Comparative Study on Recognition of Transportation Under Real and UE Status

Abstract

Transportation system is a complex, large, integrated and open system. It’s difficult to recognize the system with analytical methods. So, two neural network models are developed to recognize the system. One is a back propagation neural network to recognize ideal system under equilibrium status, and the other is a counter propagation model to recognize real system with probe vehicle data. By recognizing ideal system, it turn out that neural network can simulate the process of traffic assignment, that is, neural network can simulate mapping relationship between OD matrix and assigned link flows, or link travel times. Similarly, if real-time OD matrix is obtained by probe vehicle technology, and then similarly results like link travel times can be obtained by similarly models. By comparing outputs of two models, difference about real and ideal transportation system can be found.