This document discusses intelligent traffic light control using multi-agent reinforcement learning. It summarizes three research papers on the topic. The first paper proposes a distributed Q-learning approach that considers both motorized and non-motorized traffic to achieve near-global optimization. The second designs a two-stage negotiation system where traffic lights determine green times based on real-time traffic conditions. The third applies particle swarm optimization to find optimal light cycles for large vehicular networks under various scenarios.
Presentation of GreenYourMove's hybrid approach in the 3rd Conference on Sust...GreenYourMove
Β
The document summarizes a hybrid approach to solving the environmental multi-modal journey planning problem. It uses Dijkstra's algorithm to find the closest public transportation nodes to the starting and ending points, and then builds a mixed integer linear program (MILP) to compute the optimal journey between those nodes that minimizes both travel time and environmental costs. The proposed method provides a novel way to address the multi-criteria optimization challenge of journey planning across multiple transportation modes.
This document summarizes a presentation on a hybrid approach to journey planning that minimizes environmental impact. The approach uses Dijkstra's algorithm to find the closest public transport nodes to the start and end points, and then builds a mathematical model to compute the optimal journey between the nodes. The model is a mixed integer linear program that minimizes a weighted combination of travel time and environmental cost. The approach was developed for the GreenYourMove project, which aims to create a multi-modal transport planning app that provides the most environmentally friendly routes.
2019-2020 research findings in Public Transit from the Centre for Transport Studies, University of TWENTE. The presented findings at the Transportation Research board include overcrowding, operational control, electric buses, and train assignment.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
A MODIFIED ANT COLONY ALGORITHM FOR SOLVING THE UNIT COMMITMENT PROBLEMaeijjournal
Β
Solving the unit commitment (UC) problem is one of the most complicated issues in power systems that its
exact solving can be calculated by perfect counting of entire possible compounds of generative units. UC is
equated as a nonlinear optimization with huge size. Purpose of solving this problem is to programming the
optimization of the generative units to minimize the full action cost regarding problem constraints. In this
article, a modified version of ant colony optimization (MACO) is introduced for solving the UC problem in
a power system. ACO algorithm is a powerful optimization method which has the capability of fleeing from
local minimums by performing flexible memory system. The efficiency of proposed method in two power
system containing 4 and 10 generative units is indicated. Comparison of obtained results from the proposed
method with results of the past well-known methods is a proof for suitability of performing the introduced
algorithm in economic input and output of generative units.
Bi-objective Optimization Apply to Environment a land Economic Dispatch Probl...ijceronline
Β
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
Performance of Phase Congruency and Linear Feature Extraction for Satellite I...IOSR Journals
Β
This document summarizes research on extracting linear features from satellite images. It introduces using a phase congruency and linear feature extraction model combined with an adaptive smoothing algorithm. The paper aims to evaluate the advantages and limitations of this approach when applied to satellite image feature extraction. It also describes other common feature extraction methods, such as using mathematical morphology operations like dilation and erosion. Overall, the document reviews techniques for automated linear feature extraction from satellite imagery.
A comparative study of initial basic feasible solution methodsAlexander Decker
Β
This document compares three methods for finding an initial basic feasible solution for transportation problems: Vogel's Approximation Method (VAM), a Proposed Approximation Method (PAM), and a new Minimum Transportation Cost Method (MTCM). It presents the algorithms for each method and applies them to a sample transportation problem. The MTCM provides not only the minimum transportation cost but also an optimal solution, unlike VAM and PAM which sometimes only find a close to optimal solution. The document aims to evaluate which initial basic feasible solution method works best.
Presentation of GreenYourMove's hybrid approach in the 3rd Conference on Sust...GreenYourMove
Β
The document summarizes a hybrid approach to solving the environmental multi-modal journey planning problem. It uses Dijkstra's algorithm to find the closest public transportation nodes to the starting and ending points, and then builds a mixed integer linear program (MILP) to compute the optimal journey between those nodes that minimizes both travel time and environmental costs. The proposed method provides a novel way to address the multi-criteria optimization challenge of journey planning across multiple transportation modes.
This document summarizes a presentation on a hybrid approach to journey planning that minimizes environmental impact. The approach uses Dijkstra's algorithm to find the closest public transport nodes to the start and end points, and then builds a mathematical model to compute the optimal journey between the nodes. The model is a mixed integer linear program that minimizes a weighted combination of travel time and environmental cost. The approach was developed for the GreenYourMove project, which aims to create a multi-modal transport planning app that provides the most environmentally friendly routes.
2019-2020 research findings in Public Transit from the Centre for Transport Studies, University of TWENTE. The presented findings at the Transportation Research board include overcrowding, operational control, electric buses, and train assignment.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
A MODIFIED ANT COLONY ALGORITHM FOR SOLVING THE UNIT COMMITMENT PROBLEMaeijjournal
Β
Solving the unit commitment (UC) problem is one of the most complicated issues in power systems that its
exact solving can be calculated by perfect counting of entire possible compounds of generative units. UC is
equated as a nonlinear optimization with huge size. Purpose of solving this problem is to programming the
optimization of the generative units to minimize the full action cost regarding problem constraints. In this
article, a modified version of ant colony optimization (MACO) is introduced for solving the UC problem in
a power system. ACO algorithm is a powerful optimization method which has the capability of fleeing from
local minimums by performing flexible memory system. The efficiency of proposed method in two power
system containing 4 and 10 generative units is indicated. Comparison of obtained results from the proposed
method with results of the past well-known methods is a proof for suitability of performing the introduced
algorithm in economic input and output of generative units.
Bi-objective Optimization Apply to Environment a land Economic Dispatch Probl...ijceronline
Β
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
Performance of Phase Congruency and Linear Feature Extraction for Satellite I...IOSR Journals
Β
This document summarizes research on extracting linear features from satellite images. It introduces using a phase congruency and linear feature extraction model combined with an adaptive smoothing algorithm. The paper aims to evaluate the advantages and limitations of this approach when applied to satellite image feature extraction. It also describes other common feature extraction methods, such as using mathematical morphology operations like dilation and erosion. Overall, the document reviews techniques for automated linear feature extraction from satellite imagery.
A comparative study of initial basic feasible solution methodsAlexander Decker
Β
This document compares three methods for finding an initial basic feasible solution for transportation problems: Vogel's Approximation Method (VAM), a Proposed Approximation Method (PAM), and a new Minimum Transportation Cost Method (MTCM). It presents the algorithms for each method and applies them to a sample transportation problem. The MTCM provides not only the minimum transportation cost but also an optimal solution, unlike VAM and PAM which sometimes only find a close to optimal solution. The document aims to evaluate which initial basic feasible solution method works best.
IRJET- Road Recognition from Remote Sensing Imagery using Machine LearningIRJET Journal
Β
This document discusses a framework for recognizing roads from remote sensing imagery using machine learning. It proposes using a convolutional neural network (CNN) trained on large amounts of reference data to initially classify imagery into classification maps, then refining the CNN on a small amount of accurately labeled data. A series of experiments in MATLAB show this two-step training approach considers a large amount of context to provide fine-grained classification maps while addressing issues with imperfect training data. The document also reviews several existing methods for road extraction from remote sensing imagery and their limitations.
The document summarizes research on using ant colony optimization (ACO) supervised by particle swarm intelligence (PSI) to solve multi-objective vehicle routing problems. It proposes applying this approach to determine optimal routes on a linearly expanded network model. The ACO algorithm finds shortest paths between nodes while avoiding local optima, guided by PSI. Experimental results show the ACOLS-PSI algorithm improves average route distance by 8% compared to existing greedy algorithms. Future work could combine this approach with other shortest path methods into a memetic algorithm to better solve wide and sparse vehicle routing networks.
This document summarizes a study that uses Ant Colony Optimization (ACO) to optimize the capacity of a railway terminal station. The capacity problem is formulated as a Travelling Salesman Problem (TSP) where arrival/departure events are nodes and the schedule length is the tour length. The ACO algorithm is applied to find an optimal schedule that maximizes the number of trains departing per hour while satisfying constraints. Simulation results show ACO produces superior solutions to domain experts and validate formulating the capacity problem as a TSP. The study contributes an application of soft computing techniques to solve a combinatorial optimization problem in transportation planning.
KβMEANS CLUSTERING ANDSNAKES PATTERN USED FOR ROAD EXTRACTIONijiert bestjournal
Β
The road extraction from digital images or satellit e images has become topic to be dealt with in the recent past. Numerous methods have been di scovered such as semi automatic extraction of road as well as automatic extraction road. Now in this paper,we are proposing the method for extracting road from urban part as we ll as non urban part from an image.
International Journal of Computational Engineering Research(IJCER)ijceronline
Β
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
A New Method to Solving Generalized Fuzzy Transportation Problem-Harmonic Mea...AI Publications
Β
Transportation Problem is one of the models in the Linear Programming problem. The objective of this paper is to transport the item from the origin to the destination such that the transport cost should be minimized, and we should minimize the time of transportation. To achieve this, a new approach using harmonic mean method is proposed in this paper. In this proposed method transportation costs are represented by generalized trapezoidal fuzzy numbers. Further comparative studies of the new technique with other existing algorithms are established by means of sample problems.
Monitoring traffic in urban areas is an important task for intelligent transport applications to alleviate the traffic problems like traffic jams and long trip times. The traffic flow in urban areas is more complicated than the traffic flow in highway, due to the slow movement of vehicles and crowded traffic flows in urban areas. In this paper, a vehicle detection and classification system at intersections is proposed. The system consists of three main phases: vehicle detection, vehicle tracking and vehicle classification. In the vehicle detection, the background subtraction is utilized to detect the moving vehicles by employing mixture of Gaussians (MoGs) algorithm, and then the removal shadow algorithm is developed to improve the detection phase and eliminate the undesired detected region (shadows). After the vehicle detection phase, the vehicles are tracked until they reach the classification line. Then the vehicle dimensions are utilized to classify the vehicles into three classes (cars, bikes, and trucks). In this system, there are three counters; one counter for each class. When the vehicle is classified to a specific class, the class counter is incremented by one. The counting results can be used to estimate the traffic density at intersections, and adjust the timing of traffic light for the next light cycle. The system is applied to videos obtained by stationary cameras. The results obtained demonstrate the robustness and accuracy of the proposed system.
A New Method for Solving Transportation Problems Considering Average PenaltyIOSRJM
Β
Vogelβs Approximation Method (VAM) is one of the conventional methods that gives better Initial Basic Feasible Solution (IBFS) of a Transportation Problem (TP). This method considers the row penalty and column penalty of a Transportation Table (TT) which are the differences between the lowest and next lowest cost of each row and each column of the TT respectively. In a little bit different way, the current method consider the Average Row Penalty (ARP) and Average Column Penalty (ACP) which are the averages of the differences of cell values of each row and each column respectively from the lowest cell value of the corresponding row and column of the TT. Allocations of costs are started in the cell along the row or column which has the highest ARP or ACP. These cells are called basic cells. The details of the developed algorithm with some numerical illustrations are discussed in this article to show that it gives better solution than VAM and some other familiar methods in some cases.
Road surface classification based on LBP and GLCM features using kNN classifierjournalBEEI
Β
Autonomous Ground Vehicle (UGV) technology has shown a fast development this past year and proven to be useful. The use of UGV technology is restricted on a particular road condition. Classification of the road is an essential process in UGV, especially to control the autonomous vehicle. For example, the speed could be adjusted by referring to the road type, these process require a fast computational time. This research focuses on finding the most discriminant feature while keeping the number of features into a minimum to obtain fast computational time and accurate classification result. One can experiences difficulties because the condition of the road varies, this research proposes a combination of Gray Level Co-occurrence Matrix (GLCM) a statistical method to extract feature and Local Binary Pattern (LBP) feature to improve the robustness of the features. The kNN classifier is used to do the classification with the accuracy of 98% and 12 picture processed per second.
Forecasting electricity usage in industrial applications with gpu acceleratio...Conference Papers
Β
This document compares various exponential smoothing and ARIMA models to forecast electricity usage in an industrial setting using a short time series dataset. It finds that Holt linear trend and Holt linear damped trend models provide the most accurate forecasts for electricity usage in mill production of hammers and pellets based on having the lowest root mean square error values compared to actual usage. GPU acceleration via the RAPIDS framework is used to improve the training and forecasting speed of the models on the short dataset.
Improving transport in Malta using GIS and LBSMatthew Pulis
Β
A presentation prepared to the University of Malta as part of my MSc. Informatics. This seminar discusses ways and improvements how can a GIS driven system help and improve the current situation in Malta. This presentation also provides a survey discussing how the Maltese view the public transport and gives out interesting conclusions as to where the GIS needs to tackle. The study focuses mainly on ways as to where and how to improve the routes, promoting cultural places, buses ETA and taxi fleet handling amongst others.
Prediction of traveller information and route choiceayishairshad
Β
ayisha irshad ppt Subjected presentation is based on a research paper by
Afzal Ahmeda, Dong Ngoduya & David Watlinga
a Institute for Transport Studies, University of Leeds, 34β40
University Road, Leeds LS2 9JT, UK Published online: 10 Jun 2015.
Presentation given during the first transportation workshop at Melbourne Uni. Focus on crowd monitoring and management. With examples from various projects (SAIL, Mekka, etc.)
Rational polynomial coefficients (RPCs) are empirical mathematical models that relate image pixel coordinates to geographic coordinates. RPCs express normalized image row and column values as the ratio of polynomial functions of normalized latitude, longitude, and height values. The coefficients of the two rational polynomials for row and column are computed by satellite companies based on orbital position, sensor model, and other factors. RPC files contain these coefficients and are used to orthorectify satellite images by transforming image coordinates to geographic coordinates.
An IoT based Dynamic Traffic Signal ControlGauthamSK4
Β
Used Kerner three-phase traffic theory to establishing an Intelligent Traffic System that will provide automatic management of traffic lights based on the concept of the Internet of Things which will resolve the traffic jam issues which will in turn reduce CO2 emissions and also the mobility metrics like the travel time.
Help the Genetic Algorithm to Minimize the Urban Traffic on IntersectionsIJORCS
Β
This document summarizes a research paper that uses genetic algorithms to optimize traffic light timing at intersections to minimize traffic. It first describes modeling traffic light intersections using Petri nets. It then explains how genetic algorithms can be used for optimization by coding the problem variables in chromosomes, defining a fitness function to evaluate populations over generations, and using operators like mutation and crossover. The fitness function aims to minimize average traffic light cycle times based on 14 parameters related to light timing and vehicle wait times at two intersections. The genetic algorithm optimization of traffic light timing parameters is found to improve traffic flow at intersections.
Design of intelligent traffic light controller using gsm & embedded systemYakkali Kiran
Β
This document describes the design of an intelligent traffic light controller using an embedded system. The proposed system aims to make traffic light control more efficient by using sensor networks and embedded technology to dynamically determine light timings based on real-time traffic conditions. This allows the system to optimize traffic flow and reduce congestion compared to traditional fixed-time controllers. Key features include emergency vehicle detection and providing traffic information to drivers via GSM. The performance of the intelligent controller is evaluated against a conventional fixed-time controller based on metrics like waiting time, vehicle travel distance, and efficient emergency response.
A Computational Study Of Traffic Assignment AlgorithmsNicole Adams
Β
The document summarizes a study comparing algorithms for solving traffic assignment problems. It classified algorithms as link-based (using link flows), path-based (using path flows), or origin-based (using link flows from origins). It reviewed literature on algorithms like Frank-Wolfe (link-based), path equilibration (path-based), and origin-based algorithm. It chose to implement representative algorithms from each class: Frank-Wolfe, conjugate Frank-Wolfe, bi-conjugate Frank-Wolfe (link-based), path equilibration, gradient projection, projected gradient, improved social pressure (path-based), and Algorithm B (origin-based) to compare their performance on benchmark problems.
A Computational Study Of Traffic Assignment AlgorithmsAlicia Buske
Β
This document summarizes a research study that compares different algorithms for solving traffic assignment problems. The study performs a literature review of prominent traffic assignment algorithms, classifying them based on how the solution is represented (link-based, path-based, origin-based). It then implements representative algorithms from each class and conducts computational tests on benchmark networks of varying sizes. The results are analyzed to compare algorithm performance and identify the impact of different algorithm components on running time.
IRJET- Road Recognition from Remote Sensing Imagery using Machine LearningIRJET Journal
Β
This document discusses a framework for recognizing roads from remote sensing imagery using machine learning. It proposes using a convolutional neural network (CNN) trained on large amounts of reference data to initially classify imagery into classification maps, then refining the CNN on a small amount of accurately labeled data. A series of experiments in MATLAB show this two-step training approach considers a large amount of context to provide fine-grained classification maps while addressing issues with imperfect training data. The document also reviews several existing methods for road extraction from remote sensing imagery and their limitations.
The document summarizes research on using ant colony optimization (ACO) supervised by particle swarm intelligence (PSI) to solve multi-objective vehicle routing problems. It proposes applying this approach to determine optimal routes on a linearly expanded network model. The ACO algorithm finds shortest paths between nodes while avoiding local optima, guided by PSI. Experimental results show the ACOLS-PSI algorithm improves average route distance by 8% compared to existing greedy algorithms. Future work could combine this approach with other shortest path methods into a memetic algorithm to better solve wide and sparse vehicle routing networks.
This document summarizes a study that uses Ant Colony Optimization (ACO) to optimize the capacity of a railway terminal station. The capacity problem is formulated as a Travelling Salesman Problem (TSP) where arrival/departure events are nodes and the schedule length is the tour length. The ACO algorithm is applied to find an optimal schedule that maximizes the number of trains departing per hour while satisfying constraints. Simulation results show ACO produces superior solutions to domain experts and validate formulating the capacity problem as a TSP. The study contributes an application of soft computing techniques to solve a combinatorial optimization problem in transportation planning.
KβMEANS CLUSTERING ANDSNAKES PATTERN USED FOR ROAD EXTRACTIONijiert bestjournal
Β
The road extraction from digital images or satellit e images has become topic to be dealt with in the recent past. Numerous methods have been di scovered such as semi automatic extraction of road as well as automatic extraction road. Now in this paper,we are proposing the method for extracting road from urban part as we ll as non urban part from an image.
International Journal of Computational Engineering Research(IJCER)ijceronline
Β
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
A New Method to Solving Generalized Fuzzy Transportation Problem-Harmonic Mea...AI Publications
Β
Transportation Problem is one of the models in the Linear Programming problem. The objective of this paper is to transport the item from the origin to the destination such that the transport cost should be minimized, and we should minimize the time of transportation. To achieve this, a new approach using harmonic mean method is proposed in this paper. In this proposed method transportation costs are represented by generalized trapezoidal fuzzy numbers. Further comparative studies of the new technique with other existing algorithms are established by means of sample problems.
Monitoring traffic in urban areas is an important task for intelligent transport applications to alleviate the traffic problems like traffic jams and long trip times. The traffic flow in urban areas is more complicated than the traffic flow in highway, due to the slow movement of vehicles and crowded traffic flows in urban areas. In this paper, a vehicle detection and classification system at intersections is proposed. The system consists of three main phases: vehicle detection, vehicle tracking and vehicle classification. In the vehicle detection, the background subtraction is utilized to detect the moving vehicles by employing mixture of Gaussians (MoGs) algorithm, and then the removal shadow algorithm is developed to improve the detection phase and eliminate the undesired detected region (shadows). After the vehicle detection phase, the vehicles are tracked until they reach the classification line. Then the vehicle dimensions are utilized to classify the vehicles into three classes (cars, bikes, and trucks). In this system, there are three counters; one counter for each class. When the vehicle is classified to a specific class, the class counter is incremented by one. The counting results can be used to estimate the traffic density at intersections, and adjust the timing of traffic light for the next light cycle. The system is applied to videos obtained by stationary cameras. The results obtained demonstrate the robustness and accuracy of the proposed system.
A New Method for Solving Transportation Problems Considering Average PenaltyIOSRJM
Β
Vogelβs Approximation Method (VAM) is one of the conventional methods that gives better Initial Basic Feasible Solution (IBFS) of a Transportation Problem (TP). This method considers the row penalty and column penalty of a Transportation Table (TT) which are the differences between the lowest and next lowest cost of each row and each column of the TT respectively. In a little bit different way, the current method consider the Average Row Penalty (ARP) and Average Column Penalty (ACP) which are the averages of the differences of cell values of each row and each column respectively from the lowest cell value of the corresponding row and column of the TT. Allocations of costs are started in the cell along the row or column which has the highest ARP or ACP. These cells are called basic cells. The details of the developed algorithm with some numerical illustrations are discussed in this article to show that it gives better solution than VAM and some other familiar methods in some cases.
Road surface classification based on LBP and GLCM features using kNN classifierjournalBEEI
Β
Autonomous Ground Vehicle (UGV) technology has shown a fast development this past year and proven to be useful. The use of UGV technology is restricted on a particular road condition. Classification of the road is an essential process in UGV, especially to control the autonomous vehicle. For example, the speed could be adjusted by referring to the road type, these process require a fast computational time. This research focuses on finding the most discriminant feature while keeping the number of features into a minimum to obtain fast computational time and accurate classification result. One can experiences difficulties because the condition of the road varies, this research proposes a combination of Gray Level Co-occurrence Matrix (GLCM) a statistical method to extract feature and Local Binary Pattern (LBP) feature to improve the robustness of the features. The kNN classifier is used to do the classification with the accuracy of 98% and 12 picture processed per second.
Forecasting electricity usage in industrial applications with gpu acceleratio...Conference Papers
Β
This document compares various exponential smoothing and ARIMA models to forecast electricity usage in an industrial setting using a short time series dataset. It finds that Holt linear trend and Holt linear damped trend models provide the most accurate forecasts for electricity usage in mill production of hammers and pellets based on having the lowest root mean square error values compared to actual usage. GPU acceleration via the RAPIDS framework is used to improve the training and forecasting speed of the models on the short dataset.
Improving transport in Malta using GIS and LBSMatthew Pulis
Β
A presentation prepared to the University of Malta as part of my MSc. Informatics. This seminar discusses ways and improvements how can a GIS driven system help and improve the current situation in Malta. This presentation also provides a survey discussing how the Maltese view the public transport and gives out interesting conclusions as to where the GIS needs to tackle. The study focuses mainly on ways as to where and how to improve the routes, promoting cultural places, buses ETA and taxi fleet handling amongst others.
Prediction of traveller information and route choiceayishairshad
Β
ayisha irshad ppt Subjected presentation is based on a research paper by
Afzal Ahmeda, Dong Ngoduya & David Watlinga
a Institute for Transport Studies, University of Leeds, 34β40
University Road, Leeds LS2 9JT, UK Published online: 10 Jun 2015.
Presentation given during the first transportation workshop at Melbourne Uni. Focus on crowd monitoring and management. With examples from various projects (SAIL, Mekka, etc.)
Rational polynomial coefficients (RPCs) are empirical mathematical models that relate image pixel coordinates to geographic coordinates. RPCs express normalized image row and column values as the ratio of polynomial functions of normalized latitude, longitude, and height values. The coefficients of the two rational polynomials for row and column are computed by satellite companies based on orbital position, sensor model, and other factors. RPC files contain these coefficients and are used to orthorectify satellite images by transforming image coordinates to geographic coordinates.
An IoT based Dynamic Traffic Signal ControlGauthamSK4
Β
Used Kerner three-phase traffic theory to establishing an Intelligent Traffic System that will provide automatic management of traffic lights based on the concept of the Internet of Things which will resolve the traffic jam issues which will in turn reduce CO2 emissions and also the mobility metrics like the travel time.
Help the Genetic Algorithm to Minimize the Urban Traffic on IntersectionsIJORCS
Β
This document summarizes a research paper that uses genetic algorithms to optimize traffic light timing at intersections to minimize traffic. It first describes modeling traffic light intersections using Petri nets. It then explains how genetic algorithms can be used for optimization by coding the problem variables in chromosomes, defining a fitness function to evaluate populations over generations, and using operators like mutation and crossover. The fitness function aims to minimize average traffic light cycle times based on 14 parameters related to light timing and vehicle wait times at two intersections. The genetic algorithm optimization of traffic light timing parameters is found to improve traffic flow at intersections.
Design of intelligent traffic light controller using gsm & embedded systemYakkali Kiran
Β
This document describes the design of an intelligent traffic light controller using an embedded system. The proposed system aims to make traffic light control more efficient by using sensor networks and embedded technology to dynamically determine light timings based on real-time traffic conditions. This allows the system to optimize traffic flow and reduce congestion compared to traditional fixed-time controllers. Key features include emergency vehicle detection and providing traffic information to drivers via GSM. The performance of the intelligent controller is evaluated against a conventional fixed-time controller based on metrics like waiting time, vehicle travel distance, and efficient emergency response.
A Computational Study Of Traffic Assignment AlgorithmsNicole Adams
Β
The document summarizes a study comparing algorithms for solving traffic assignment problems. It classified algorithms as link-based (using link flows), path-based (using path flows), or origin-based (using link flows from origins). It reviewed literature on algorithms like Frank-Wolfe (link-based), path equilibration (path-based), and origin-based algorithm. It chose to implement representative algorithms from each class: Frank-Wolfe, conjugate Frank-Wolfe, bi-conjugate Frank-Wolfe (link-based), path equilibration, gradient projection, projected gradient, improved social pressure (path-based), and Algorithm B (origin-based) to compare their performance on benchmark problems.
A Computational Study Of Traffic Assignment AlgorithmsAlicia Buske
Β
This document summarizes a research study that compares different algorithms for solving traffic assignment problems. The study performs a literature review of prominent traffic assignment algorithms, classifying them based on how the solution is represented (link-based, path-based, origin-based). It then implements representative algorithms from each class and conducts computational tests on benchmark networks of varying sizes. The results are analyzed to compare algorithm performance and identify the impact of different algorithm components on running time.
This document presents a study on developing an artificial intelligence system to manage real-time traffic. The study developed a traffic simulator using Python to model vehicle and traffic light behavior at an intersection. A linear regression model was then used to control the traffic lights dynamically based on current traffic conditions, collected from sensors. Testing showed the AI-based dynamic system improved traffic flow compared to a static traffic light system, allowing more vehicles to pass through the intersection in a given time period. The authors conclude the linear regression model provides better real-time traffic management than existing approaches and suggest further improving it with deep learning techniques.
A Robust Algorithm To Solve The Signal Setting Problem Considering Different ...Joshua Gorinson
Β
This paper presents an algorithm to optimize traffic signal settings that considers the interaction between signal timing and traffic assignment. The algorithm iteratively updates signal timings based on fixed traffic flows, and then updates traffic flows based on the new signal timings, with the goal of minimizing total delay. Two different traffic assignment approaches are considered: user equilibrium assignment and a platoon simulation model. The proposed algorithm is compared to other optimization methods on a real traffic network, demonstrating its robustness in handling different assignment approaches.
This document describes a proposed two-stage traffic light system using fuzzy logic to minimize vehicle delay at intersections. The system has two modules: a traffic urgency decision module that selects the next phase to turn green based on traffic urgency, and an extension time decision module that determines how long to extend the green light phase based on vehicle numbers. Software was developed in MATLAB to simulate this system at an isolated intersection and evaluate its performance using average vehicle delay. The document reviews other related works applying fuzzy logic to traffic light control and adaptive signal systems.
Urban Bus Route Planning Using Reverse Labeling Dijkstra Algorithm for Tempor...IRJET Journal
Β
The document discusses using the Reverse Labeling Dijkstra Algorithm (RLDA) to optimize urban bus route planning by considering real-time traffic conditions. RLDA is an adaptation of Dijkstra's algorithm that finds the shortest path from the destination node backwards towards the source node. This allows it to consider arc attributes representing traffic congestion levels. The document presents the mathematical model and steps of the RLDA algorithm. It then discusses simulating the RLDA approach on a real-time road network to determine efficient bus routes.
With the development of the urbanization, industrialization and populace, there has been a huge development in the rush hour gridlock. With development in the rush hour gridlock, there got a heap of issues with it as well, these issues incorporate congested roads, mishaps and movement govern infringement at the overwhelming activity signals. This thusly adversy affects the economy of the nation and in addition the loss of lives. Thus, Speed control is in the need of great importance because of the expanded rate of mishaps announced in our everyday life. The criminal traffic offense expanded due to over movement on streets. The reason is rapid of vehicles. The speed of the vehicles is past the normal speed confine is called speed infringement. In this paper diverse issues are confronted that are given in issue detailing. Every one of these issues are in future with the assistance of the fortification learning issue and advancement issue the changed neural system is contemplated with NN calculations forward Chaining back spread . Omesh Goyal | Chamkour Singh ""A Review on Traffic Signal Identification"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-4 , June 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23557.pdf
Paper URL: https://www.ijtsrd.com/engineering/computer-engineering/23557/a-review-on-traffic-signal-identification/omesh-goyal
This document summarizes the application of computational intelligence techniques like genetic algorithms and particle swarm optimization for solving economic load dispatch problems. It first applies a real-coded genetic algorithm to minimize generation costs for a 6-generator test system with continuous fuel cost equations, showing superiority over quadratic programming. It then uses particle swarm optimization to minimize costs for a 10-generator system with each generator having discontinuous fuel options, showing better results than other published methods. The document provides background on economic load dispatch problems and optimization techniques like quadratic programming, genetic algorithms, and particle swarm optimization.
Simulation analysis Halmstad University 2013_projectAlexandru Gutu
Β
This document summarizes a study that uses computer simulation to compare a proposed roundabout intersection to the current intersection with traffic lights on Kristian IVs vΓ€g. Data on traffic patterns were collected from the current intersection, including the number of cars per lane during peak/off-peak hours, inter-arrival times of cars, and wait times at the traffic lights. The simulation will analyze average and maximum wait times at the roundabout versus traffic lights to determine if the roundabout provides more effective traffic flow. Objectives are to minimize average and maximum crossing times. Key decision variables are roundabout diameter, car speed, and slot size, while traffic patterns and maximum constraints are uncontrolled.
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Comp prese (1)
1. Intelligent Traffic Light Control
Mohamed Khasawneh
CIISE, Concordia University
PhD Comprehensive Exam
1
2. Outline
1. Introduction
2. Context
3. Papers
1. Intelligent Traffic Light Control Using Distributed Multi-agent Q Learning
2. Intelligent Traffic Lights: Green Time Period Negotiation
3. Optimal Cycle Program of Traffic Lights with Particle Swarm Optimization
4. Conclusions
5. Timeline
6. Future works
2
3. 1. Introduction
β’ Nowadays, studying Intelligent Transport Systems (ITS) is considered to be one of the hottest
topics which can improve the quality of life in many ways. Designing a good ITS system can bring
us many benefits such as saving lives by reducing the percentage of accidents, increasing the
safety for pedestrians, reducing the waiting times for both vehicles and pedestrians, optimizing
fuel consumption, reducing the emissions of CO and CO2 gases, reducing the noise pollution, etc.
3
4. 2. Context
β’ Smart cities
β’ Traffic Congestion management problem
β’ Intelligent Traffic signaling problem
β’ Connected vehicles
4
5. Challenges
β’ There are many challenges in designing these systems:
ο±The lack of global view of traffic conditions at the neighboring streets or intersections.
ο±The unexpected change in traffic condition at any given time.
ο±The high congestion status which makes it a hard and daunting task to accomplish
optimization between vehicles and pedestrian in a fair and satisfying manner.
5
6. Papers under study
1. Intelligent Traffic Light Control Using Distributed Multi-agent Q Learning.
2. Intelligent Traffic Lights: Green Time Period Negotiation.
3. Optimal Cycle Program of Traffic Lights with Particle Swarm Optimization.
6
8. Problem Definition
β’ Designing Intelligent traffic light control, where two main aspects are not considered in the
previous studies:
ο±Non motorized traffic.
ο±Global optimization.
8
10. Proposed System Design
β’ The proposed system design is based on the usage of Q learning which falls under the
reinforcement learning techniques.
β’ Q learning technique is composed of two main phases which are exploration and exploitation
phases.
β’ In exploration phase, the Q learning agent is randomly exploring the environment around, after
the environment is learned an exploitation phase is starting with an objective to maximize the
rewards in the whole system.
10
14. Used parameters
Parameters Meaning
πΊπ,π
π The state at intersection i, at day d and time t.
π ππ,π
π
, π ππ,π
π
, β¦ , πππ,π
π The queue length from intersection j to intersection i, at
day d and time t
π ππ,π ,π³
π
, π ππ,π ,πΉ
π The queue length for pedestrians at the left side from
intersection j to i, at day d and time t,
πΉπ,π
π The reward at intersection i, at day d and time
πΎ π,π
π The weight to present the local vehicular queues at
intersection I
πΎ π,π
π The weight to present the neighborhood vehicular
queues at the neighbors of intersection I
πΎ π,π
π Weight to present the total pedestrian queues at
intersection I
ππ,π
π The action at intersection i, at day d and time t
14Table 1. Used Parameters
15. Results
Fig. 4: Total cumulated queue lengths for vehicles and
pedestrians in the case of high pedestrian rate
Fig. 5: Total waiting and traveling time for vehicles and
pedestrians in the case of high pedestrian rate
15
16. Results
Fig. 6: CO2 and CO emission of vehicles in the case
of high pedestrian rate
Fig. 7: Fuel consumption and noise pollution of vehicles in the
case of high pedestrian rate
Fig. 8: Total cumulated queue lengths for vehicles and
pedestrians in the case of medium pedestrian rate
Fig. 9: Total waiting and traveling time for vehicles and
pedestrians in the case of medium pedestrian rate
16
17. Conclusion
β’ Unlike other papers in the literature, one of the main contributions of this paper is handling both
motorized and non-motorized vehicles. Non-motorized traffic such as pedestrian and bicyclist are
also an important part of the whole system and should not be overlooked in order to achieve the
fairness among all users. Other contribution is the achievement of near global optimal solution by
gathering/exchanging all the information from/to local and neighboring intersections, this helps
in a better and more accurate decisions compared with other proposed algorithms in literature
which are either using fixed-time or dynamic control approach
17
19. Problem Definition
β’ Designing Intelligent traffic light control, where two main aspects are not considered in the
previous studies:
ο±Person-based.
ο±The distribution of green time period between the traffic streams.
19
20. Literature Review
20
Author Approach
Dresner and Stone (2005) (2009) βreservation basedβ strategy
Vasirani and Ossowski(2009) (2011) βmarket basedβ strategy and extended
the work done by Dresner and Stone
Schepperle and Bohm(2008) this strategy will take in account
valuation of the driver
Table 2. Literature Review
21. Proposed System Design
β’ A multi-agent computerized system (MAS or "self-organized system") composed of multiple
interacting intelligent agents is proposed. Two stages are involved:
οΆFirst stage : try to find a traffic plan through defining the phase and determining green time
periods
οΆSecond stage: include two decisions defining the time of terminating the current stage and
defining the next stage to be implemented.
21
25. Case study
Variables values
Simulation Time 2 hours
The inter-green time 5s (3s yellow, 2s red).
The minimum green duration 7 second
The maximum planned cycle length 120 second
the maximum acceptable degree of saturation 80%
The saturation flows for straight-ahead
movements, right-turn movement and left-turn
movements
1800, 1600 and 1700 veh/h/lane, respectively.
average vehicle occupancy 1.2
Simulation Software AIMSUN
Table 3. Simulation Parameters
25
26. Result
Fig. 5 β Average travel time of each replication versus time: a) baseline; b) proposed
26
27. Conclusions
β’ This paper unveiled a person-oriented traffic signal control method for secluded intersection that
include signal timing optimization and concurrent signal plan designs with actuated-time data
about the dynamics of the network.
β’ The suggested control mechanism operates within two phases: first, it chooses traffic signals and
second, it undertakes negotiation of green time duration based on actual traffic situations. The
suggested control is assessed using a tiny traffic simulation tool.
27
29. Problem definition
β’ Designing Intelligent traffic light control, where three main aspects are not considered in the
previous studies:
ο±Large vehicular networks.
ο±Many scenarios.
ο±Comparison with other algorithms.
29
30. Literature Review
Mathematic models Fuzzy logic approaches Biologically inspired optimizers
οMcCrea and Moutari[5]( merged
knowledge-founded and continuous
calculus-founded models with the
intent of defining the road networksβ
traffic flow)
ο Tolba et al.( offered a Petri net-
based model to indicate traffic flow,
from both macroscopic and
macroscopic perceptions,
consideration of vehiclesβ individual
trajectories and observation of
global variables, respectively
οLim et al. [4]( derived a fuzzy logic
controller during a single
intersectionβs real-time local
optimization)
ο Other experts [3] developed a traffic
simulator utilizing fuzzy logic agents
for traffic lights at isolated
intersections.
Cellular automata:
ο Brockfeld et al. [6] utilized a CA
model whereby the city network
was instigated as a simple square
having some normal streets and
four junctions.
Neural networks (NN):
ο Spall and Chin [1] illustrated an NN
considered in configuring traffic
lightsβ control parameters.
Genetics Algorithm
30
Table 4.Literature Review(Paper 2)
31. Solution Approach
β’ PSO has been considered in [7] and [8] as a population-based meta-heuristic motivated by the
birdsβ social behavior within a flock, whose initial purpose was to continually optimize problems.
β’ In this model, initial solutions are randomly generated .each solutions to the problem is
identified as particle locations while the particlesβ population is known as swarm. For this
algorithm, xi, the position of each particle, is updated for each iteration g as follows:
where term π£ π+1
π
is the velocity of the particle, given by:
31
π π+1
π
= π π
π + π£ π+1
π
π£ π+1
π
= π€. π£π
π
+U[0,π1]. (ππ
π
β π₯ π
π
)+U[0,π2]. π π β π₯ π
π
.
33. PSO for Traffic Light Scheduling
The optimization solver proposed in this paper for optimal cycle programs for traffic light involves three
steps which are :
οΆSolution Encoding: all traffic light logics with their phase durations are mapped into each solution and
those solutions are randomly generated by PSO
οΆThe fitness function :Each solution is evaluated, taking into account the information obtained from
events that occur during simulation by the following equation:
F(s)= π£=0
π π π£ π + π£=0
π+πΆ π€ π£ π +(πΆ π .ππ‘)
π2 π +πΆπ
οΆThe global optimization procedure: the optimization strategy is composed into two parts :
οan optimization algorithm
οa simulation procedure
33
36. Papers comparison
Weakness Strengths
Paper 1 o Vehicle-based strategy
o does not react well to a sharp
changes in traffic patterns
o Global optimization
o Motorized and non-motorized traffic
Paper 2 o The proposed mechanism is
only admissible where the
demand of pedestrians is low.
o Person-based strategy
o the system is flexible
Paper 3 o priority for motorized traffic. o unlimited vehicular networks
o various scenario topologies
o Compared with four algorithms
36
Table 6. Papers comparison
37. Conclusions
β’ In this report, research papers tackling the efficient design of intelligent traffic light are discussed.
Considering different parameters to reach a different objective function.
β’ In [2], both motorized and non-motorized vehicles are taken into account to minimize the overall
congestion and waiting time. Whereas, in [9] an auction based intersection control strategy is
proposed with the objective function of minimizing the waiting time for each person by taking
into consideration the number of persons in each car.
β’ On the other hand, in another paper used an optimization method called Particle Swarm
Optimization (PSO) to find successful traffic light cycle programs with two main objectives which
are the number of vehicles arriving at the destination and the total travel time.
37
39. Future works
β’ Designing a person based globally optimal intelligent traffic light which fairly considers many
parameters (motorized and non-motorized traffic).
β’ Designing a machine learning-based algorithm to predict traffic congestion and signal length
timings based on historical information
39
40. References
[1] Y. Feng, K. L. Head, S. Khoshmagham, and M. Zamanipour, βA real-time adaptive signal control
in a connected vehicle environment,Transportation Research Part C: Emerging Technologies, vol. 55,
pp. 460β473, 2015.
[2] Ying Liu1, 2, Lei Liu1, Wei-Peng Chen1,Intelligent Traffic Light Control Using Distributed Multi-
Agent Q Learning, Fujitsu Laboratories of America, Inc., Sunnyvale, CA, USA
[3] TimΓ³teo, I., AraΓΊjo, M., Rossetti, R. & Oliveira, E. 2010. TraSMAPI: An API oriented towards Multi-
Agent Systems real-time interaction with multiple Traffic Simulators. In: 13th Intern. IEEE
Conference on Intelligent Transportation Systems (ITSC), Funchal, pp. 1183-1188.
[4] Dion, F., Rakha, H. & Kang, Y.-S. 2004. Comparison of delay estimates at under-saturated and
over-saturated pre-timed signalized intersections. Transportation Research Part B: Methodological,
38(2):99-122.
[5] J. McCrea and S. Moutari, βA hybrid macroscopic-based model for traffic flow in road
networks,Eur. J. Oper. Res., vol. 207, no. 1, pp. 676β684, 2010.
40