Effect model of urban traffic congestion on driver’s lane-changing behavior

Abstract

Road traffic congestion has become a normal state and caused many problems in large cities of China, and lane-changing model has attracted increased attention in recent years. This study is aimed to explore the changing trend and quantify the logical relationship between driver’s heart rate and lane-changing behavior under urban traffic congestion. First, the testing scheme of driver’s heart rate and lane-changing has been designed. Tested drivers and testing paths are chosen strictly to achieve the experiments as well. Then, with the drivers’ behavior-related data, the backpropagation neural network theory is introduced to build the driver’s “pressure–state–response” model under urban traffic congestion, which takes driver’s pressure and state as input variables, and driver’s response is selected as output variables. As the result of pressure–state–response model, it is significant that effect of urban traffic congestion on driver’s heart rate and lane-changing proportion. The validation results indicate that the pressure–state–response model works well to predict the proportion of risky lane-changing, and the pressure–state–response model can be used for warning the risky lane-changing directly under urban traffic congestion.