Optimal Variable, Lane Group–Based Speed Limits at Freeway Lane Drops: A Multiobjective Approach

Abstract

This study develops optimal variable, lane group–based, speed limits for traffic control at freeway lane drop areas (e.g., work zones). The proposed approach adopts a simulation-optimization framework that utilizes a calibrated and validated macroscopic traffic flow model METANET, along with the microscopic traffic simulation model VISSIM, to develop the optimal speed limits. A multiobjective optimization framework is implemented whereby the model primarily seeks to improve traffic safety by reducing the average number of stops, while taking other objectives, such as the average travel time and throughput, into consideration. For optimization, the heuristic, biologically-inspired optimization technique known as particle swarm optimization (PSO), is utilized, and the ε-constraint method is adopted to allow for considering multiple objectives in the optimization process. The proposed traffic control strategy is then evaluated for a hypothetical freeway lane drop area under a real-world congested traffic scenario. The research findings show that the proposed lane group–based control strategy outperforms other link-based, variable speed limits reported in the literature.