A Road Traffic Crash Risk Assessment Method Using Vehicle Trajectory Data and Surrogate Safety Measures

The development of traffic sensing technology makes it possible to obtain high-precision microscopic traffic flow data; characteristic parameters based on microscopic traffic flow can be used for real-time crash risk assessment on roadways along with active safety control. The purpose of this paper is to study the relationship between surrogate safety measures (SSM) based on microscopic traffic parameters and road real-time crash risk. It is proposed to use the microwave radar detector to obtain real-time position and speed of the vehicle moving through the detection area. Vehicle trajectory data can be processed to extract the modified time-to-collision (MTTC), potential index for collision with urgent deceleration (PICUD), stopping headway distance (SHD), deceleration rate to avoid a crash (DRAC), and deceleration-based surrogate safety measure (DSSM). This can represent the three categories based on measuring attributes: temporal proximity, distance, and deceleration. Collectively, the time is divided into 30 s segments, and the traffic is divided into two states by the combination of TTC value, rapid deceleration rate and manual observation: no risk and risk. The Mann-Whitney U test is used to analyze the distribution difference of selected SSM under different traffic accident risks. Finally, the Fisher discriminant method is used to determine the road segment risk in real-time through the selected parameters. This study identified real-time microscopic traffic flow indicators that are significantly associated with road traffic crash risks and provides a basis for predictive models of road traffic crashes with proactive prevention and control technologies.


  • English

Media Info

  • Media Type: Web
  • Pagination: pp 3657-3669
  • Monograph Title: CICTP 2020: Advanced Transportation Technologies and Development-Enhancing Connections

Subject/Index Terms

Filing Info

  • Accession Number: 01749006
  • Record Type: Publication
  • ISBN: 9780784482933
  • Files: TRIS, ASCE
  • Created Date: Aug 27 2020 10:16AM