Travel time prediction using k nearest neighbor method with combined data from vehicle detector system and automatic toll collection system

Transportation Research Record

Volumn , Issue 2256, 2011, Pages 51-59

  Myung, Jiwon ^a   Kim, Dong Kyu ^b   Kho, Seung Young ^b   Park, Chang Ho ^b  

^a SK C and C Ltd   (South Korea)

^b Seoul National University   (South Korea)

Author keywords

[No Author keywords available]

Indexed keywords

DECISION MAKING; FORECASTING; MOTION COMPENSATION; NEAREST NEIGHBOR SEARCH; NEURAL NETWORKS; TOLL HIGHWAYS; TRAFFIC CONTROL;

ACCURATE PREDICTION; DECISIONS MAKINGS; DEPARTURE TIME; K-NEAREST NEIGHBORS METHOD; MODEL PREDICTION; TECHNOLOGY DRIVERS; TOLL COLLECTION SYSTEMS; TRAVEL TIME PREDICTION; TRAVEL-TIME; VEHICLE DETECTOR SYSTEMS;

TRAVEL TIME;

EID: 84863041488     PISSN: 03611981     EISSN: None     Source Type: Journal    
DOI: 10.3141/2256-07     Document Type: Article

References (19)

1. Chen, M., and S. I. J. Chien. Dynamic Freeway Travel-Time Prediction with Probe Vehicle Data: Link Based Versus Path Based. In Transporta-tion Research Record: Journal of the Transportation Research Board, No. 1768, TRB, National Research Council, Washington, D.C., 2001, pp. 157-161. (Pubitemid 34254756)
2. Chien, S. I. J., X. Liu, and K. Ozbay. Predicting Travel Times for South Jersey Real-Time Motorist Information System. In Transporta-tion Research Record: Journal of the Transportation Research Board, No. 1855, Transportation Research Board of the National Academies, Washington, D.C., 2003, pp. 32-40.
3. Robinson, S., and J. W. Polak. Modeling Urban Link Travel Time with Inductive Loop Detector Data by Using the k-NN Method. In Trans-portation Research Record: Journal of the Transportation Research Board, No. 1935, Transportation Research Board of the National Acad-emies, Washington, D.C., 2005, pp. 47-56.
4. Soriguera, F., D. Abeijón, D. Rosas, L. Thorson, and F. Robuste. Travel Time Estimation from Multiple Data Sources. Presented at 11th World Conference on Transport Research, Berkeley, California, 2007.
5. D'Angelo, M. P., H. M. Al-Deek, and M. C. Wang. Travel-Time Predic-tion for Freeway Corridors. In Transportation Research Record: Journal of the Transportation Research Board, No. 1676, TRB, National Research Council, Washington, D.C., 1999, pp. 184-191.
6. Kwon, J., B. Coifman, and P. Bickel. Day-to-Day Travel-Time Trends and Travel-Time Prediction from Loop-Detector Data. In Transporta-tion Research Record: Journal of the Transportation Research Board, No. 1717, TRB, National Research Council, Washington, D.C., 2000, pp. 120-129. (Pubitemid 32792416)
7. Zhang, G., B. E. Patuwo, and M. Y. Hu. Forecasting with Artificial Neural Networks: The State of the Art. International Journal of Fore-casting, Vol. 14, No. 1, 1998, pp. 35-62. (Pubitemid 128340470)
8. 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. (Pubitemid 128553708)
9. Huisken, G., and E. C. van Berkum. Comparative Analysis of Short-Range Travel Time Prediction Methods. Presented at 82nd Annual Meeting of the Transportation Research Board, Washington, D.C., 2003.
10. van Lint, J. W. C., S. P. Hoogendoorn, and H. J. van Zuylen. Freeway Travel Time Prediction with State-Space Neural Networks: Modeling State-Space Dynamics with Recurrent Neural Networks. In Transporta-tion Research Record: Journal of the Transportation Research Board, No. 1811, Transportation Research Board of the National Academies, Washington, D.C., 2002, pp. 30-39.
11. van Lint, J. W. C., S. P. Hoogendoorn, and H. J. van Zuylen. Accurate Freeway Travel Time Prediction with State-Space Neural Networks Under Missing Data. Transportation Research Part C, Vol. 13, 2005, pp. 347-369.
12. Liu, H., H. J. van Zuylen, H. van Lint, and M. Salomons. Predicting Urban Arterial Travel Time with State-Space Neural Networks and Kalman Filters. In Transportation Research Record: Journal of the Transportation Research Board, No. 1968, Transportation Research Board of the National Academies, Washington, D.C., 2006, pp. 99-108. (Pubitemid 46167296)
13. Liu, H., H. W. C. van Lint, and H. J. van Zuylen. Neural-Network-Based Traffic Flow Model for Urban Arterial Travel Time Prediction. Pre-sented at 86th Annual Meeting of the Transportation Research Board, Washington, D.C., 2007.
14. Shen, L., and M. Hadi. Freeway Travel Time Prediction with Dynamic Neural Networks. Presented at 89th Annual Meeting of the Transportation Research Board, Washington, D.C., 2010.
15. Gault, H. E., and I. G. Taylor. The Use of the Output from Vehicle Detectors to Assess Delay in Computer-Controlled Area Traffic Control Systems. Research Report 37. Transport Operations Research Group, University of Newcastle, United Kingdom, 1981.
16. Bajwa, S. I., E. Chung, and M. Kuwahara. A Travel Time Prediction Method Based on Pattern Matching Technique. Presented at 11th Con-ference of the Road Engineering Association of Asia and Australasia and the 21st Conference of the Australian Road Research Board, Cairns, Australia, 2003.
17. Bajwa, S. I., E. Chung, and M. Kuwahara. An Adaptive Travel Time Prediction Model Based on Patter Matching. Presented at 11th Intelligent Transportation Systems World Congress, Nagoya, Japan, 2004.
18. Zou, N., J. Wang, G.-L. Chang, and J. Paracha. Application of Advanced Traffic Information Systems: Field Test of a Travel-Time Prediction Sys-tem with Widely Spaced Detectors. In Transportation Research Record: Journal of the Transportation Research Board, No. 2129, Transportation Research Board of the National Academies, Washington, D.C., 2009, pp. 62-72.
19. Highway Capacity Manual. TRB, National Research Council, Washing-ton, D.C., 2000.