Controlling Traffic Flow to Mitigate Congestion on Motorways

Abstract

Traffic congestion inflicted by shockwaves has developed into a substantial universal issue, incurring critical impacts worldwide. This research concentrates on exploring the potential of employing Connected Autonomous Vehicles (CAVs) to alleviate these shockwaves. Consequently, an integrated system is proposed, merging early shockwave detection with optimization based CAVs control for enhanced traffic flow. The system incorporates real-time shockwave identification using motorway detector data, an automated CAV speed regulation strategy, and shockwave endpoint prediction models. A novel algorithm accurately detects shockwaves by tracking individual vehicle speeds and headways from inductive loops. The algorithm introduces an “Events Count” parameter allowing configuration for larger, high-impact shockwaves. The control strategy assumes command of CAVs approaching the shockwave to smooth traffic flow by optimizing speed based on the shockwave endpoint predictions. The prediction models leverage vehicle trajectories within shockwaves to reliably estimate future time, position, and speed values. The system is implemented and thoroughly evaluated using the PTV VISSIM microsimulation platform. Various motorway environments are simulated to rigorously test functionality across diverse traffic conditions. Results exhibit the system’s effectiveness in enhancing traffic flow, significantly improving vehicle speeds, acceleration patterns, safety, fuel consumption without disrupting travel times. This research advances knowledge on leveraging CAVs for proactive traffic management and congestion relief. The integrated system contributes a promising solution toward smarter, smoother transportation systems by automatically detecting and mitigating shockwaves before broader congestion materializes. Further validation through field data is recommended. Broader implementation could yield substantial community benefits.