Real-time travel time predictor for route guidance consistent with driver behavior

Canadian Journal of Civil Engineering

Volumn 39, Issue 10, 2012, Pages 1113-1124

  Xu, Tian dong ^a   Hao, Yuan ^b   Peng, Zhong ren ^c,d   Sun, Li jun ^e  

^a ZHEJIANG UNIVERSITY   (China)

^b China Academy of Urban Planning and Design   (China)

^c SHANGHAI JIAO TONG UNIVERSITY   (China)

^d UNIVERSITY OF FLORIDA   (United States)

^e TONGJI UNIVERSITY   (China)

Author keywords

Adaptive kalman filtering; Driver behavior; Macroscopic traffic flow model; Route guidance; Travel time prediction

Indexed keywords

ADAPTIVE KALMAN FILTERING; DRIVER BEHAVIOR; MACROSCOPIC TRAFFIC FLOW MODEL; ROUTE GUIDANCE; TRAVEL TIME PREDICTION;

ADVANCED TRAVELER INFORMATION SYSTEMS; FORECASTING; KALMAN FILTERS; STOCHASTIC MODELS; TIME VARYING CONTROL SYSTEMS; TRAVEL TIME;

BEHAVIORAL RESEARCH;

KALMAN FILTER; MODEL TEST; NUMERICAL MODEL; PREDICTION; REAL TIME; STOCHASTICITY; TRAFFIC CONGESTION; TRAFFIC MANAGEMENT; TRAVEL TIME;

CHINA;

EID: 84866894395     PISSN: 03151468     EISSN: 12086029     Source Type: Journal    
DOI: 10.1139/L2012-092     Document Type: Article

References (26)

1. Ben-Akiva, M., Koutsopoulos, H.N., and Mishalani, A. 1994. A dynamic traffic model system forATMS/ATIS operations. IVHS Journal, 2(l): 1-19.
2. Chen, C., Skabardonis, A., and Varaiya, P. 2004. A system for displaying travel times on changeable message signs. Research Report UCB-ITSPRR-2004-5. California Partners for Advanced Transit and Highways, Berkeley.
3. Chien, S., and Kuchipudi, C.M. 2003. Dynamic travel time prediction with real time and historic data. Journal of Transportation Engineering, 129(6): 608-616. doi:10.1061/(ASCE)0733-947X(2003)129:6(608).
4. Clark, S. 2003. Traffic prediction using multivariate nonparametricregression. Journal of Transportation Engineering, 129(2): 161-168. doi:10.1061/(ASCE)0733-947X(2003)129:2(161).
5. Dougherty, M.S., and Cobbett, M.R. 1997. Short-term inter-urban traffic forecasts using neural networks. International Journal of Forecasting, 13(1): 21-31. doi:10.1016/S0169-2070(96)00697-8.
6. El Esawey, M., and Sayed, T. 2011. Travel time estimation in urban networks using limited probes data. Canadian Journal of Civil Engineering, 38(3): 305-318. doi:10.1139/L11-001
7. Kalman, R.E. 1960. A new approach to linear filtering and prediction problems. Transactions of the ASME, Journal of Basic Engineering, 82(1): 34-35.
8. Lee, S., and Fambro, D.B. 1999. Application of subset autoregressive integrated moving average model for short-term freeway traffic volume forecasting. Transportation Research Record. Journal of the Transportation Research Board, No. 1678(1): 179-188. doi:10.3141/1678-22
9. Lin, W.H., Kulkarni, A., and Mirchandani, P. 2004. Short-term arterial travel time prediction for advanced traveler information systems. Journal of Intelligent Transportation Systems, 8(3): 143-154. doi:10.1080/15472450490492833
10. Mahmassani, H.S., Lu, C.C., and Dong, J. 2005. Value of information: Provision of anticipatory descriptive travel information through a real-time traffic estimation and prediction system. Proceedings of the 12th World Congress on Intelligent Transportation Systems, San Francisco California, United States, 6-10 November 2005. Intelligent Transportation Society of American, Washington, D.C.
11. Mark, C.D., Sadek, A.W., and Rizzo, D. 2004. Predicting experienced travel time with neural networks: A PARAMICS simulation study. Proceedings of the 2004 IEEE Intelligent Transportation Systems Conference, Washington, D.C.
12. Messmer, A., and Papageorgiou, M. 1995. Route diversion control in motorway networks via nonlinear optimization. IEEE Transactions on Control Systems Technology, 3(1): 144-154. doi:10.1109/87.370720.
13. Nikovski, D., Nishiuma, N., Goto, Y., and Kumazawa, H. 2005. Univariate short-term prediction of road travel times. Proceedings of the 2005 IEEE Intelligent Transportation Systems Conference, Vienna, Austria.
14. Papageorgiou, M. 1983. Applications of automatic control concepts to traffic flow modeling and control. Spring-Verlag, New York.
15. Rice, J., and van Zwet, E. 2004. A simple and effective method for predicting travel times on freeways. IEEE Transactions on Intelligent Transportation Systems, 5(3): 200-207. doi:10.1109/TITS.2004.833765.
16. Smith, B.L., Williams, B.M., and Oswald, R.K. 2002. Comparison of parametric and nonparametric models for traffic flow forecasting. Transportation Research Part C, Emerging Technologies, 10(4): 303-321. doi:10.1016/S0968-090X(02)00009-8.
17. Sun, L.J., Xu, T.D., and Du, Y.C. 2008. Key technologies in Shanghai VMS-based advanced traffic guidance system. Shanghai Municipal Transportation Information Center, Shanghai.
18. Sun, L., Gu, W.J., and Mahmassani, H. 2011. Estimation of expected travel time using themethod of moment. Canadian Journal of Civil Engineering, 38(2): 154-165. doi:10.1139/L10-115.
19. Wang, Y.B., Papageorgiou, M., and Messmer, A. 2006. RENAISSANCE-A unified macroscopic model-based approach to realtime freeway network traffic surveillance. Transportation Research Part C, Emerging Technologies, 14(3): 190-212. doi:10.1016/j.trc.2006.06.001.
20. Xu, T.D. 2007. Travel time prediction of urban expressway in unstable traffic flow. International Conference on Transportation Engineering 2007, American Society of Civil Engineers.
21. Xu, T.D. 2010. Research on route guidance of urban freeway traffic network based on VMS and evaluation of demonstration project. Ph.D thesis, School of Transportation Engineering, Tongji University, Shanghai.
22. Xu, T.D., Sun, L.J., and Peng, Z.R. 2011a. Empirical analysis and modeling of drivers' response to variable message signs in shanghai, china. Transportation Research Record, 2243(-1): 99-107. doi:10.3141/2243-12.
23. Xu, T.D., Sun, L.J., Peng, Z.R., and Hao, Y. 2011b. Integrated route guidance and ramp metering consistent with drivers' en-route diversion behaviour. IET Intelligent Transport Systems, 5(4): 267-276. doi:10.1049/iet-its.2011.0073.
24. Yu, J., Park, S.Y., and Chang, G.L. 2010. Advanced traveler information system for guiding route choice to ocean city, Maryland. Transportation Research Record. Transportation Research Record, 2189(1): 56-67. doi:10.3141/2189-07.
25. Zhong, M., Sharma, S., and Lingras, P. 2005. Refining genetically designed models for improved traffic prediction on rural roads. Transportation Planning and Technology, 28(3): 213-236. doi:10.1080/03081060500120340.
26. Zou, N., Wang, J.W., Chang, G.L., and Paracha, J. 2009. Application of advanced traffic information systems: Field test of a travel-time prediction system with widely spaced detectors. Transportation Research Record. Transportation Research Record, 2129(1): 62-72. doi:10.3141/2129-08.