Enhancing Traffic Safety and Connectivity: A Data-Driven Multi-Step-Ahead Vehicle Headway Prediction Leveraging High-Resolution Vehicular Trajectories

Vehicle headway, defined as the time elapsed between two successive vehicles passing a roadway point, is a key mesoscopic-scale measure in traffic flow theory with safety-critical transportation applications, such as preemptive collision avoidance warning systems as well as connected and autonomous vehicle (CAV) platoon control. Hence, it is crucial to accurately predict vehicle headway over sufficiently long future horizons (i.e., multi-step-ahead prediction) to be applicable for downstream safety-critical applications. This is a challenging task due to several random factors influencing headway, including inter- and intra-driver heterogeneity, asymmetric car-following driving behavior, and vehicle heterogeneity under mixed traffic of different vehicle classes. This becomes even more complicated under traffic congestion, which results in tangible inter-vehicle interactions and, thus, speed-dependent headways. The complex effects of the above factors on headway, along with the unprecedented amount of high time-resolution vehicle trajectory big data (e.g., datapoints recorded every 0.1 second), call for advanced data-driven headway prediction models. Deep learning architectures, particularly variants of Recurrent Neural Network (RNN), are promising candidates as they can “learn” highly nonlinear relationships from headway time-series data. However, recurrent networks are notorious for the vanishing gradient problem, which precludes learning long-term dependencies in time series data. To tackle, this proposed project will employ a state-of-the-art interpretable deep learning model for multi-step-ahead time series forecasting (e.g., next 5 seconds), which can accommodate reasonably long prediction horizons that can capture human/vehicle reaction time. Leveraging the vehicle trajectory big data from the USDOT’s Next Generation Simulation (NGSIM) dataset, the model will be trained and tested to investigate the effects on headway of microscopic traffic measures, macroscopic traffic flow, vehicle class, and lane position.

• Record URL:
  https://ppms.cit.cmu.edu/projects/detail/470

Language

• English

Project

• Status: Completed
• Funding: $41280
• Contract Numbers:

  69A3552344811

• Sponsor Organizations:

  Safety21

  Carnegie Mellon University
  Pittsburgh, PA  United States  15213

  Office of the Assistant Secretary for Research and Technology

  University Transportation Center Program
  ,    
• Managing Organizations:

  Safety21

  Carnegie Mellon University
  Pittsburgh, PA  United States  15213
• Project Managers:

  Stearns, Amy

• Performing Organizations:

  University of Texas Rio Grande Valley

  ,    
• Principal Investigators:

  Noruzoliaee, Mohamadhossein

• Start Date: 20230701
• Expected Completion Date: 20240630
• Actual Completion Date: 20240730
• USDOT Program: University Transportation Centers

Subject/Index Terms

• TRT Terms: Autonomous vehicles; Connected vehicles; Connectivity; Crash avoidance systems; Headways; Machine learning; Neural networks; Traffic platooning; Traffic safety; Trajectory
• Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management; Safety and Human Factors; Vehicles and Equipment;

Filing Info

• Accession Number: 01900365
• Record Type: Research project
• Source Agency: Safety21
• Contract Numbers: 69A3552344811
• Files: UTC, RIP
• Created Date: Nov 21 2023 6:37PM