Variable speed limit strategy with anticipatory lane changing decisions

Abstract

This paper develops a novel anticipatory Variable Speed Limit (VSL) control strategy that incorporates driver behavior based on trajectory data from probe vehicles. In particular, the research examines how the speed limit controls can be coordinated and optimized to reduce lane changing and overall braking, thus to achieve greater traffic throughput, safety, and sustainability. Driver behavior, such as acceleration/deceleration and lane changing, as reflected in individual trajectory data from GPS enabled probe vehicles are crucial for early detection of shockwave formation and proactive selection of speed limits; and consequently delay, or even eliminate breakdown formation. The core of this approach is the incorporation of a lane changing model which provides a more robust integrated speed limit selection and shockwave detection framework than the one we have developed in an earlier paper. The control principle is formulated in a generic fashion that finds the optimal speed limit control variables for either separate or simultaneous reduction of travel time, crash rates, and fuel consumption over a prediction horizon. The findings from the paper suggested that the VSL strategy was able to result in fewer lane changing rate (LCR) compared to the No-VSL case. Also, the developed algorithm was able to produce higher frequency of lower acceleration and deceleration rates than the No-VSL case, which indicated a smoother acceleration and deceleration pattern that corresponded to lower emission and fuel consumption. The performance of the algorithm was also examined under different probe penetration rates and congestion levels to identify the % of probe needed to achieve simultaneous mobility, safety, and environmental objectives.

Keywords:

• driver behavior
• lane changing rate
• probe penetration rate
• probe vehicles
• sensitivity analysis
• variable speed limit

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The authors wish to thank the Natural Science and Engineering Research Council of Canada (NSERC), Stantec, Alberta Motor Association - Alberta Innovates Technology Futures (AMA-AITF) collaborative grant in Smart Multimodal Transportation Systems and Urban Alliance for providing funding for this research.