The Experts below are selected from a list of 180 Experts worldwide ranked by ideXlab platform

Chaozhe Jiang - One of the best experts on this subject based on the ideXlab platform.

  • nonlinear decision rule approach for real time Traffic signal control for congestion and emission mitigation
    Networks and Spatial Economics, 2020
    Co-Authors: Junwoo Song, Ke Han, Chaozhe Jiang
    Abstract:

    We propose a real-time signal control framework based on a nonlinear decision rule (NDR), which defines a nonlinear mapping between network states and signal control parameters to actual signal controls based on prevailing Traffic conditions, and such a mapping is optimized via off-line simulation. The NDR is instantiated with two neural networks: feedforward neural network (FFNN) and recurrent neural network (RNN), which have different ways of Processing Traffic information in the near past, and are compared in terms of their performances. The NDR is implemented within a microscopic Traffic simulation (S-Paramics) for a real-world network in West Glasgow, where the off-line training of the NDR amounts to a simulation-based optimization aiming to reduce delay, CO2 and black carbon emissions. The emission calculations are based on the high-fidelity vehicle dynamics generated by the simulation, and the AIRE instantaneous emission model. Extensive tests are performed to assess the NDR framework, not only in terms of its effectiveness in reducing the aforementioned objectives, but also in relation to local vs. global benefits, trade-off between delay and emissions, impact of sensor locations, and different levels of network saturation. The results suggest that the NDR is an effective, flexible and robust way of alleviating congestion and reducing Traffic emissions.

  • nonlinear decision rule approach for real time Traffic signal control for congestion and emission reductions
    arXiv: Optimization and Control, 2018
    Co-Authors: Junwoo Song, Ke Han, Chaozhe Jiang
    Abstract:

    We propose a real-time signal control framework based on a nonlinear decision rule (NDR), which defines a nonlinear mapping between network states and signal control parameters to actual signal controls based on prevailing Traffic conditions, and such a mapping is optimized via off-line simulation. The NDR is instantiated with two neural networks: feedforward neural network (FFNN) and recurrent neural network (RNN), which have different ways of Processing Traffic information in the near past, and are compared in terms of their performances. The NDR is implemented within a microscopic Traffic simulation (S-Paramics) for a real-world network in West Glasgow, where the off-line training of the NDR amounts to a simulation-based optimization aiming to reduce delay, CO2 and black carbon emissions. The emission calculations are based on the high-fidelity vehicle dynamics generated by the simulation, and the AIRE instantaneous emission model. Extensive tests are performed to assess the NDR framework, not only in terms of its effectiveness in reducing the aforementioned objectives, but also in relation to local vs. global benefits, trade-off between delay and emissions, impact of sensor locations, and different levels of network saturation. The results suggest that the NDR is an effective, flexible and robust way of alleviating congestion and reducing Traffic emissions.

Ke Han - One of the best experts on this subject based on the ideXlab platform.

  • nonlinear decision rule approach for real time Traffic signal control for congestion and emission mitigation
    Networks and Spatial Economics, 2020
    Co-Authors: Junwoo Song, Ke Han, Chaozhe Jiang
    Abstract:

    We propose a real-time signal control framework based on a nonlinear decision rule (NDR), which defines a nonlinear mapping between network states and signal control parameters to actual signal controls based on prevailing Traffic conditions, and such a mapping is optimized via off-line simulation. The NDR is instantiated with two neural networks: feedforward neural network (FFNN) and recurrent neural network (RNN), which have different ways of Processing Traffic information in the near past, and are compared in terms of their performances. The NDR is implemented within a microscopic Traffic simulation (S-Paramics) for a real-world network in West Glasgow, where the off-line training of the NDR amounts to a simulation-based optimization aiming to reduce delay, CO2 and black carbon emissions. The emission calculations are based on the high-fidelity vehicle dynamics generated by the simulation, and the AIRE instantaneous emission model. Extensive tests are performed to assess the NDR framework, not only in terms of its effectiveness in reducing the aforementioned objectives, but also in relation to local vs. global benefits, trade-off between delay and emissions, impact of sensor locations, and different levels of network saturation. The results suggest that the NDR is an effective, flexible and robust way of alleviating congestion and reducing Traffic emissions.

  • nonlinear decision rule approach for real time Traffic signal control for congestion and emission reductions
    arXiv: Optimization and Control, 2018
    Co-Authors: Junwoo Song, Ke Han, Chaozhe Jiang
    Abstract:

    We propose a real-time signal control framework based on a nonlinear decision rule (NDR), which defines a nonlinear mapping between network states and signal control parameters to actual signal controls based on prevailing Traffic conditions, and such a mapping is optimized via off-line simulation. The NDR is instantiated with two neural networks: feedforward neural network (FFNN) and recurrent neural network (RNN), which have different ways of Processing Traffic information in the near past, and are compared in terms of their performances. The NDR is implemented within a microscopic Traffic simulation (S-Paramics) for a real-world network in West Glasgow, where the off-line training of the NDR amounts to a simulation-based optimization aiming to reduce delay, CO2 and black carbon emissions. The emission calculations are based on the high-fidelity vehicle dynamics generated by the simulation, and the AIRE instantaneous emission model. Extensive tests are performed to assess the NDR framework, not only in terms of its effectiveness in reducing the aforementioned objectives, but also in relation to local vs. global benefits, trade-off between delay and emissions, impact of sensor locations, and different levels of network saturation. The results suggest that the NDR is an effective, flexible and robust way of alleviating congestion and reducing Traffic emissions.

Junwoo Song - One of the best experts on this subject based on the ideXlab platform.

  • nonlinear decision rule approach for real time Traffic signal control for congestion and emission mitigation
    Networks and Spatial Economics, 2020
    Co-Authors: Junwoo Song, Ke Han, Chaozhe Jiang
    Abstract:

    We propose a real-time signal control framework based on a nonlinear decision rule (NDR), which defines a nonlinear mapping between network states and signal control parameters to actual signal controls based on prevailing Traffic conditions, and such a mapping is optimized via off-line simulation. The NDR is instantiated with two neural networks: feedforward neural network (FFNN) and recurrent neural network (RNN), which have different ways of Processing Traffic information in the near past, and are compared in terms of their performances. The NDR is implemented within a microscopic Traffic simulation (S-Paramics) for a real-world network in West Glasgow, where the off-line training of the NDR amounts to a simulation-based optimization aiming to reduce delay, CO2 and black carbon emissions. The emission calculations are based on the high-fidelity vehicle dynamics generated by the simulation, and the AIRE instantaneous emission model. Extensive tests are performed to assess the NDR framework, not only in terms of its effectiveness in reducing the aforementioned objectives, but also in relation to local vs. global benefits, trade-off between delay and emissions, impact of sensor locations, and different levels of network saturation. The results suggest that the NDR is an effective, flexible and robust way of alleviating congestion and reducing Traffic emissions.

  • nonlinear decision rule approach for real time Traffic signal control for congestion and emission reductions
    arXiv: Optimization and Control, 2018
    Co-Authors: Junwoo Song, Ke Han, Chaozhe Jiang
    Abstract:

    We propose a real-time signal control framework based on a nonlinear decision rule (NDR), which defines a nonlinear mapping between network states and signal control parameters to actual signal controls based on prevailing Traffic conditions, and such a mapping is optimized via off-line simulation. The NDR is instantiated with two neural networks: feedforward neural network (FFNN) and recurrent neural network (RNN), which have different ways of Processing Traffic information in the near past, and are compared in terms of their performances. The NDR is implemented within a microscopic Traffic simulation (S-Paramics) for a real-world network in West Glasgow, where the off-line training of the NDR amounts to a simulation-based optimization aiming to reduce delay, CO2 and black carbon emissions. The emission calculations are based on the high-fidelity vehicle dynamics generated by the simulation, and the AIRE instantaneous emission model. Extensive tests are performed to assess the NDR framework, not only in terms of its effectiveness in reducing the aforementioned objectives, but also in relation to local vs. global benefits, trade-off between delay and emissions, impact of sensor locations, and different levels of network saturation. The results suggest that the NDR is an effective, flexible and robust way of alleviating congestion and reducing Traffic emissions.

Bin Fan - One of the best experts on this subject based on the ideXlab platform.

  • Traffic sign recognition using a multi task convolutional neural network
    IEEE Transactions on Intelligent Transportation Systems, 2018
    Co-Authors: Hengliang Luo, Yi Yang, Bei Tong, Bin Fan
    Abstract:

    Although Traffic sign recognition has been studied for many years, most existing works are focused on the symbol-based Traffic signs. This paper proposes a new data-driven system to recognize all categories of Traffic signs, which include both symbol-based and text-based signs, in video sequences captured by a camera mounted on a car. The system consists of three stages, Traffic sign regions of interest (ROIs) extraction, ROIs refinement and classification, and post-Processing. Traffic sign ROIs from each frame are first extracted using maximally stable extremal regions on gray and normalized RGB channels. Then, they are refined and assigned to their detailed classes via the proposed multi-task convolutional neural network, which is trained with a large amount of data, including synthetic Traffic signs and images labeled from street views. The post-Processing finally combines the results in all frames to make a recognition decision. Experimental results have demonstrated the effectiveness of the proposed system.

Henk J Van Zuylen - One of the best experts on this subject based on the ideXlab platform.

  • Processing Traffic Data Collected by Remote Sensing
    Transportation Research Record, 2009
    Co-Authors: Victor L. Knoop, Serge P. Hoogendoorn, Henk J Van Zuylen
    Abstract:

    Video data are being used more often to study Traffic operations. However, extracting vehicle trajectories from video by current methods is a difficult process, typically resulting in many errors. The process requires extensive labor to correct the trajectories manually. This paper proposes a method to process video data from Traffic operations. Instead of detecting a vehicle in each picture of the video separately, the video data are transformed so that the trajectories of the vehicles (their position over time) become visible in a single image. In this single image, the trajectories can be found by detecting lines. The difference from other methods is that trajectories rather than vehicles are detected. Trajectory (line) detection is more robust than vehicle (rectangle) detection; with this method, about 95% of the trajectories are detected correctly and, more important, the segments of each trajectory are much longer compared with results from other methods in the literature. Also, the detection is a q...