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A coordinated control framework of freeway continuous merging areas considering traffic risks and energy consumption
Weihua Zhang1, Fan Zhang1, Zhongxiang Feng1
1School of Automotive and Transportation Engineering, Hefei University of Technology, Hefei 230009 China.
Abstract:
Freeway continuous merging areas in a short distance exist continuous multiple ramps. In these areas, traffic flow and vehicle interactions are more complex, and traffic crashes and congestion are more frequent, which has been a major concern influencing traffic operation of freeways. Active traffic management (ATM) measures can improve traffic efficiency and reduce traffic risks in merging areas. Previous studies have focused on variable speed limit (VSL) control or ramp metering (RM) to address traffic problems in merging areas, whereas the problem of comprehensively ameliorating for traffic risks on mainlines and ramps by coordinating VSL and RM control strategies has rarely been explored. This study introduces a Bi-level Programming Model capable of coordinating controls of traffic risks (e.g., Crash Risk and Congestion Risk) in freeway continuous merging areas. The upper-level model aims to minimize the crash risk, the congestion risk, and vehicle energy consumption by VSL control. While the lower-level model focuses on the ramp control by minimizing the congestion risk and energy consumption of the ramp. Then an extended Cell Transmission Model (CTM) (it is based on VSL and RM control) is utilized to simulate the traffic flow of merging areas, based on which a traffic risk evaluation model and a Bi-level coordinated control model for the continuous merging areas are developed. The results demonstrate the constructed method outperforms other control strategies for improving the safety and efficiency of freeways. Specifically, the proposed control framework in the continuous merging areas of freeways reduces the average crash risk (ACR), average mainline congestion risk (AMCI), and average energy consumption (AEC) by 14.10%, 19.52%, and 8.86%, respectively. The research results could be potentially applied to active and coordinated traffic management of freeways.
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