Macroscopic Modeling of Freeway Traffic Using an Artificial Neural Network
Abstract
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.
Get full access to this article
View all access and purchase options for this article.
References
1. Traffic Control Systems Handbook. Publication LP-123. ITE, Washington, D.C., 1985.
2. Derzko N. A., Ugge A. J., and Case E. R. Evaluation of Dynamic Freeway Flow Model by Using Field Data. In Transportation Research Record 905, TRB, National Research Council, Washington, D.C., 1983, pp. 52–60.
3. Rumelhart D. E., Hinton G. E., and Williams R. J. Parallel Distributed Processing, Vol. I. MIT Press, Cambridge, Mass., 1986.
4. Lo Z. P., and Bavarian B. A Neural Piecewise Linear Classifier for Pattern Classification. Proc., International Joint Neural Network Conference, Vol. 1, 1991, pp. 264–268.
5. Ritchie S. G., Kaseko M., and Bavarian B. Development of an Intelligent System for Automated Pavement Evaluation. In Transportation Research Record 1311, TRB, National Research Council, Washington, D.C., 1991, pp. 112–119.
6. Cheu R. L., Ritchie S. G., Recker W. W., and Bavarian B. Investigation of a Neural Network Model for Freeway Incident Detection. In Artificial Intelligence and Civil and Structural Engineering (Topping B. H. V., ed.), Civil-Comp Press, 1991, pp. 267–274.
7. Ritchie S. G., Cheu R. L., and Recker W. W. Freeway Incident Detection Using Artificial Neural Networks. Proc., International Conference on A.I. Applications in Transportation Engineering (Ritchie S. G. and Hendrickson C., eds.), San Buenaventura, Calif., 1992.
8. Kaseko M., and Ritchie S. G. A Neural Network-Based Methodology for Automated Distress Classification of Pavement Images. Proc., International Conference on A.I. Applications in Transportation Engineering (Ritchie S. G. and Hendrickson C., eds.), San Buenaventura, Calif., 1992.
9. Lighthill M. J., and Whitham G. B. On Kinematic Waves: II. A Theory of Traffic Flow on Long Crowded Roads. Proc., Royal Society, London, Series A, Vol. No. 229, 1178, 1955, pp. 317–345.
10. Michalopoulos P. G., Beskos D., and Yamauchi Y. Multilane Traffic Flow Dynamics: Some Macroscopic Considerations. Transportation Research B, Vol. 18B, No. 4/5, 1984, pp. 377–395.
11. Papageorgiou M. Applications of Automatic Control Concepts to Traffic Flow Modeling and Control. Springer-Verlag, 1983.
12. Payne H. J. Models of Freeway Traffic and Control. Simulation Councils Proceedings Series: Mathematical Models of Public Systems, Vol. 1, No. 1 (Bekey G. A., ed.), 1971.
13. Phillips W. F. A Kinetic Model for Traffic Flow with Continuum Implications. Transportation Planning and Technology, Vol. 5, 1979, pp. 131–138.
14. Prigogine I. A Boltzmann-Like Approach to Statistical Theory of Traffic Flow. In Theory of Traffic Flow (Herman R., ed.), Elsevier, Amsterdam, the Netherlands, 1961, pp. 158–164.
15. Phillips W. F. Kinetic Model for Traffic Flow. Report DOT/RSPD/DPB/50-77/17. U.S. Department of Transportation, 1977.
16. Phillips W. F. A New Continuum Model for Traffic Flow. Utah State University, Logan, 1979.
17. Papageorgiou M., Blosseville J., and Hadj-Salem H. Modeling and Real-Time Control of Traffic Flow on the Southern Part of Boulevard Peripherique in Paris: Part I: Modeling. Transportation Research A, Vol. 24A, No. 5, 1990, pp. 345–359.
18. Payne H. J. FREFLO: A Macroscopic Simulation Model of Freeway Traffic. In Transportation Research Record 722, TRB, National Research Council, Washington, D.C., 1979, pp. 68–75.
19. Zhang H. A Real-Time Decision-Support System for Freeway Management and Control. M.S. thesis. Department of Civil Engineering, University of California, Irvine, 1992.
20. Hauer E., and Hurdle V. F. Discussion of FREFLO: A Macroscopic Simulation Model of Freeway Traffic. In Transportation Research Record 722, TRB, National Research Council, Washington, D.C., 1979, pp. 75–77.
21. Cremer M., and May A. D. An Extended Traffic Flow Model for Inner Urban Freeways. Preprints, 5th IFAC/IFIP/IFORS International Conference on Control in Transportation Systems, Vienna, Austria, 1986, pp. 383–388.
22. Leo C. J., and Pretty R. L. Numerical Simulations of Macroscopic Continuum Traffic Models. Transportation Research B, 26B, No. 3, 1992, pp. 207–220.
23. Cremer M., and Papageorgiou M. Parameter Identification for a Traffic Flow Model. Automatica, 1981, pp. 17, 837–843.
Cite article
Cite article
Cite article
OR
Download to reference manager
If you have citation software installed, you can download article citation data to the citation manager of your choice
Information, rights and permissions
Information
Published In
Article first published: January 1997
Issue published: January 1997
Authors
Metrics and citations
Metrics
Journals metrics
This article was published in Transportation Research Record: Journal of the Transportation Research Board.
VIEW ALL JOURNAL METRICSArticle usage*
Total views and downloads: 28
*Article usage tracking started in December 2016
Altmetric
See the impact this article is making through the number of times it’s been read, and the Altmetric Score.
Learn more about the Altmetric Scores
Articles citing this one
Receive email alerts when this article is cited
Web of Science: 0
Crossref: 36
- Short‐term prediction of traffic flow under incident conditions using ...
- Artificial Neural Networks for Forecasting Passenger Flows on Metro Li...
- Survey of neural network‐based models for short‐term traffic state pre...
- Spatial Extension of Road Traffic Sensor Data with Artificial Neural N...
- Artificial neural networks for forecasting user flows in transportatio...
- A spatial–temporal-based state space approach for freeway network traf...
- A functional approach to monitor and recognize patterns of daily traff...
- Flow rate and time mean speed predictions for the urban freeway networ...
- Methods for numerical calculation of parameters pertaining to the micr...
- Dynamic traffic prediction for insufficient data roadways via automati...
- A Hybrid Tree Approach to Modeling Alternate Route Choice Behavior Wit...
- A dynamic network loading process with explicit delay modelling
- Using automatic passenger counter data in bus arrival time prediction
- Public transportation trip flow modeling with generalized regression n...
- Modelling public transport trips by radial basis function neural netwo...
- Neural Network-Wavelet Microsimulation Model for Delay and Queue Lengt...
- Short-Term Freeway Traffic Flow Prediction: Bayesian Combined Neural N...
- A Real-Time Traffic Information Prediction Model Based on AOSVR and On...
- Refining Genetically Designed Models for Improved Traffic Prediction o...
- Short-Term Traffic Prediction on Different Types of Roads with Genetic...
- A Dynamic Bus-Arrival Time Prediction Model Based on APC Data
- Optimizing Traffic Prediction Performance of Neural Networks under Var...
- Online Recursive Algorithm for Short-Term Traffic Prediction
- Hybrid Model-Based and Memory-Based Traffic Prediction System
- Analyzing the Performance of Genetically Designed Short-Term Traffic P...
- A multivariate state space approach for urban traffic flow modeling an...
- Optimization of Dynamic Neural Network Performance for Short-Term Traf...
- Dynamic Bus Arrival Time Prediction with Artificial Neural Networks
- Coordinated traffic-responsive ramp control via nonlinear state feedba...
- Time Delay Neural Networks Designed Using Genetic Algorithms for Short...
- Real-Time Prediction of Traffic Flows Using Dynamic Generalized Linear...
- Simultaneously Prediction of Network Traffic Flow Based on PCA-SVR
- A neural network approach for freeway traffic flow prediction
- A recursive traffic flow predictor based on dynamic generalized linear...
- Spectral and cross-spectral analysis of urban traffic flows
- Developing a driver compliance model for ATIS
Figures and tables
Figures & Media
Tables
View Options
Get access
Access options
If you have access to journal content via a personal subscription, university, library, employer or society, select from the options below:
loading institutional access options
Alternatively, view purchase options below:
Purchase 24 hour online access to view and download content.
Access journal content via a DeepDyve subscription or find out more about this option.
