Traffic Signal

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Dipti Srinivasan - One of the best experts on this subject based on the ideXlab platform.

  • multi agent system in urban Traffic Signal control
    IEEE Computational Intelligence Magazine, 2010
    Co-Authors: P G Balaji, Dipti Srinivasan
    Abstract:

    Multi-agent system is a rapidly developing field of distributed artificial intelligence that has gained significant importance because of its ability to solve complex real-world problems. It provides a highly flexible and modular structure, which incorporates the domain expertise in the system, to achieve the optimal solution. Multi-agent system also allows a problem to be divided into smaller sub-problems that require less domain expertise compared to solving the problem as a whole. In recent years, multi-agent system has gained significant attention in solving Traffic Signal control problems because of the advantages it offers in solving complex problems with uncertainties. In this paper, two different types of multi-agent architectures that have been implemented on a simulated complex urban Traffic network in Singapore for adaptive intelligent Signal control are discussed. The results obtained indicate the superior performance of the multi-agent Signal controller in comparison to pre-timed and Signal control methods which are currently in use.

  • urban Traffic Signal control using reinforcement learning agents
    Iet Intelligent Transport Systems, 2010
    Co-Authors: P G Balaji, X German, Dipti Srinivasan
    Abstract:

    This study presents a distributed multi-agent-based Traffic Signal control for optimising green timing in an urban arterial road network to reduce the total travel time and delay experienced by vehicles. The proposed multi-agent architecture uses Traffic data collected by sensors at each intersection, stored historical Traffic patterns and data communicated from agents in adjacent intersections to compute green time for a phase. The parameters like weights, threshold values used in computing the green time is fine tuned by online reinforcement learning with an objective to reduce overall delay. PARAMICS software was used as a platform to simulate 29 Signalised intersection at Central Business District of Singapore and test the performance of proposed multi-agent Traffic Signal control for different Traffic scenarios. The proposed multi-agent reinforcement learning (RLA) Signal control showed significant improvement in mean time delay and speed in comparison to other Traffic control system like hierarchical multi-agent system (HMS), cooperative ensemble (CE) and actuated control.

  • neural networks for real time Traffic Signal control
    IEEE Transactions on Intelligent Transportation Systems, 2006
    Co-Authors: Dipti Srinivasan, Min Chee Choy, Ruey Long Cheu
    Abstract:

    Real-time Traffic Signal control is an integral part of the urban Traffic control system, and providing effective real-time Traffic Signal control for a large complex Traffic network is an extremely challenging distributed control problem. This paper adopts the multiagent system approach to develop distributed unsupervised Traffic responsive Signal control models, where each agent in the system is a local Traffic Signal controller for one intersection in the Traffic network. The first multiagent system is developed using hybrid computational intelligent techniques. Each agent employs a multistage online learning process to update and adapt its knowledge base and decision-making mechanism. The second multiagent system is developed by integrating the simultaneous perturbation stochastic approximation theorem in fuzzy neural networks (NN). The problem of real-time Traffic Signal control is especially challenging if the agents are used for an infinite horizon problem, where online learning has to take place continuously once the agent-based Traffic Signal controllers are implemented into the Traffic network. A comprehensive simulation model of a section of the Central Business District of Singapore has been developed using PARAMICS microscopic simulation program. Simulation results show that the hybrid multiagent system provides significant improvement in Traffic conditions when evaluated against an existing Traffic Signal control algorithm as well as the SPSA-NN-based multiagent system as the complexity of the simulation scenario increases. Using the hybrid NN-based multiagent system, the mean delay of each vehicle was reduced by 78% and the mean stoppage time, by 85% compared to the existing Traffic Signal control algorithm. The promising results demonstrate the efficacy of the hybrid NN-based multiagent system in solving large-scale Traffic Signal control problems in a distributed manner

  • cooperative hybrid agent architecture for real time Traffic Signal control
    Systems Man and Cybernetics, 2003
    Co-Authors: Min Chee Choy, Dipti Srinivasan, Ruey Long Cheu
    Abstract:

    This paper presents a new hybrid, synergistic approach in applying computational intelligence concepts to implement a cooperative, hierarchical, multiagent system for real-time Traffic Signal control of a complex Traffic network. The large-scale Traffic Signal control problem is divided into various subproblems, and each subproblem is handled by an intelligent agent with a fuzzy neural decision-making module. The decisions made by lower-level agents are mediated by their respective higher-level agents. Through adopting a cooperative distributed problem solving approach, coordinated control by the agents is achieved. In order for the multiagent architecture to adapt itself continuously to the dynamically changing problem domain, a multistage online learning process for each agent is implemented involving reinforcement learning, learning rate and weight adjustment as well as dynamic update of fuzzy relations using an evolutionary algorithm. The test bed used for this research is a section of the Central Business District of Singapore. The performance of the proposed multiagent architecture is evaluated against the set of Signal plans used by the current real-time adaptive Traffic control system. The multiagent architecture produces significant improvements in the conditions of the Traffic network, reducing the total mean delay by 40% and total vehicle stoppage time by 50%.

Peter Koonce - One of the best experts on this subject based on the ideXlab platform.

Walid Gomaa - One of the best experts on this subject based on the ideXlab platform.

  • adaptive multi objective reinforcement learning with hybrid exploration for Traffic Signal control based on cooperative multi agent framework
    Engineering Applications of Artificial Intelligence, 2014
    Co-Authors: Mohamed A Khamis, Walid Gomaa
    Abstract:

    In this paper, we focus on computing a consistent Traffic Signal configuration at each junction that optimizes multiple performance indices, i.e., multi-objective Traffic Signal control. The multi-objective function includes minimizing trip waiting time, total trip time, and junction waiting time. Moreover, the multi-objective function includes maximizing flow rate, satisfying green waves for platoons traveling in main roads, avoiding accidents especially in residential areas, and forcing vehicles to move within moderate speed range of minimum fuel consumption. In particular, we formulate our multi-objective Traffic Signal control as a multi-agent system (MAS). Traffic Signal controllers have a distributed nature in which each Traffic Signal agent acts individually and possibly cooperatively in a MAS. In addition, agents act autonomously according to the current Traffic situation without any human intervention. Thus, we develop a multi-agent multi-objective reinforcement learning (RL) Traffic Signal control framework that simulates the driver's behavior (acceleration/deceleration) continuously in space and time dimensions. The proposed framework is based on a multi-objective sequential decision making process whose parameters are estimated based on the Bayesian interpretation of probability. Using this interpretation together with a novel adaptive cooperative exploration technique, the proposed Traffic Signal controller can make real-time adaptation in the sense that it responds effectively to the changing road dynamics. These road dynamics are simulated by the Green Light District (GLD) vehicle Traffic simulator that is the testbed of our Traffic Signal control. We have implemented the Intelligent Driver Model (IDM) acceleration model in the GLD Traffic simulator. The change in road conditions is modeled by varying the Traffic demand probability distribution and adapting the IDM parameters to the adverse weather conditions. Under the congested and free Traffic situations, the proposed multi-objective controller significantly outperforms the underlying single objective controller which only minimizes the trip waiting time (i.e., the total waiting time in the whole vehicle trip rather than at a specific junction). For instance, the average trip and waiting times are ~8 and 6 times lower respectively when using the multi-objective controller.

Tingting Huang - One of the best experts on this subject based on the ideXlab platform.

P G Balaji - One of the best experts on this subject based on the ideXlab platform.

  • multi agent system in urban Traffic Signal control
    IEEE Computational Intelligence Magazine, 2010
    Co-Authors: P G Balaji, Dipti Srinivasan
    Abstract:

    Multi-agent system is a rapidly developing field of distributed artificial intelligence that has gained significant importance because of its ability to solve complex real-world problems. It provides a highly flexible and modular structure, which incorporates the domain expertise in the system, to achieve the optimal solution. Multi-agent system also allows a problem to be divided into smaller sub-problems that require less domain expertise compared to solving the problem as a whole. In recent years, multi-agent system has gained significant attention in solving Traffic Signal control problems because of the advantages it offers in solving complex problems with uncertainties. In this paper, two different types of multi-agent architectures that have been implemented on a simulated complex urban Traffic network in Singapore for adaptive intelligent Signal control are discussed. The results obtained indicate the superior performance of the multi-agent Signal controller in comparison to pre-timed and Signal control methods which are currently in use.

  • urban Traffic Signal control using reinforcement learning agents
    Iet Intelligent Transport Systems, 2010
    Co-Authors: P G Balaji, X German, Dipti Srinivasan
    Abstract:

    This study presents a distributed multi-agent-based Traffic Signal control for optimising green timing in an urban arterial road network to reduce the total travel time and delay experienced by vehicles. The proposed multi-agent architecture uses Traffic data collected by sensors at each intersection, stored historical Traffic patterns and data communicated from agents in adjacent intersections to compute green time for a phase. The parameters like weights, threshold values used in computing the green time is fine tuned by online reinforcement learning with an objective to reduce overall delay. PARAMICS software was used as a platform to simulate 29 Signalised intersection at Central Business District of Singapore and test the performance of proposed multi-agent Traffic Signal control for different Traffic scenarios. The proposed multi-agent reinforcement learning (RLA) Signal control showed significant improvement in mean time delay and speed in comparison to other Traffic control system like hierarchical multi-agent system (HMS), cooperative ensemble (CE) and actuated control.

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