Modeling Traffic Control Agency Decision Behavior for Multimodal Manual Signal Control Under Event Occurrences


Traffic control agencies (TCAs), including police officers, firefighters, or different traffic law enforcement officers, will override automatic traffic signal control and manually management the traffic at an intersection. TCA-based mostly traffic signal management is crucial to mitigate nonrecurrent tie up caused by planned and unplanned events. Understanding and predicting TCA behaviors is significant to optimize event traffic management and operations. In this paper, we propose a pressure-based human behavior model to mimic TCA's call-making behavior. The model calculates TCA's pressure based mostly on 2 attributes: vehicle and pedestrian queue dynamics and the red time period for every phase. When TCA's pressure on each section meet bound criteria and therefore the minimal inexperienced is happy, TCA can terminate the current phase and switch to a different part. In order to review TCA behavior systematically, we have a tendency to 1st build a manual signal control simulator primarily based on a microscopic traffic simulation tool. Supported by the manual control simulator, a series of human subject experiments are conducted with real-world TCAs. Experiment information are divided into training knowledge and take a look at knowledge. The proposed behavior model is then calibrated by coaching data, and also the model is validated by both offline segment-based section and period prediction and on-line VISSIM-based simulation. Additional, we test the model with videotaped TCA behavior knowledge at a true-world intersection. Both validation results support the effectiveness of proposed behavior model.

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