A gradient boosting logit model to investigate driver's stop-or-run behavior at signalized intersections using high-resolution traffic data. (November 2016)

• Record Type: Journal Article
• Authors: Ding, Chuan; Wu, Xinkai; Yu, Guizhen; Wang, Yunpeng
• Is Part Of: Transportation research. Volume 72(2016)
• Page Start: 225
• Page End: 238
• Publication Date: 2016-11
• Subjects: Driving behavior -- Gradient boosting logit -- Variable importance -- High-resolution traffic even data; Transportation -- Periodicals; Transportation -- Technological innovations -- Periodicals; 388.011
• DOI: 10.1016/j.trc.2016.09.016
• Languages: English
• ISSNs: 0968-090X

Abstract

Highlights: Gradient boosting logit model is proposed to drivers' stop-or-run behavior prediction. High-resolution traffic and signal event data collected from loop detectors are used to study intersection safety. Impact of different combination of regularization parameters on model performance is discussed. Relative influences of predictors on drivers' stop-or-run behavior predictions are identified and ranked. Abstract: Driver's stop-or-run behavior at signalized intersection has become a major concern for the intersection safety. While many studies were undertaken to model and predict drivers' stop-or-run (SoR) behaviors including Yellow-Light-Running (YLR) and Red-Light-Running (RLR) using traditional statistical regression models, a critical problem for these models is that the relative influences of predictor variables on driver's SoR behavior could not be evaluated. To address this challenge, this research proposes a new approach which applies a recently developed data mining approach called gradient boosting logit model to handle different types of predictor variables, fit complex nonlinear relationships among variables, and automatically disentangle interaction effects between influential factors using high-resolution traffic and signal event data collected from loop detectors. Particularly, this research will first identify a series of related influential factors including signal timing information, surrounding traffic information, and surrounding drivers' behaviors using thousands drivers' decision events including YLR, RLR, and first-to-stop (FSTP) extracted from high-resolution loop detector data from three intersections. Then the research applies the proposed data mining approach to search for the optimal prediction model for each intersection. Furthermore, a comparison was conducted to compare the proposed new method with the traditional statistical regression model. The results show that the gradient boosting logit model has superior performance in terms of prediction accuracy. In contrast to other machine learning methods which usually apply 'black-box' procedures, the gradient boosting logit model can identify and rank the relative importance of influential factors on driver's stop-or-run behavior prediction. This study brings great potential for future practical applications since loops have been widely implemented in many intersections and can collect data in real time. This research is expected to contribute to the improvement of intersection safety significantly.