Hybrid model-based and memory-based traffic prediction system

Transportation Research Record

Volumn , Issue 1879, 2004, Pages 59-70

  Alecsandru, Ciprian ^a   Ishak, Sherif ^a  

^a LOUISIANA STATE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; DECISION MAKING; ERROR ANALYSIS; FEEDFORWARD NEURAL NETWORKS; HIGHWAY TRAFFIC CONTROL; ONLINE SYSTEMS; OPTIMIZATION; RELIABILITY; ROBUSTNESS (CONTROL SYSTEMS);

HYBRID MODEL; INTELLIGENT TRANSPORTATION SYSTEMS (ITS); TRAFFIC FORECASTING; TRAFFIC PREDICTION MODEL;

INTELLIGENT VEHICLE HIGHWAY SYSTEMS;

EID: 14744282511     PISSN: 03611981     EISSN: None     Source Type: Journal    
DOI: 10.3141/1879-08     Document Type: Conference Paper

References (29)

1. Ben-Akiva, M., A. de Palma, and I. Kaysi. Dynamic Network Models and Driver Information Systems. Transportation Research A, Vol. 25, No. 5, 1991, pp. 251-266.
2. Ben-Akiva, M., E. Kroes, J. de Ruiter, S. Smulders, J. Whittaker, and K. Lindveld. Real-Time Prediction of Traffic Congestion. In Vehicle Navigation and Information Systems: Conference Record of Papers. IEEE, New York, 1992, pp. 557-562.
3. Stamatiadis, C., and W. Taylor. Travel Time Predictions for Dynamic Route Guidance with a Recursive Adaptive Algorithm. Presented at 73rd Annual Meeting of the Transportation Research Board, Washington, D.C., 1994.
4. Ishak, S., and H. Al-Deek. Performance Evaluation of a Short-Term Time-Series Traffic Prediction Model. ASCE Journal of Transportation Engineering, Vol. 128, No. 6, 2002, pp. 490-498.
5. Ahmed, M. S., and A. R. Cook. Analysis of Freeway Traffic Time-Series Data by Using Box-Jenkins Techniques. In Transportation Research Record 722, TRB, National Research Council, Washington, D.C., 1979, pp. 1-9.
6. Chang, J. L., and S. P. Miaou. Real-Time Prediction of Traffic Flows Using Dynamic Generalized Linear Models. In Transportation Research Record: Journal of the Transportation Research Board, No. 1678, TRB, National Research Council, Washington, D.C., 1999, pp. 168-178.
7. Hamed, M. M., H. R. Al-Masaeid, and Z. M. B. Said. Short-Term Prediction of Traffic Volume in Urban Arterials. ASCE Journal of Transportation Engineering, Vol. 121, No. 3, 1995, pp. 249-254.
8. Lee, S., and D. Fambro. Application of Subset Autoregressive Integrated Moving Average Model for Short-Term Freeway Traffic Volume Forecasting. In Transportation Research Record: Journal of the Transportation Research Board, No. 1678, TRB, National Research Council, Washington, D.C., 1999, pp. 179-188.
9. Okutani, I., and Y. Stephanedes. Dynamic Prediction of Traffic Volume Through Kalman Filtering Theory. In Transportation Research B, Vol. 18, No. 1, 1984, pp. 1-11.
10. Lu, J. Prediction of Traffic Flow by an Adaptive Prediction System. In Transportation Research Record 1287, TRB, National Research Council, Washington, D.C., 1990, pp. 54-61.
11. Smith, B. L., B. M. Williams, and R. K. Oswald. Comparison of Parametric and Nonparametric Models for Traffic Flow Forecasting. Transportation Research C, Vol. 10, No. 4, 2002, pp. 303-321.
12. Park, D., and L. R. Rilett. Forecasting Multiple-Period Freeway Link Travel Times Using Modular Neural Networks. In Transportation Research Record 1617, TRB, National Research Council, Washington, D.C., 1998, pp. 163-170.
13. Park, D., L. R. Rilett, and G. Han. Spectral Basis Neural Networks for Real-Time Travel Time Forecasting. ASCE Journal of Transportation Engineering, Vol. 125, No. 6, 1999, pp. 515-523.
14. Abdulhai, B., H. Porwal, and W. Recker. Short-Term Freeway Traffic Flow Prediction Using Genetically Optimized Time-Delay-Based Neural Networks. Presented at 78th Annual Meeting of the Transportation Research Board, Washington, D.C., 1999.
15. Zhang, H., S. G. Ritchie, and Z. P. Lo. Macroscopic Modeling of Freeway Traffic Using an Artificial Neural Network. In Transportation Research Record 1588, TRB, National Research Council, Washington, D.C., 1997, pp. 110-119.
16. Fu, L., and L. R. Rilett. Estimation of Time-Dependent, Stochastic Route Travel Times Using Artificial Neural Networks. Transportation Planning and Technology, Vol. 24, No. 1, 2000, pp. 25-36.
17. Ishak, S., and C. Alecsandru. Optimizing Traffic Prediction Performance of Neural Networks Under Various Topological, Input, and Traffic Condition Settings. Presented at 82nd Annual Meeting of the Transportation Research Board, Washington, D.C., 2003.
18. Ishak, S., P. Kotha, and C. Alecsandru. Optimization of Dynamic Neural Network Performance for Short-Term Traffic Prediction. In Transportation Research Record: Journal of the Transportation Research Board, No. 1836, TRB, National Research Council, Washington, D.C., 2003, pp. 45-56.
19. Khattak, A., and A. Kanafani. Case-Based Reasoning: A Planning Tool for Intelligent Transportation Systems. Transportation Research C, Vol. 4, No. 5, 1996, pp. 267-288.
20. Sadek, A. W., M. J. Demetsky, and B. L. Smith. Case-Based Reasoning for Real-Time Traffic Flow Management. Journal of Computer-Aided Civil and Infrastructure Engineering, Vol. 14, No. 5, 1999.
21. Sadek, A. W., B. L. Smith, and M. J. Demetsky. A Prototype Case-Based Reasoning Decision Support System for Real-Time Freeway Traffic Routing. Transportation Research C, Vol. 9, No. 5, 2001.
22. Capus, L., and N. Tourigny. Road Safety Analysis: A Case-Based Reasoning Approach. Presented at 77th Annual Meeting of the Transportation Research Board, Washington D.C., 1998.
23. Sadek, A. W. Hybrid Simulated Annealing and Case-Based Reasoning Approach for Computationally Intensive Transportation Problems: Rationale and Design Issues. In Transportation Research Record: Journal of the Transportation Research Board, No. 1774, TRB, National Research Council, Washington, D.C., 2001.
24. Pal, S. K., T. S. Dillon, and D. S. Yeung. Soft Computing in Case-Based Reasoning. Springer-Verlag, London, 2001.
25. Kolodner, J. Case-Based Reasoning. Morgan Kaufmann, San Mateo, Calif., 1993.
26. Principe, J. C., N. R. Euliano, and W. C. Lefebvre. Neural and Adaptive Systems. John Wiley and Sons, New York, 2000.
27. Ham, F. M., and I. Kostanic. Principles of Neurocomputing for Science and Engineering. McGraw-Hill, New York, 2001.
28. Jang, J. S. R., C. T. Sun, and E. Mizutani. Neuro-Fuzzy and Soft Computing. Prentice Hall, Englewood Cliffs, N.J., 1997.
29. Haykin, S. Neural Networks: A Comprehensive Foundation. Macmillan College Publishing, New York, 1994.