A cooperative longitudinal lane-changing distributions advisory for a freeway weaving segment

Abstract

The lane-changing (LC) concentration problem in freeway weaving segments poses crash risks and reduces freeway efficiency. To address this issue, this paper proposes a cooperative longitudinal LC distribution (CLLCD) advisory for freeway weaving segments utilizing cooperative intelligent transport system technology. The weaving segment is divided into sections, and the CLLCD strategy distributes lane changes for each section using a general rule that allows easy calculation of each section’s CLLCD from the maximum permitted number of lane changes for different movements. A corresponding percentage of drivers in each section are then permitted to change lanes from the start of that section. The CLLCD strategy is evaluated for 27 scenarios with varying traffic demands. A sensitivity analysis is conducted to determine optimal parameters, and the performance of the proposed strategy is compared to other methods. This study also explores the working mechanism of the proposed approach using headway data and speed profiles. The effects of the section configurations and penetration rates of connected vehicles (CVs) are discussed. Simulation results show that this easy-to-apply strategy improves speed and delay as effectively as the heuristic algorithms-based strategy. The number of sections does not influence the CLLCD strategy’s performance when the maximum freeway-to-ramp lane changes per section and other parameters per 100 m remain constant. The delay in the weaving area decreases as the CV penetration rate increases; however, only marginal further improvements are observed when the penetration rate increases beyond 60%. This study provides a practical and effective solution to enhance weaving segments’ efficiency.