Identifying Potentially Risky Intersections for Heavy-Duty Truck Drivers Based on Individual Driving Styles

Yi Zhu, Yongfeng Ma, Shuyan Chen, Aemal J. Khattak, Qianqian Pang

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


In developing countries, heavy-duty trucks play an important role in transportation for infrastructure construction. However, frequent truck accidents cause great losses. Previous studies have mainly focused on passenger drivers; to date, little has been done to assess the driving behav¬ior of heavy truck drivers. The overall objective of this study is to classify driving styles at intersec¬tions, analyze the impacts of differing types of traffic control at intersections on driving styles, and identify potentially risky intersections. We selected 11 heavy-duty truck drivers and collected kine¬matic driving parameters (including driving speed and both lateral and longitudinal acceleration) from field experiments in Nanjing for our study. Our study on driving styles followed the following steps. First, we reduced data size and extracted data features on the basis of time windows in Py¬thon. Second, driving styles were classified into three driving styles: Cautious, normal, and aggres¬sive, based on the K-means clustering method, and the corresponding thresholds for each category were obtained. Kinematic driving parameters were used as driving style measurements. Third, ac¬cording to classifications of driving style, the impacts of four different intersection traffic control types: Two-phase signalized, multiphase signalized, stop, and yield intersections, on driving styles have been analyzed using the multinomial logit model. Moreover, based on the above analysis, potentially risky intersections were identified. The results suggest that different types of traffic con¬trol at intersections lead to variations in driving styles and have different influences on driving styles. In terms of accuracy, our method, which uses driving speed, both lateral and longitudinal acceleration, and jerk as features, performs better than traditional methods which only use speed and acceleration. The results of the study allow us to analyze the driving data of heavy-duty trucks and identify drivers who drive more aggressively during a trip. In addition, the results show that aggressive driving styles mostly occur at stop intersections and in the dilemma zones of signalized intersections. Therefore, early-warning interventions can be provided during a driver's trip by an¬alyzing the different types of traffic control at intersections on the route in advance. Finally, the cumulative analysis of driving styles at intersections over multiple trips can be used to identify potentially high-risk intersections. It is possible to eliminate potential risks in these areas through measures such as early warnings and by improving traffic management control methods.

Original languageEnglish (US)
Article number4678
JournalApplied Sciences (Switzerland)
Issue number9
StatePublished - May 1 2022


  • Driving behavior
  • Driving style
  • Heavy-duty trucks
  • K-means clustering
  • Traffic control types of intersections

ASJC Scopus subject areas

  • General Materials Science
  • Instrumentation
  • General Engineering
  • Process Chemistry and Technology
  • Computer Science Applications
  • Fluid Flow and Transfer Processes


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