A multi-scale framework for pedestrian behavior model at signalized crosswalks

A multi-scale simulation framework is proposed to analyze pedestrian delays at signalized crosswalks in large urban areas under different conditions. An aggregated-level model runs in normal conditions, where each crosswalk is represented as an agent. In this level, pedestrian counts collected near crosswalks are utilized to derive the binary choice probability from a utility maximization model. The derived probability function extended from Adams' model is utilized to estimate an average pedestrian delay with corresponding traffic flow rate and traffic light control at each crosswalk. When an abnormality is detected, a detailed-level model with each pedestrian being an agent is executed in the affected subareas. Pedestrian decision-making under abnormal conditions, physical movement, and crowd congestion are explicitly considered in the detailed-level model. The proposed framework is illustrated with the scenario of Chicago Loop area which contains 87 signalized intersections. A simulation model has been implemented in AnyLogicR software, where the input of pedestrian arrival distribution is derived from a field study. Experiments have been conducted to compare the results obtained from different levels and analyze correlations among statistics of neighboring crosswalks. The obtained results will allow us to automate appropriate fidelity selection under varying conditions as a future work.