Statistical and neural classifiers to detect traffic operational problems on urban arterials

Transportation Research Part C: Emerging Technologies

Volumn 6, Issue 5-6, 1998, Pages 291-314

  Khan, Sarosh I ^a   Ritchie, Stephen G ^b  

^a The University of Colorado Denver   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

Artificial neural network; Incident detection; Operational problems; Signalized arterials; Surface streets

Indexed keywords

HIGHWAY PLANNING; INTERSECTIONS; NEURAL NETWORKS; PATTERN RECOGNITION; SENSORS; TRAFFIC SIGNALS; URBAN PLANNING;

INCIDENT DETECTION; SIGNALIZED ARTERIALS; URBAN ARTERIALS;

HIGHWAY TRAFFIC CONTROL;

METHODOLOGY; STATISTICAL ANALYSIS; TRAFFIC MANAGEMENT;

ANAHEIM; CALIFORNIA; LOS ANGELES; UNITED STATES;

EID: 0032322630     PISSN: 0968090X     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0968-090X(99)00005-4     Document Type: Article

References (37)

1. Abdulhai, B., 1996. A Neuro-genetic-based universally transferable freeway incident detection framework. Ph.D. Dissertation, Department of Civil and Environmental Engineering, University of California, Irvine.
2. Ahmed S.A., Cook A.R. Application of time-series analysis techniques to freeway incident detection. Transportation Research Record. 841:1982;19-21.
3. Bell, M.G.H., Thancanamootoo, B., 1988. Automatic incident detection within urban traffic control systems. In: Proceedings of the Roads and Traffic 2000 Conference, Berlin, 4(2).
4. Bretherton, R.D., Bowen, G.T., 1991. Incident detection and traffic monitoring in urban areas. In: Proceedings of DRIVE Conference on Advanced Telematics in Road Transport 1.
5. Chen, C., Chang, G., 1993. A dynamic real-time incident detection System for urban arterials - system architecture and preliminary results. In: Proceedings of the ASCE Third International Conference on Applications of Advanced Technologies in Transportation Engineering, Seattle, Washington.
6. Cheu R.L., Ritchie S.G. Automated detection of lane blocking freeway incidents using artificial neural networks. Transportation Research. C3(6):1995;371-388.
7. Duda, R.O., Hart, P.E., 1973. Pattern Classification and Scene Analysis. Wiley, New York.
8. Effron, B., Tibshirami, R.J., 1993. An Introduction to the Bootstrap. Chapman & Hall, NY.
9. Eshraghnia-Jahromi, A., 1992. Arterial delay and performance seen through traffic models. Ph.D. Thesis.
10. Federal Highway Administration, 1992. TRAF User Reference Guide. FHWA-RD-92-060.
11. Hampshire, J.B., Waibel, A.H., 1989. The meta-pi network: building distributed knowledge representations for robust pattern recognition. Technical Report No. CMU-CS-89-166. Carnegie-Mellon University, Pittsburg, PA.
12. Han, D.L., May, A.D., 1988. Artificial intelligence approaches for signalized network control. Working Paper, University of California, Berkeley.
13. Han, D.L., May, A.D., 1989. Automatic detection of traffic operational problems on urban arterials. Research Report, University of California, Berkeley.
14. Han, D.L., May, A.D., 1990. Traffic flow characteristics of signalized arterials under disturbance situations. Research Report, University of California, Berkeley.
15. Heicht-Nielsen, R., 1990. Neurocomputing. Addison-Wesley, Reading, MA.
16. Ivan, J.N., Schofer, J.L., Koppelman, F.S., 1995. Real-time data fusion for arterial street incident detection using neural networks. In: Proceedings of Transportation Research Board, 74th Annual Meeting, Washington DC.
17. Jacobs, R.A., Jordan, M.I., 1991. A competitive modular connectionist architecture. In: Lippmann, R.P., Moody, J.E., Tourezky, D.J. (Eds.), Advances in Neural Information Processing Systems 3, Morgan Kaufmann, San Mateo, CA, pp. 767-773.
18. Jacobs R.A., Jordan M.I., Nowlan S.J., Hinton G.E. Adaptive mixtures of local experts. Neural Computation. 3:1991;79-87.
19. Jacobs R.A., Jordan M.I., Barto A.G. Task decomposition through competition in a modular connectionist architecture: The what and where vision tasks. Cognitive Science. 15:1991;219-250.
20. Johnson, R.A., Wichern, D.W., 1992. Applied Multivariate Statistical Analysis. Prentice Hall, NJ.
21. Khan, S.I., 1995. Modular Neural Network Architecture for Detection of Operational Problems on Urban Arterials. Ph.D. Dissertation, Department of Civil and Environmental Engineering, University of California, USA.
22. Levin, M., Krause, G.M., 1978. Incident detection: A Bayesian approach. Transportation Research Record 682.
23. Payne H.J., Tignor S.C. Freeway incident-detection algorithms based on decision tree with states. Transportation Research Record. 682:1978;30-37.
24. Peat, Marwick, Mitchell & Company, 1971. Network flow simulation for urban traffic control system - phase II FHWA-RD-73, 83-87.
25. Persaud B., Hall F.L. Catastrophe theory and pattern in 30 second freeway traffic data-implication for incident detection. Transportation Research. 23A(2):1989;103-113.
26. Rathi, A.J., Santiago, A., 1990a. The new NETSIM simulation program. Traffic Engineering and Control 31(5).
27. Rathi, A.J., Santiago, A., 1990b. Urban network traffic simulation: TRAF-NETSIM program. Journal of Transportation Engineering 116(6).
28. Ritchie S.G. A knowledge based decision support architecture for advanced traffic management. Transportation Research, Part A. 24A(1):1990;27-37.
29. Ritchie S.G., Cheu R.L. Simulation of freeway incident detection using artificial neural networks. Transportation Research. C1(3):1993;203-217.
30. Rosenblatt F. The perceptron: a probabilistic model for information storage and organization of the brain. Psychological Review. 65:1958;386-408.
31. Rumelhart, D.E., McClelland, J.L., 1986. Parallel Distributed Processing: Explorations in the Microstructure of Cognition, vol. 1. MIT Press, Cambridge, MA.
32. Sellam S., Boulmakoul, A., Pierrelee, 1991. A distributed real time knowledge-based system using video image processing for junctions automatic incident detection. In: Applications of Advanced Technologies in Transportation Engineering, Proceedings of the Second International Conference on Applications of Advanced Technologies in Transportation Engineering, ASCE.
33. Serano, T.J., 1990. NETSIM: A Computer Program for Simulating Detection and Location Capability of Regional Seismic Networks. Science Applications International Corporation, San Diego.
34. Sethi, V., Bhandari, F.S., Koppelman, Schoffer, J.L., 1995. Arterial incident detection using fixed detector and probe vehicle data. Transportation Research C3(2) 99-112.
35. Sulzberg, J.D., Demetsky, M. J., 1991. Demonstration of TRAF-NETSIM for Traffic Operations Management, Virginia Transportation Research Council.
36. Thancanamootoo, S., Bell, M.G.H., 1988. Automatic detection of traffic incidents on a signal-controlled road network. Research report, University of Newcastle upon Tyne, Transport Operations Research Group no. 76.
37. Williams J.C., Mahmassani H.S., Herman R. Sampling strategies for two-fluid model parameter estimation in urban network. Transportation Research, Part A. 29A(3):1995;229-244.