Time-Varying Lane-Based Capacity Reversibility for Traffic Management

Abstract

Many metropolitan areas have adopted various traffic management techniques to maintain an efficient traffic flow. This article proposes a new bi-level formulation for the time-varying lane-based capacity reversibility problem for traffic management. The problem is formulated as a bi-level program where the lower level is the cell-transmission-based user-optimal dynamic traffic assignment (UODTA). Due to its Non-deterministic Polynomial-time hard (NP-hard) complexity, the genetic algorithm (GA) with the simulation-based UODTA is adopted to solve multi-origin multi-destination problems. Four GA variations are proposed. GA1 is a simple GA. GA2, GA3, and GA4, with a jam-density factor parameter (JDF), employ time-dependent congestion measures in their decoding procedures. The four algorithms are empirically tested on a grid network and compared based on solution quality, convergence speed, and central processing unit (CPU) time. GA3 with JDF of 0.6 appears to be the best on the three criteria. On the Sioux Falls network, GA3 with JDF of 0.7 performs the best. The GA with the appropriate inclusion of problem-specific knowledge and parameter calibration indeed provides excellent results when compared with the simple GA.