Performance Comparison between Centralized Global Optimization and Distributed Local Optimization for Alleviation of Traffic Congestion

Abstract

In this paper, we consider a traffic flow model where the information about the actual travel time for each alternative route is not available when each driver performs route selection. For such a traffic flow model, we examine the effectiveness of two routing methods for minimizing the average travel time over all vehicles running in the model. One method is to minimize the average travel time globally. We employ a genetic algorithm to search for a near-optimal route choice of each driver. The other method is to minimize the average travel time locally by each driver. We employ a neural network to estimate the travel time for each route. Through computational experiments, we compare the two methods with each other and demonstrate the characteristic features of each method.