首页|Exploring the Behavior-Driven Crash Risk Prediction Model: The Role of Onboard Navigation Data in Road Safety
Exploring the Behavior-Driven Crash Risk Prediction Model: The Role of Onboard Navigation Data in Road Safety
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NETL
NSTL
Wiley-Hindawi
Driving behavior has frequently been overlooked in previous road traffic crash research. Hereby, abnormal (extreme) driving behavior data transmitted by the onboard navigation systems were collected for vehicles involved in traffic crashes, including sharp-lane-change, sharp-acceleration, and sudden-braking behaviors. Using these data in conjunction with expressway crash records, multiple classification learners were trained to establish a behavior-driven risk prediction model. To further investigate the influence of driving behavior on crash risk, partial dependence plots (PDPs) were applied. Regression analyses indicate that models have a stronger effect when derivative features such as frequency of specific deviant behavior, speed, and acceleration in the behavior process are included. The behavioral RUSBoost model surpasses other models, achieving an AUC prediction metric of 0.782 and outperforming traditional traffic-flow-driven machine learning models. PDP analysis demonstrates that the sudden-braking behavior is the leading contributory factor of expressway crashes, particularly when the acceleration exceeds 0.5 G. This study confirms the potential of predicting crash risks through augmenting behavior data from navigation software; the findings lay a foundation for countermeasures.
Xiao-chi Ma、Jian Lu、Yiik Diew Wong、Jaehyun (Jason) So
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Jiangsu Key Laboratory of Urban ITS Southeast University Nanjing ||Jiangsu Province Collaborative Innovation Centre of Modern Urban Traffic Technologies Southeast University Nanjing ||School of Transportation Southeast University Nanjing ||School of Civil and Environmental Engineering Nanyang Technological University
Jiangsu Key Laboratory of Urban ITS Southeast University Nanjing ||Jiangsu Province Collaborative Innovation Centre of Modern Urban Traffic Technologies Southeast University Nanjing ||School of Transportation Southeast University Nanjing
School of Civil and Environmental Engineering Nanyang Technological University
NETL
NSTL
Wiley-Hindawi
