Abstract
The driving tendency of drivers is one of the most important factors in lane-changing maneuvers. However, the heterogeneity of the characteristics of drivers’ lane-changing behaviors has not been adequately considered. The primary objective of the present study is to explore the risk level of the lane-changing implementation process under different driving tendencies upon approaching signalized intersections in an urban area. This paper defines the Integrated Conflict Risk Index (ICRI), which takes into account the probability and severity of risk. Using the index as the dependent variable, the risk prediction model of implementing the lane-change process is established. A series of experiments, which included a questionnaire, a number of tests, and on-road experiments, was conducted to identify the driving tendencies of the participants. A combination of video recording and instrumented vehicles was used to collect lane-changing trajectory data of different driving tendencies. The parameters of the model were calibrated, and the results indicate that driving tendency has a significant effect on the risk level of lane-changing execution. More specifically, the more aggressive the driving tendency, the higher the risk level. The quantitative results of the study can provide the basis for conflict risk assessment in the existing lane-changing models.
Highlights
The design and assessment of traffic safety management are difficult in real transportation because of the cost and risks of collecting trajectory data
Compared to car following models, in which vehicles are in the same lane and the driver behavior is only influenced by the lead vehicle, the lane-changing process involves a high level of interaction between the vehicles and is more complex [1,2,3]
A traffic conflict risk prediction model with a new index is developed in this study
Summary
The design and assessment of traffic safety management are difficult in real transportation because of the cost and risks of collecting trajectory data. Microscopic traffic simulation models are powerful tools extensively used to evaluate traffic safety management policies and to assess their effects. In microscopic traffic simulation models, car following and lane changing are two fundamental components. As a result of the importance of the role of microscopic traffic simulation models, how to improve the accuracy of the lane-changing model has attracted wide attention among scholars over the past decades. A large amount of work has been conducted to collect trajectory data [4,5,6,7,8,9] and formulate models of lane changing [10]
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