Combined Traffic Signal Control and Route Guidance: Multiple User Class Traffic Assignment Model versus Discrete Choice Model

Abstract

Intelligent transportation systems (ITS) are the major solutions to relieving traffic congestion. Traffic signal control and route guidance, two key subsystems of ITS, jointly influence traffic flow in time and space. Over the last three decades, combined traffic signal control and route guidance (CTSCRG) has been research emphasis. There are two kinds of model for describing CTSCRG: multiple user class traffic assignment model and discrete choice model. The intent of the paper is to compare the traffic equilibrium results of these two models. For describing route guidance system which is composed of guided driver and non-guided driver, the concept of multiple user class (MUC) is introduced. In MUC traffic assignment model, guided drivers are modeled as complete information user equilibrium traffic assignment, and non-guided drivers are modeled as stochastic user equilibrium traffic assignment. In discrete choice model, guided drivers and non-guided drivers are modeled as stochastic dynamic user optimum and route choice model is logit model. The perception error of non-guided drivers is larger than the perception error of guided drivers. In this paper, perception error is the function of current travel time and sampled from uniform distribution. Because simulation-based method can allow more complex interactions and win in reality value than travel time formula, we combine Hybrid Genetic Algorithm with cellular automata simulation to calculate travel time and optimize signal setting plan. Iterative simulation and assignment procedure is built: road is discretized by cellular automata. Traffic flow dynamics is represented by cell transmission model; signal setting is optimized by hybrid genetic algorithm; vehicle agent can receive route guidance information and select suggested route. Comparison results show that MUC traffic assignment model converge, discrete choice model does not converge. Average delay of MUC traffic assignment model is lower than that of discrete choice model. Perception error has important influence on the resulting performance which is expressed as total delay.