Integrated Cooperative Adaptive Cruise Control and Machine Learning Algorithms for Intelligent Vehicles Near an Off-Ramp

Freeway diverge segment has significant impacts on the current traffic flow, and could affect the heterogeneous traffic flow consisting of manual and intelligent vehicles. The primary objective of this study is to evaluate how intelligent vehicles equipped with cooperative adaptive cruise control (CACC) improve freeway efficiency and safety at an off-ramp bottleneck. Based on NGSIM database, the lane-changing characteristics were learned from the ground-truth vehicle trajectory data utilizing randomized forest and back-propagation neural network algorithms. A microscopic simulation testbed was constructed, in which the realistic PATH CACC models and surrogate safety measures of the time exposed time-to-collision (TET) and time integrated time-to-collision (TIT) were used. The results showed that both CACC penetration and length of diverge influence areas exerted considerable influence on road capacity and traffic safety. Particularly, the capacity ascended to the peak after an initial decrease with the increase of CACC vehicles. The maximum capacity obtained in 100% CACC vehicle scenario was improved by over 53%, compared with 50% CACC penetration scenario. The proposed integration system with 100% CACC penetration can reduce the rear-end collision risks effectively, with the TIT and TET declined by 71.2%~97.8%. Moreover, the transport system with longer range of lane-changing area had better performance if all other parameters remain unchanged. Findings of this study can support freeway management and operations in the future.

• Supplemental Notes:
  • This paper was sponsored by TRB committee ABJ70 Standing Committee on Artificial Intelligence and Advanced Computing Applications.
• Authors:
  • Dong, Changyin
  • Wang, Hao
  • Li, Ye
  • Zheng, Yunzhuang
  • Liu, Shanwen
• Conference:
  • Transportation Research Board 97th Annual Meeting
  • Location: Washington DC, United States
  • Date: 2018-1-7 to 2018-1-11
• Date: 2018

Language

• English

Media Info

• Media Type: Digital/other
• Features: References;
• Pagination: 4p

Subject/Index Terms

• TRT Terms: Algorithms; Autonomous intelligent cruise control; Bottlenecks; Intelligent vehicles; Lane changing; Machine learning; Off ramps; Traffic safety; Traffic simulation; Vehicle trajectories
• Subject Areas: Highways; Planning and Forecasting; Safety and Human Factors; Vehicles and Equipment;

Filing Info

• Accession Number: 01658376
• Record Type: Publication
• Report/Paper Numbers: 18-03671
• Files: TRIS, TRB, ATRI
• Created Date: Jan 29 2018 10:27AM