Two-Stage Stochastic Program for Dynamic Coordinated Traffic Control Under Demand Uncertainty

Abstract

This study develops a cell-based two-stage stochastic program to address the dynamic, spatial and stochastic characteristics of traffic flow for arterial adaptive signal control. To capture demand uncertainty, we formulate the adaptive coordinated traffic signal control as a two-stage stochastic program. To capture dynamic and spatial features of traffic flow, Cell Transmission Model (CTM) is embedded in the two-stage formulation. We incorporate the concept of Phase Clearance Reliability (PCR) to decompose the original two-stage stochastic formulation into separable sub-problems, which greatly enhances solution efficiency. A gradient-based solution algorithm is developed to solve the problem. Numerical examples are constructed to investigate the importance of capturing (or ignoring) each of the dynamic, spatial and stochastic features for traffic control. The results show that failure to account for any of these three traffic flow features will incur a certain extent of delay performance degradation, especially for heavy traffic. Finally, this study validates the findings through VISSIM, with promising results for the newly developed stochastic formulation.