Prediction of pedestrians’ wait-or-go decision using trajectory data based on gradient boosting decision tree

The road-crossing of pedestrians at unsignalized crosswalks is a major concern for road safety. Previous studies focused on explaining of the mechanism underlying this behavior, but a framework of prediction is missing. To predict this behavior, only variables measured before the decision is made should be considered. To explore whether historical data is able to predict the behavior, this paper investigates pedestrians’ wait-or-go (WOG) behavior based on trajectory data and a machine learning method, both of which have been rarely applied by previous studies. The use of trajectory data enables the analysis of several influential factors related to moving characteristics, which are critical for pedestrians’ decision making. The framework based on machine learning, combined with trajectory data, achieves good explanatory power and predictability of pedestrians’ WOG behavior. Moreover, a possible application of this study is the prediction of pedestrian road-crossing intention in the context of autonomous cars.