Social Influence on Driver Decisions Using Modeling and Gossip Algorithms

Most car-following models are physics-based and do not capture the sociological aspects of driving. The intended outcome of this study is to account for social dynamics in traffic flow and provide new knowledge about social influences during driving in terms of acceleration, deceleration and cruise decisions. This study develops and simulates a theoretical and empirical framework, indicating applicability in transportation contexts such as traffic merging behind a crash or at a construction work zone. The authors study the heterogeneous nature of the driving decision-making mechanism through Monte Carlo simulations and vehicle trajectory data. The study uses a randomized gossip-based algorithm to explore interactions among proximate drivers; drivers strive to achieve social consensus. To calibrate vehicle motion, data from connected vehicles participating in Michigan Safety Pilot Program was used. The study calculates probability density of a polarization factor, which in turn determines bias in driver states after information propagation. The results show that a majority of drivers within a small group can reach a consensus in terms of acceleration, deceleration and cruise decisions. However, the initial bias in states (i.e., acceleration, deceleration and cruise) determines the overall shape of the probability of the final consensus. The authors also explore the effect of leaders, drivers with strong preferences, on the decision-making of follower drivers. The leaders had a dominant influence on the final consensus of driver decisions regardless of the initial bias (initial polarization factor). Finally, this paper discusses the implications of adding sociological phenomena to physically-based models.

  • Supplemental Notes:
    • This paper was sponsored by TRB committee AHB45 Standing Committee on Traffic Flow Theory and Characteristics.
  • Corporate Authors:

    Transportation Research Board

  • Authors:
    • Mohammadi, Sevin
    • Kamrani, Mohsen
    • Khattak, Asad Jan
    • Chakraborty, Subhadeep
  • Conference:
  • Date: 2019


  • English

Media Info

  • Media Type: Digital/other
  • Features: Figures; References; Tables;
  • Pagination: 7p

Subject/Index Terms

Filing Info

  • Accession Number: 01697430
  • Record Type: Publication
  • Report/Paper Numbers: 19-01424
  • Files: TRIS, TRB, ATRI
  • Created Date: Dec 7 2018 9:27AM