In most right-driving urban signalized intersections, right-turn vehicle signals do not usually control turns. To address the problem of signal control in a pedestrian–vehicle interaction, this paper establishes a right-turn signal optimization (RTSO) model that considers both efficiency and safety. First, the main factors influencing the behavior of vehicle and pedestrian during pedestrian–vehicle interaction are analyzed, and a pedestrian–vehicle interaction model (PVI model) at an urban road crosswalk is established. This model is used to determine the probabilities of four pedestrian–vehicle interaction situations. Then, based on the traffic conflict theory, the next step was to construct an objective function that minimizes the total delay of traffic participants considering pedestrian–vehicle interactions, and another objective function that minimizes the potential conflicts considering pedestrian–vehicle interactions. Then, an RTSO model is obtained by introducing a safety-efficiency coefficient to combine the previously described two constructed functions. Finally, the PVI model and delay model are verified through video observation data and the establishment of a cellular automata simulation platform of pedestrian–vehicle interaction. Using these models, a field signal plan, the delay minimization scheme, the conflict minimization scheme, and the proposed scheme are numerically analyzed under different yielding rates. This proposed scheme is further numerically analyzed under different safety-efficiency coefficients. The results show that this paper’s RTSO model has certain advantages in increasing safety and reducing delay. In addition, using these results, this paper gives a recommended value for the safety-efficiency coefficients in different application scenarios.


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    Title :

    Optimizing Right-Turn Signals to Benefit Pedestrian–Vehicle Interactions


    Additional title:

    Transportation Research Record: Journal of the Transportation Research Board


    Contributors:


    Publication date :

    2021-03-03




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



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