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

Traffic control agencies (TCAs), including police officers, firefighters, or other traffic law enforcement officers, can override automatic traffic signal control and manually control the traffic at an intersection. TCA-based traffic signal control is crucial to mitigate nonrecurrent traffic congestion 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 decision-making behavior. The model calculates TCA's pressure based on two attributes: vehicle and pedestrian queue dynamics and the red time duration for each phase. When TCA's pressure on each phase meet certain criteria and the minimal green is satisfied, TCA will terminate the current phase and switch to another phase. In order to study TCA behavior systematically, we first build a manual signal control simulator based on a microscopic traffic simulation tool. Supported by the manual control simulator, a series of human subject experiments have been conducted with real-world TCAs. Experiment data are divided into training data and test data. The proposed behavior model is then calibrated by training data, and the model is validated by both offline segment-based phase and duration prediction and online VISSIM-based simulation. Further, we test the model with videotaped TCA behavior data at a real-world intersection. Both validation results support the effectiveness of proposed behavior model.