Experienced travel time prediction for congested freeways

Transportation Research Part B: Methodological

Volumn 53, Issue , 2013, Pages 45-63

  Yildirimoglu, Mehmet ^a   Geroliminis, Nikolas ^a  

^a EPFL   (Switzerland)

Author keywords

Congestion maps; Freeway; Prediction; Traffic flow; Travel times

Indexed keywords

CLUSTERING ALGORITHMS; FORECASTING; HIGHWAY SYSTEMS; STOCHASTIC SYSTEMS; STREET TRAFFIC CONTROL; TIME VARYING CONTROL SYSTEMS; TRAFFIC CONTROL; TRANSPORTATION; TRANSPORTATION ROUTES; TRAVEL TIME;

BOTTLENECK IDENTIFICATIONS; CONGESTION MAPS; METHODOLOGICAL FRAMEWORKS; REAL-TIME TRAFFIC INFORMATION; STATISTICAL FORECASTING; TRAFFIC FLOW; TRAVEL TIME INFORMATION; TRAVEL TIMES;

TRAFFIC CONGESTION;

ALGORITHM; MOTORWAY; PREDICTION; TRAFFIC CONGESTION; TRAVEL TIME;

CALIFORNIA; UNITED STATES;

EID: 84877133254     PISSN: 01912615     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.trb.2013.03.006     Document Type: Article

References (39)

1. Adeli H. Neural networks in civil engineering: 1989-2000. Computer-Aided Civil and Infrastructure Engineering 2001, 16(2):126-142.
2. Chen C., Petty K., Skabardonis A., Varaiya P. Freeway performance measurement: mining loop detector data. Transportation Research Record 2001, 1748:96-102.
3. Chen C., Skabardonis A., Varaiya P. Travel time reliability as measure of service. Transportation Research Record 2003, 1855:74-79.
4. Chen C., Skabardonis A., Varaiya P. Systematic identification of freeway bottlenecks. Transportation Research Record 2004, 1867:46-52.
5. Coifman B. Estimating travel times and vehicle trajectories on freeways using dual loop detectors. Transportation Research Part A 2002, 36(4):351-364.
6. Coifman B., Krishnamurthy S. Vehicle re-identification and travel time measurement across freeway junctions using the existing detector infrastructure. Transportation Research Part C 2007, 15(3):135-153.
7. Dempster A.P., Laird N.M., Rubin D.B. Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society Series B (Methodological) 1977, 39(1):1-38.
8. Dharia A., Adeli H. Neural network model for rapid forecasting of freeway link travel time. Engineering Applications of Artificial Intelligence 2003, 16(7-8):607-613.
9. Dion F., Rakha H. Estimating dynamic roadway travel times using automatic vehicle identification data for low sampling rates. Transportation Research Part B 2006, 40(9):745-766.
10. Du L., Peeta S., Kim Y.H. An adaptive information fusion model to predict the short-term link travel time distribution in dynamic traffic networks. Transportation Research Part B 2012, 46(1):235-252.
11. Fei X., Lu C.-C., Liu K. A Bayesian dynamic linear model approach for real-time short-term freeway travel time prediction. Transportation Research Part C 2011, 19(6):1306-1318.
12. Herrera J.C., Bayen A.M. Incorporation of Lagrangian measurements in freeway traffic state estimation. Transportation Research Part B 2010, 44(4):460-481.
13. Ji Y., Geroliminis N. On the spatial partitioning of urban transportation networks. Transportation Research Part B 2012, 46(10):1639-1656.
14. Jiang X., Adeli H. Wavelet packet-autocorrelation function method for traffic flow pattern analysis. Computer Aided Civil Infrastructure Engineering 2004, 19(5):324-337.
15. Kothuri S.M., Tufte K.A., Fayed E., Bertini R.L. Toward understanding and reducing errors in real-time estimation of travel times. Transportation Research Record 2008, 2049:21-28.
16. Leclercq L., Laval J.A., Chiabaut N. Capacity drops at merges: an endogenous model. Transportation Research Part B 2011, 45(9):1302-1313.
17. Ledoux C. An urban traffic flow model integrating neural networks. Transportation Research Part C 1997, 5(5):287-300.
18. Li X., Peng F., Ouyang Y. Measurement and estimation of traffic oscillation properties. Transportation Research Part B 2010, 44(1):1-14.
19. Liu Y., Lin P.W., Lai X.R., Chang G.L., Marquess A. Developments and applications of simulation-based online travel time prediction system: traveling to Ocean City, Maryland. Transportation Research Record 2006, 1959:92-104.
20. Mazaré, P.E., Tossavainen, O.P., Bayen, A., Work, D., 2012. Trade-offs between inductive loops and GPS vehicles for travel time estimation: a mobile century case study. In: Transportation Research Board 91st Annual Meeting, Washington, DC.
21. Milligan G. An examination of the effect of six types of error perturbation on fifteen clustering algorithms. Psychometrika 1980, 45(3):325-342.
22. Min W., Wynter L. Real-time road traffic prediction with spatio-temporal correlations. Transportation Research Part C 2011, 19(4):606-616.
23. Nagendra S.M.S., Khare M. Principal component analysis of urban traffic characteristics and meteorological data. Transportation Research Part D 2003, 8(4):285-297.
24. Nanthawichit C., Nakatsuji T., Suzuki H. Application of probe-vehicle data for real-time traffic-state estimation and short-term travel-time prediction on a freeway. Transportation Research Record 2003, 1855:49-59.
25. Newell G.F. A simplified theory of kinematic waves in highway traffic. Part I, general theory. Transportation Research Part B 1993, 27(4):281-287.
26. Okutani I., Stephanedes Y.J. Dynamic prediction of traffic volume through Kalman filtering theory. Transportation Research Part B 1984, 18(1):1-11.
27. Rousseeuw P.J. Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. Computational and Applied Mathematics 1987, 20:53-65.
28. Treiber M., Kesting A., Helbing D. Three-phase traffic theory and two-phase models with a fundamental diagram in the light of empirical stylized facts. Transportation Research Part B 2010, 44(8-9):983-1000.
29. Van Lint J.W.C. Reliable real-time framework for short-term freeway travel time prediction. Journal of Transportation Engineering 2006, 132(12):921-932.
30. Van Lint J.W.C. Online learning solutions for freeway travel time prediction. IEEE Transactions on Intelligent Transportation Systems 2008, 9(1):38-47.
31. Vanajakshi, L., Rilett, L.R., 2007. Support vector machine technique for the short term prediction of travel time. In: IEEE Proceedings of Intelligent Vehicles Symposium, Istanbul, Turkey, pp. 600-605.
32. Vlahogianni E.I., Geroliminis N., Skabardonis A. Empirical and analytical investigation of traffic flow regimes and transitions in signalized arterials. Journal of Transportation Engineering 2008, 134(12):512-522.
33. Vlahogianni E.I., Karlaftis M.G., Golias J.C. Optimized and meta-optimized neural networks for short-term traffic flow prediction: a genetic approach. Transportation Research Part C 2005, 13(3):211-234.
34. Wang Y., Papageorgiou M., Messmer A. Real-time freeway traffic state estimation based on extended Kalman filter: adaptive capabilities and real data testing. Transportation Research Part A 2008, 42(10):1340-1358.
35. Weijermars, W., Van Berkum, E., 2005. Analyzing highway flow patterns using cluster analysis. In: 8th International IEEE Annual Conference on Intelligent Transportation Systems, Vienna, Austria, pp. 831-836.
36. Wieczorek J., Fernández-Moctezuma R.J., Bertini R.L. Techniques for validating an automatic bottleneck detection tool using archived freeway sensor data. Transportation Research Record 2010, 2160:96-106.
37. Yang, J.-S., 2005. A study of travel time modeling via time series analysis. In: Proceedings of IEEE Conference on Control Applications, Toronto, Canada, pp. 855-860.
38. Yeon J., Elefteriadou L., Lawphongpanich S. Travel time estimation on a freeway using discrete time Markov chains. Transportation Research Part B 2008, 42(4):325-338.
39. Zhang X., Rice J.A. Short-term travel time prediction. Transportation Research Part C 2003, 11(3-4):187-210.