Based on Hybrid Genetic Algorithm and Cellular Automata Combined Traffic Signal Control and Route Guidance

Abstract

Signal control and route guidance 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. Firstly, the conceptual structure and definition of CTSCRG was analyzed. Then, the mathematical models of CTSCRG were summarized. Link travel time function and signal control policy have significant influence on solution uniqueness and convergence of CTSCRG model. Simulation-based method can allow more complex interactions, therefore win in reality value than travel time formula. The paper combines 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. The simulation result is encouraging, when combined traffic signal control and route guidance equilibrium converge, the saving in total travel time is 54.4%.