential and vocational areas. We perceive the problem of vehicles parking
in vocational establishments / malls. Today majority of parking systems are manual parking systems where
in, on the spot, parking of the vehicle is done and a parking slip is generated and handed over to customer.
This is cumbersome technique wherein various parking attendants in the parking areas manually keeps on
informing the Parking inspector on how many free parking slots available so that only that many number of
parking slips/tickets are generated as the number of free parking slots. We address the problem of parking
in Delay Tolerant Network (DTN) by proposing metaheuristic driven approach of Ant Colony optimization
(ACO) technique with anycast semantics models . Here we propose the parking architecture to solve the
problem of parking especially in commercial areas with their design diagrams . In this architecture we
apply the delivery model to deliver the packet correctly to the intended receiver. Using this we can book
various parking’s through remote areas so that the customer can get the information about availability of
various parking’s inside an area and the parking fare for each category of the automobile. Using this
architecture the customer can get the prior knowledge about various vacant parking slots inside a parking
area and he can book the corresponding parking from his location.
AS-PUMA : ANYCAST SEMANTICS IN PARKING USING METAHEURISTIC APPROACHpijans
This document proposes an ant colony optimization (ACO) approach for routing in delay tolerant networks to address the problem of vehicle parking. It presents a parking architecture that applies ACO techniques with anycast semantics to allow customers to remotely book parking slots based on availability information. The architecture uses mobile vehicles to broadcast parking requests and a central server to reply with information. It also describes three anycast semantics models - current membership, temporal interval membership, and temporal point membership - to correctly deliver messages to intended receivers in the changing network. The approach aims to build an open source automated parking tool to solve real-world parking challenges.
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.
This document summarizes a student project on traffic simulation. It introduces the goals of developing a reasonably realistic traffic simulator using real city maps that allows real-time interaction on inexpensive machines and is easily extended. It reviews related work on simulators like SUMO, MATSim, and AIM. It describes how the project uses OpenStreetMap data to build maps and models each vehicle as an autonomous agent. The traffic simulation modeling approach and concepts like determinism and intersection bidding are also summarized. Future work ideas are outlined like improving traffic light logic and adding vehicle diversity.
Neural Network Based Parking via Google Map GuidanceIJERA Editor
This document describes an intelligent parking guidance system that uses neural networks and algorithms to predict travel times between locations and allocate parking spaces. It consists of an Intelligent Trip Modeling System (ITMS) that uses a Speed Prediction Neural Network System (SPNNS) and Dynamic Traversing Speed Profile (DTSP) algorithm to accurately predict traffic speed and travel times. The system also includes an intelligent parking guidance component that provides information on nearby parking availability and allows users to reserve spaces based on their predicted time of arrival. The overall goal is to help drivers efficiently find parking by predicting travel times and allocating spaces in advance.
Queuing theory: What is a Queuing system???
Waiting for service is part of our daily life….
Example:
we wait to eat in restaurants….
We queue up in grocery stores…
Jobs wait to be processed on machine…
Vehicles queue up at traffic signal….
Planes circle in a stack before given permission to land at an airport….
Unfortunately, we can not eliminate waiting time without incurring expenses…
But, we can hope to reduce the queue time to a tolerable levels… so that we can avoid adverse impact….
Why study???? What analytics can be drawn??? Analytics means ---- measures of performance such as
1. Average queue length
2. Average waiting time in the queue
3. Average facility utilization….
Predicting Post-SafeTrack Metro ReliabilityMicah Melling
The document describes a project to model the potential impact of Metro's SafeTrack maintenance project on commuter rail delays. It involved developing two simulation models: 1) a discrete event simulation framework using dummy data for future use when real data becomes available, and 2) a less robust simulation of daily trips using available delay data to estimate SafeTrack's effects. Various scenarios were tested by adjusting delay probabilities and severities. Results showed potential reductions in delay times and variations, though limitations exist due to data gaps. The project provides insight into SafeTrack's possible impacts while highlighting the need for improved data to develop more accurate models.
Traffic Congestion Prediction using Deep Reinforcement Learning in Vehicular ...IJCNCJournal
In recent years, a new wireless network called vehicular ad-hoc network (VANET), has become a popular research topic. VANET allows communication among vehicles and with roadside units by providing information to each other, such as vehicle velocity, location and direction. In general, when many vehicles likely to use the common route to proceed to the same destination, it can lead to a congested route that should be avoided. It may be better if vehicles are able to predict accurately the traffic congestion and then avoid it. Therefore, in this work, the deep reinforcement learning in VANET to enhance the ability to predict traffic congestion on the roads is proposed. Furthermore, different types of neural networks namely Convolutional Neural Network (CNN), Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM) are investigated and compared in this deep reinforcement learning model to discover the most effective one. Our proposed method is tested by simulation. The traffic scenarios are created using traffic simulator called Simulation of Urban Mobility (SUMO) before integrating with deep reinforcement learning model. The simulation procedures, as well as the programming used, are described in detail. The performance of our proposed method is evaluated using two metrics; the average travelling time delay and average waiting time delay of vehicles. According to the simulation results, the average travelling time delay and average waiting time delay are gradually improved over the multiple runs, since our proposed method receives feedback from the environment. In addition, the results without and with three different deep learning algorithms, i.e., CNN, MLP and LSTM are compared. It is obvious that the deep reinforcement learning model works effectively when traffic density is neither too high nor too low. In addition, it can be concluded that the effective algorithms for traffic congestion prediction models in descending order are MLP, CNN, and LSTM, respectively.
TRAFFIC CONGESTION PREDICTION USING DEEP REINFORCEMENT LEARNING IN VEHICULAR ...IJCNCJournal
In recent years, a new wireless network called vehicular ad-hoc network (VANET), has become a popular research topic. VANET allows communication among vehicles and with roadside units by providing information to each other, such as vehicle velocity, location and direction. In general, when many vehicles likely to use the common route to proceed to the same destination, it can lead to a congested route that should be avoided. It may be better if vehicles are able to predict accurately the traffic congestion and then avoid it. Therefore, in this work, the deep reinforcement learning in VANET to enhance the ability to predict traffic congestion on the roads is proposed. Furthermore, different types of neural networks namely Convolutional Neural Network (CNN), Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM) are investigated and compared in this deep reinforcement learning model to discover the most effective one. Our proposed method is tested by simulation. The traffic scenarios are created using traffic simulator called Simulation of Urban Mobility (SUMO) before integrating with deep reinforcement learning model. The simulation procedures, as well as the programming used, are described in detail. The performance of our proposed method is evaluated using two metrics; the average travelling time delay and average waiting time delay of vehicles. According to the simulation results, the average travelling time delay and average waiting time delay are gradually improved over the multiple runs, since our proposed method receives feedback from the environment. In addition, the results without and with three different deep learning algorithms, i.e., CNN, MLP and LSTM are compared. It is obvious that the deep reinforcement learning model works effectively when traffic density is neither too high nor too low. In addition, it can be concluded that the effective algorithms for traffic congestion prediction models in descending order are MLP, CNN, and LSTM, respectively.
Decentralized system to compute safest route - ReportAnushka Patil
Designed and implemented a backend application to show people how to avoid dangerous spots on city streets while walking from one place to another. Here we are providing the paths that offer trade-offs between safety and distance. We have developed an algorithm that would give a person walking through a city options for getting from one place to another — the shortest path, the safest path that balanced between both factors.
Project url: https://github.com/anushkaaaa/-Decentralized-system-to-compute-safest-route
1. The document describes a mixed-integer programming model for optimizing mass transit rail timetables to minimize passenger transfer waiting times.
2. The model adjusts train run times, dwell times at stations, and dispatch times to improve synchronization between connecting routes. This helps coordinate schedules to allow for smooth transfers between lines.
3. Preliminary tests on Hong Kong's MTR system showed the optimized timetables from this model significantly reduced total passenger waiting times compared to current schedules. The model provides a useful decision tool to balance operational parameters and improve passenger experience.
AUTO-MOBILE VEHICLE DIRECTION IN ROAD TRAFFIC USING ARTIFICIAL NEURAL NETWORKS.ijaia
This document summarizes a research paper that proposes using an artificial neural network to analyze the best route for automobile vehicles in traffic conditions. It presents a neural network architecture with 5 input parameters (distance, traffic volume, number of signals, road condition, travel time) and 5 output routes. The network is trained on 243 input combinations and tested on unseen data. Test results showed the network with backpropagation had 91.2% accuracy and lower error than without backpropagation. The research aims to help commuters optimize routes based on traffic parameters, though assumptions about static conditions limit realism. Future work will analyze more dynamic traffic flow conditions.
1) The document discusses cellular automata traffic flow models, which use a discrete approach to simulate vehicle movement on roads.
2) It specifically examines the Nagel-Schreckenberg model, which models single-lane traffic as a probabilistic cellular automaton. In this model, vehicles move according to rules of acceleration, deceleration based on gaps, and randomization of speed.
3) The document provides the rules and algorithms for simulating traditional cellular automaton traffic models on a single lane, including input parameters, defining gaps between vehicles, and having each vehicle follow the four rules of movement at each time step.
The Ipsos - AI - Monitor 2024 Report.pdfSocial Samosa
According to Ipsos AI Monitor's 2024 report, 65% Indians said that products and services using AI have profoundly changed their daily life in the past 3-5 years.
1) The document summarizes a simulation of shuttle bus operations for the University of Cincinnati's North Route shuttle using Arena simulation software.
2) Three scenarios were modeled - a single large 26-seat bus, two large 26-seat buses, and two smaller 14-seat buses.
3) Based on the statistical analysis of 100 replications of each scenario, using two smaller 14-seat buses achieved the best utilization with the lowest average number of empty seats while maintaining a reasonable average passenger waiting time.
Dispatching taxi cabs with ridesharing – an efficient implementation of popul...Bogusz Jelinski
The article describes how to effectively dispatch hundreds of thousands
ride requests per hour, with thousands of cabs. Not only which cab should pick up
which passenger, but which passengers should share a ride and what is the best pickup
and drop-off order. An automatic dispatching process has been implemented to
verify feasibility of such cab sharing solution, simulation was used to check quality
of routes. Performance of different programming tools and frameworks has been
tested. Thousands of passengers per minute could be dispatched with basic
algorithms and simple hardware and they can be dispatched in a cab sharing scheme
very effectively, at least 11 passengers per cab per hour. The spotlight is on practical
aspects, not well-known theory. The goal is to verify feasibility of a large-scale
dispatcher and to give its benchmark. Implementation of algorithms including a
dispatcher and simulation environment is available as open source on GitHub.
This document proposes a new queue prediction model based on data that can be collected from a single loop detector at a signalized intersection stop line. The model was developed using an enhanced NGSIM vehicle trajectory dataset. Six logistic regression models were developed that correctly predicted whether a vehicle was queued or part of a platoon 83-95% of the time based on variables like speed, headway, spacing and occupancy that could be measured from a single stop line detector. When combined with a logical filter to group sequential vehicles, the models enable estimation of queue length to help optimize traffic signal offset times.
Learning And Inferring Transportation Routinesmstjsw
The document presents a hierarchical activity model to infer a user's location, mode of transportation, and destinations over time using GPS sensor data. It uses a Rao-Blackwellized particle filter algorithm in a hierarchical model to estimate locations, modes, trip segments, and goals at each time step. The model can learn typical transportation modes and goals from data using expectation maximization. An evaluation shows it can accurately model activities and detect errors by identifying novel behaviors. The system is intended to provide predictive notifications and opportunities to users.
Iaetsd modified artificial potential fields algorithm for mobile robot path ...Iaetsd Iaetsd
This document presents a modified artificial potential fields algorithm for mobile robot path planning in unknown and dynamic environments. The algorithm uses artificial potential fields to iteratively find optimal points to form a collision-free path from the start to destination. For static obstacles, potential values are used to identify clusters of points around the start and goal, and find a connecting midpoint. This process is repeated iteratively. For dynamic obstacles, Markov models are used to analyze obstacle behavior from sensor data and predict collision points. The robot's path is replanned as needed to avoid collisions based on feedback from sensors and odometry. Simulation results show the algorithm can efficiently plan paths in unknown environments and avoid both static and dynamic obstacles.
Simulation study of the BART station at Embarcadero in San Francisco using Arena. The model compares the wait time for passengers at the station for multiple scenarios analyzed within.
Data collection scheme for wireless sensor network with mobile collectorijwmn
In this paper, we investigate the problem of designing the minimum number of required mobile elements
tours such that each sensor node is either on the tour or one hop away from the tour, and the length of the
tour to be bounded by pre-determined value L. To address this problem, we propose heuristic-based
solution. This solution works by directing the mobile element tour towards the highly dense area in the
network. The experiment results show that our scheme outperform the benchmark scheme by 10% in most
scenarios.
As-Puma : Anycast Semantics In Parking Using Metaheuristic Approachpijans
The number of vehicle used in the world are increasing day by day resulting in the obvious problem of
parking of these vehicle’s in residential and vocational areas. We perceive the problem of vehicles parking
in vocational establishments / malls. Today majority of parking systems are manual parking systems where
in, on the spot, parking of the vehicle is done and a parking slip is generated and handed over to customer.
This is cumbersome technique wherein various parking attendants in the parking areas manually keeps on
informing the Parking inspector on how many free parking slots available so that only that many number of
parking slips/tickets are generated as the number of free parking slots. We address the problem of parking
in Delay Tolerant Network (DTN) by proposing metaheuristic driven approach of Ant Colony optimization
(ACO) technique with anycast semantics models . Here we propose the parking architecture to solve the
problem of parking especially in commercial areas with their design diagrams . In this architecture we
apply the delivery model to deliver the packet correctly to the intended receiver. Using this we can book
various parking’s through remote areas so that the customer can get the information about availability of
various parking’s inside an area and the parking fare for each category of the automobile. Using this
architecture the customer can get the prior knowledge about various vacant parking slots inside a parking
area and he can book the corresponding parking from his location.
AS-PUMA : ANYCAST SEMANTICS IN PARKING USING METAHEURISTIC APPROACHpijans
This document proposes an ant colony optimization (ACO) approach for routing in delay tolerant networks to address the problem of vehicle parking. It presents a parking architecture that applies ACO techniques with anycast semantics to allow customers to remotely book parking slots based on availability information. The architecture uses mobile vehicles to broadcast parking requests and a central server to reply with information. It also describes three anycast semantics models - current membership, temporal interval membership, and temporal point membership - to correctly deliver messages to intended receivers in the changing network. The approach aims to build an open source automated parking tool to solve real-world parking challenges.
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.
This document summarizes a student project on traffic simulation. It introduces the goals of developing a reasonably realistic traffic simulator using real city maps that allows real-time interaction on inexpensive machines and is easily extended. It reviews related work on simulators like SUMO, MATSim, and AIM. It describes how the project uses OpenStreetMap data to build maps and models each vehicle as an autonomous agent. The traffic simulation modeling approach and concepts like determinism and intersection bidding are also summarized. Future work ideas are outlined like improving traffic light logic and adding vehicle diversity.
Neural Network Based Parking via Google Map GuidanceIJERA Editor
This document describes an intelligent parking guidance system that uses neural networks and algorithms to predict travel times between locations and allocate parking spaces. It consists of an Intelligent Trip Modeling System (ITMS) that uses a Speed Prediction Neural Network System (SPNNS) and Dynamic Traversing Speed Profile (DTSP) algorithm to accurately predict traffic speed and travel times. The system also includes an intelligent parking guidance component that provides information on nearby parking availability and allows users to reserve spaces based on their predicted time of arrival. The overall goal is to help drivers efficiently find parking by predicting travel times and allocating spaces in advance.
Queuing theory: What is a Queuing system???
Waiting for service is part of our daily life….
Example:
we wait to eat in restaurants….
We queue up in grocery stores…
Jobs wait to be processed on machine…
Vehicles queue up at traffic signal….
Planes circle in a stack before given permission to land at an airport….
Unfortunately, we can not eliminate waiting time without incurring expenses…
But, we can hope to reduce the queue time to a tolerable levels… so that we can avoid adverse impact….
Why study???? What analytics can be drawn??? Analytics means ---- measures of performance such as
1. Average queue length
2. Average waiting time in the queue
3. Average facility utilization….
Predicting Post-SafeTrack Metro ReliabilityMicah Melling
The document describes a project to model the potential impact of Metro's SafeTrack maintenance project on commuter rail delays. It involved developing two simulation models: 1) a discrete event simulation framework using dummy data for future use when real data becomes available, and 2) a less robust simulation of daily trips using available delay data to estimate SafeTrack's effects. Various scenarios were tested by adjusting delay probabilities and severities. Results showed potential reductions in delay times and variations, though limitations exist due to data gaps. The project provides insight into SafeTrack's possible impacts while highlighting the need for improved data to develop more accurate models.
Traffic Congestion Prediction using Deep Reinforcement Learning in Vehicular ...IJCNCJournal
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1. Simulation Modelling
Final Project
UC Night Ride Simulation
Professor: David Kelton Created by: Rutuja Gangane
UC MS-BANA (M08655415)
Index
Introduction
Model Logic
1. Challenges Faced
2. Logic Used
3. Limitations and Assumptions
Data
4. Collection
5. Cleaning
6. Model Distribution Fitting
Scenarios Tested
7. Process Analyzer
Conclusions/Suggestions
2. Introduction
This Project is based on the night-time pick-and-drop shuttle service called Night Ride
provided by the University of Cincinnati Public Safety to its students. Typically, students can
use the Night Ride anywhere within 1 mile of campus as an on-call, free, pick-and-drop cab
service. The idea is to simulate the number of shuttles that operate and observe waiting time
for students and how to best optimize the model so that average waiting times are reduced.
ModelLogic
1. Challenges Faced
a. To use Transporter, Resource or Route: As the process/transfer times
were already given, transporter, which works on distance and velocity
inputs looked like an unlikely choice. Using a resource or a route with
delay would not create a request-based model, and will not be very
useful when simulating and analysing alternatives.
b. How to make the transporter “wait” till one of the entities is dropped
off and use the same transporter to move ahead with the rest of the
passengers. Since entities are picked up and dropped off altogether as a
single entity or a batch.
c. How to batch entities?
d. How to assign the transporter (logic)?
2. Logic Used
Create and Assign Logic:
The model starts from a Create module where entities are created (when night
ride receives the calls). The entities are assigned Pickup and drop-off
locations, and routed to their stations from where they are to be picked up.
Description of one station of the Model:
The next decide module decides if the entity has been using the transporter,
then to Free and Halt the transporter (block the transporter so that rest of the
passengers can be dropped off to further locations instead of releasing the
transporter and then requesting for another one).
We need to release the transporter because entities might be travelling in
batches from the same locations but might have different drop-off points. We
will use separate module to unbatch and release the entity whose drop-off
point is current station.
If it is a new entity with pickup as current station, then this step will be
skipped. Next step is the decide module which checks if current station is a
drop off point for any entity. Such entities are disposed.
Next condition of the decide module is to check if it is an entity being picked
up from the current location. If it is, then we count the passengers (this helps
in deciding the batch size of the transporter about to leave from station 2). We
then record current time and hold such entities for some time in order for them
3. to possibly form a batch, with other new passengers to be picked up from
same station or passengers passing through this station in a night ride.
The third condition of the Decide Module will have passengers passing
through this station in the Night ride (while dropping off some other
passenger), we will count these and send them to be batched again along with
any new passengers from same pickup location.
The Queue logic for this batch module will be entity that is created the first
(already travelling passengers) will be at the start of the queue. Maximum size
of the batch is Old Passengers+ New passengers, but not more than 6. If any
new passengers are remaining, they will be in the next batch, which then goes
on to request another transporter. Meanwhile, the batch having old passengers
and might be having new passengers goes ahead, the transporter is activated
(engine started) again and is requested by the batch. It takes some delay for the
passengers to get down/climb in, take a turn and the Night ride moves ahead to
its Next station. This is the station of the entity first created among all the
passengers in the NightRide.
A screenshot of the complete model:
4. 3. Limitations and Assumptions
Assumption 1: Every Transporter/ Night ride has a limited capacity of 6(by use of
batch size no more than 6). Specific transporters were not assigned specific
capacities.
Assumption 2: Only Six Stations, which are also clubbed from various different
actual pickup and drop-offs.
Data
4. Collection
The data was requested from UC NightRide Program. The data received was Date,
Van Number/Driver Names.,Number of People, Pickup Location, and Drop-off
location, Time of Call, Time of Pickup and Time of Drop.
5. Data Cleaning
This was a very complex model to simulate. To make it easier, I have chosen six
“Areas”,in which many of the locations were clubbed (which was not an easy task at
all!). Total data used from the 49,000 odd rows given in the dataset,was about
30,000. Most of the locations (although same) were spelled incorrectly multiple times.
It took a lot of time to get the data cleaned and grouped in six areas.
Location Grouping:
5. 6. Model Distribution Fitting:
The distributions were fitted on inter-arrival times as per given data. The service times
(and hence distance, velocities) were found by fitting the data in Input Analyzer. The
distribution of drop-off and pickups were also found from the data. Six distribution
An example of the service time data fitting:
6. 30 such distributions were made to check the inter-arrival times from one area to another.
Similarly distributions were fitted to inter-arrival times. Probability of choosing the
station was a discrete probability found from the data.
7. Process Analyzer:
Process Analyzer was used to check the three different scenarios. One was the Base
case with 8 Night rides. Second was the Model with 10 Night Rides. The final
scenario was when assignment of the Night ride/transporter was not cyclical but
based on smallest distance.
The best scenario was when Night Ride was assigned on the logic of smallest
distance, i.e. when new batches request for a night ride, the transporter that is closest
to request station should be dispatched. This decreases the Totalentity time in
system, however the times are not very significant, but the best scenario is with third
model.
7.
8. Although the smallest distance scenario might increase the maximum waiting time, but it reduces the
avg. time in system significantly.
Conclusions:
The logic for assigning the Night Ride should be based on smallest distance from Requesting
location and not Cyclical/Random.
(Here, Cyclical means that starting from 1, each transporter will be used one after the other
from all the free available transporters i.e. we will try to use all ofthe shuttles equal number of
times.)