Macroscopic Modeling of Freeway Traffic Using an Artificial Neural Network

Traffic flow on freeways is a complex process that often is described by a set of highly nonlinear, dynamic equations in the form of a macroscopic traffic flow model. However, some of the existing macroscopic models have been found to exhibit instabilities in their behavior and often do not track real traffic data correctly. On the other hand, microscopic traffic flow models can yield more detailed and accurate representations of traffic flow but are computationally intensive and typically not suitable for real-time implementation. Nevertheless, such implementations are likely to be necessary for development and application of advanced traffic control concepts in intelligent vehicle-highway systems. The development of a multilayer feed-forward artificial neural network model to address the freeway traffic system identification problem is presented. The solution of this problem is viewed as an essential element of an effort to build an improved freeway traffic flow model for the purpose of developing real-time predictive control strategies for dynamic traffic systems. To study the initial feasibility of the proposed neural network approach for traffic system identification, a three-layer feed-forward neural network model has been developed to emulate an improved version of a well-known higher-order continuum traffic model. Simulation results show that the neural network model can capture the traffic dynamics of this model quite closely. Future research will attempt to attain similar levels of performance using real traffic data.

[1]  Harold J Payne,et al.  FREFLO: A MACROSCOPIC SIMULATION MODEL OF FREEWAY TRAFFIC , 1979 .

[2]  B. Bavarian,et al.  A neural piecewise linear classifier for pattern classification , 1991, IJCNN-91-Seattle International Joint Conference on Neural Networks.

[3]  Stephen G. Ritchie,et al.  DEVELOPMENT OF AN INTELLIGENT SYSTEM FOR AUTOMATED PAVEMENT EVALUATION , 1991 .

[4]  Panos G. Michalopoulos,et al.  Multilane traffic flow dynamics: Some macroscopic considerations , 1984 .

[5]  Stephen G. Ritchie,et al.  A real-time decision-support system for freeway management and control , 1992 .

[6]  Markos Papageorgiou,et al.  Parameter identification for a traffic flow model , 1979, Autom..

[7]  Markos Papageorgiou,et al.  Applications of Automatic Control Concepts to Traffic Flow Modeling and Control , 1983 .

[8]  Markos Papageorgiou,et al.  Modelling and real-time control of traffic flow on the southern part of Boulevard Peripherique in Paris: Part I: Modelling , 1990 .

[9]  M. Cremer,et al.  An Extended Traffic Flow Model for Inner Urban Freeways , 1987 .

[10]  Warren F. Phillips,et al.  A kinetic model for traffic flow with continuum implications , 1979 .

[11]  Chin Jian Leo,et al.  Numerical simulation of macroscopic continuum traffic models , 1992 .

[12]  M J Lighthill,et al.  On kinematic waves II. A theory of traffic flow on long crowded roads , 1955, Proceedings of the Royal Society of London. Series A. Mathematical and Physical Sciences.

[13]  Harold J Payne,et al.  MODELS OF FREEWAY TRAFFIC AND CONTROL. , 1971 .

[14]  Hussein Dia,et al.  Freeway incident detection using artificial neural networks , 1993 .

[15]  S G Ritchie,et al.  A neural network-based methodology for automated distress classification of pavement images , 1992 .

[16]  Stephen G. Ritchie,et al.  INVESTIGATION OF A NEURAL NETWORK MODEL FOR FREEWAY INCIDENT DETECTION , 1991 .

[17]  E R Case,et al.  EVALUATION OF DYNAMIC FREEWAY FLOW MODEL BY USING FIELD DATA (DISCUSSION AND CLOSURE) , 1983 .

[18]  R L Gordon,et al.  Traffic Control Systems Handbook , 1996 .

[19]  James L. McClelland,et al.  Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .