Incorporating Route Safety in the Pathfinding Problem Using Big Data

With the emergence of the internet of things, pathfinding problems have recently received a significant amount of attention. Various commercial applications provide automated routing by considering travel time, travel distance, fuel consumption, complexity of the road, etc. Unfortunately, many of these prospective applications do not consider route safety. Because connected vehicles (CV) generate enriched “Big Data”, researchers have opportunities to develop new transportation methods. The goal of this study is to address safety aspects in pathfinding problems by developing a methodological framework that simultaneously considers safety and mobility. To reach this goal, the concept of “driving volatility” is utilized as a surrogate safety performance measure. The proposed framework uses CV big data and real-time traffic data to obtain calculate safety indices and travel times. Measured safety indices include 5-year crash history, route speed and acceleration volatility, and driver volatility. Travel time and safety shape a cost function called “route impedance”. The algorithm has the flexibility for the user to predefine the weight for safety consideration. It also uses driver volatility to automatically increase weights of safety considerations for volatile drivers. In order to illustrate the algorithm, an origin-destination pair in Ann Arbor Michigan is selected and more than 42 million CV observations from around 2,800 CVs from the Safety Pilot Model Deployment were analyzed. Finally, this paper shows suggested routes for multiple scenarios to demonstrate the outcome of the study. The results revealed that the algorithm might suggest different routes when considering safety indices and not just travel time.

  • Supplemental Notes:
    • This paper was sponsored by TRB committee ANB20 Standing Committee on Safety Data, Analysis and Evaluation.
  • Corporate Authors:

    Transportation Research Board

  • Authors:
    • Hoseinzadeh, Nima
    • Arvin, Ramin
    • Khattak, Asad J
    • Han, Lee D
  • Conference:
  • Date: 2019


  • English

Media Info

  • Media Type: Digital/other
  • Features: Figures; References;
  • Pagination: 5p

Subject/Index Terms

Filing Info

  • Accession Number: 01698105
  • Record Type: Publication
  • Report/Paper Numbers: 19-01433
  • Files: TRIS, TRB, ATRI
  • Created Date: Mar 1 2019 3:51PM