Surrounding Vehicles’ Contribution to Car-Following Models: A Deep-Learning Based Analysis

Relatively recent increased computational power and extensive availability of traffic data have provided a unique opportunity to re-investigate drivers’ car-following (CF) behavior. Classic CF models assume drivers’ behavior is only influenced by their preceding vehicle. Recent studies have indicated that considering surrounding vehicles’ information (e.g., multiple preceding vehicles) could impact the performance of CF models. An in-depth investigation of surrounding vehicles’ contribution to CF modeling performance has not been reported in the literature. This study uses a deep-learning model with long short-term memory (LSTM) to investigate to what extent considering surrounding vehicles could improve the performance of CF models. This investigation helps to select the right inputs for traffic flow modeling. Five CF models are compared in this study (i.e., Classic, Multi-Anticipative, Adjacent-Lanes, Following-Vehicle, and All-Surrounding-Vehicles CF models). Performance of the CF models’ is compared in terms of accuracy, stability, and smoothness of traffic flow. The CF models are trained, validated, and tested by a large publicly available dataset. Results show insignificant performance differences between the Classic CF model and Multi-Anticipative model, or Adjacent-Lanes model in terms of accuracy, stability, or smoothness. The Following-Vehicle CF model shows similar performance to the Multi-Anticipative model. The All-Surrounding-Vehicles CF model has underperformed all the other models.


  • English

Media Info

  • Media Type: Digital/other
  • Features: Figures; References; Tables;
  • Pagination: 21p

Subject/Index Terms

Filing Info

  • Accession Number: 01764330
  • Record Type: Publication
  • Report/Paper Numbers: TRBAM-21-03521
  • Files: TRIS, TRB, ATRI
  • Created Date: Dec 23 2020 11:26AM