A gradient boosting method to improve travel time prediction

Transportation Research Part C: Emerging Technologies

Volumn 58, Issue , 2015, Pages 308-324

  Zhang, Yanru ^a   Haghani, Ali ^a  

^a UNIVERSITY OF MARYLAND   (United States)

Author keywords

Ensemble learning; Gradient boosting regression tree; Machine learning; Random forest; Short term forecasting; Travel time

Indexed keywords

ADAPTIVE BOOSTING; DECISION TREES; FORECASTING; LEARNING SYSTEMS; MACHINE LEARNING; RANDOM FORESTS; TIME VARYING CONTROL SYSTEMS; TRAFFIC CONTROL; TRAVEL TIME;

ENSEMBLE LEARNING; GRADIENT BOOSTING; MACHINE LEARNING METHODS; NON-LINEAR RELATIONSHIPS; PREDICTOR VARIABLES; SHORT-TERM FORECASTING; TRAVEL TIME PREDICTION; TREE-BASED ENSEMBLES;

PREDICTIVE ANALYTICS;

ENSEMBLE FORECASTING; GRADIENT ANALYSIS; MACHINE LEARNING; MOTORWAY; PREDICTION; REGRESSION ANALYSIS; TRAVEL TIME;

MARYLAND; UNITED STATES;

EID: 84940461628     PISSN: 0968090X     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.trc.2015.02.019     Document Type: Article

References (50)

1. Ahmed M.M., Abdel-Aty M. Application of stochastic gradient boosting technique to enhance reliability of real-time risk assessment. Transp. Res. Rec.: J. Transp. Res. Board 2013, 2386:26-34.
2. Ali Haghani, M.H., Kaveh Farokhi Sadabadi, 2010. Estimation of Travel Times for Multiple TMC Segments. Available: . http://www.i95coalition.org/i95/Portals/0/Public_Files/uploaded/Vehicle-Probe/I-95%20CC-Estimation%20of%20Travel%20Times%20for%20Multiple%20TMC%20Segments%20-%20FINAL2.pdf.
3. Amit Y., Geman D. Shape quantization and recognition with randomized trees. Neural Comput. 1997, 9:1545-1588.
4. Antoniou C., Koutsopoulos H.N., Yannis G. Dynamic data-driven local traffic state estimation and prediction. Transp. Res. Part C: Emerg. Technol. 2013, 34:89-107.
5. Breiman L. Classification and Regression Trees 1984, Wadsworth International Group, Belmont, Calif.
6. Breiman L. Bagging predictors. Mach. Learn. 1996, 24:123-140.
7. Breiman L. Random forests. Mach. Learn. 2001, 45:5-32.
8. Chen H.K., Wu C.J. Travel time prediction using empirical mode decomposition and gray theory example of National Central University Bus in Taiwan. Transp. Res. Rec. 2012, 11-19.
9. Chen X., Li L., Li Z. Phase diagram analysis based on a temporal-spatial queueing model. Intell. Transp. Syst. IEEE Trans. 2012, 13:1705-1716.
10. Choe T., Skabardonis A., Varaiya P. Freeway performance measurement system: operational analysis tool. Transp. Res. Rec.: J. Transp. Res. Board 2002, 1811:67-75.
11. Chung Y.-S. Factor complexity of crash occurrence: an empirical demonstration using boosted regression trees. Accid. Anal. Prev. 2013, 61:107-118.
12. Elith J., Leathwick J.R., Hastie T. A working guide to boosted regression trees. J. Anim. Ecol. 2008, 77:802-813.
13. Farokhi Sadabadi, K., Hamedi, M., Haghani, A., 2010. Evaluating moving average techniques in short-term travel time prediction using an AVI data set. Transportation Research Board 89th Annual Meeting.
14. Fei X., Lu C.C., Liu K. A bayesian dynamic linear model approach for real-time short-term freeway travel time prediction. Transp. Res. Part C - Emerg. Technol. 2011, 19:1306-1318.
15. Friedman J.H. Greedy function approximation: a gradient boosting machine. Ann. Stat. 2001, 1189-1232.
16. Friedman J.H. Stochastic gradient boosting. Comput. Stat. Data Anal. 2002, 38:367-378.
17. Hamner, B., 2010. Predicting travel times with context-dependent random forests by modeling local and aggregate traffic flow. In: Data Mining Workshops (ICDMW), 2010 IEEE International Conference on IEEE, 1357-1359.
18. Hastie T., Tibshirani R., Friedman J., Hastie T., Friedman J., Tibshirani R. The Elements of Statistical Learning 2009, Springer.
19. Ho, T. K. Random decision forests. Document Analysis and Recognition, 1995, Proceedings of the Third International Conference on, 1995. IEEE, 278-282.
20. Ho T.K. The random subspace method for constructing decision forests. Pattern Anal. Mach. Intelligence, IEEE Trans. 1998, 20:832-844.
21. Hong W.-C. Traffic flow forecasting by seasonal SVR with chaotic simulated annealing algorithm. Neurocomputing 2011, 74:2096-2107.
22. Hyndman, R.J., Khandakar, Y., 2007. Automatic Time Series for Forecasting: the Forecast Package for R.
23. Kamarianakis Y., Prastacos P. Space-time modeling of traffic flow. Comput. Geosci. 2005, 31:119-133.
24. Karlaftis M.G., Vlahogianni E.I. Memory properties and fractional integration in transportation time-series. Transp. Res. Part C: Emerg. Technol. 2009, 17:444-453.
25. Kearns, M., 1988. Thoughts on Hypothesis Boosting. Unpublished manuscript, December.
26. Koren, Y., 2009. The Bellkor Solution to the Netflix Grand Prize. Netflix Prize Documentation.
27. Laboratory, T.C.F.A.T.T. Available: . http://www.cattlab.umd.edu/?portfolio=ritis.
28. Leshem G., Ritov Y.A. Traffic flow prediction using Adaboost algorithm with random forests as a weak learner. Int. J. Intelligent Technol. 2007, 2.
29. Li L., Chen X., Li Z., Zhang L. Freeway travel-time estimation based on temporal-spatial queueing model. Intelligent Transp. Syst. IEEE Trans. 2013, 14:1536-1541.
30. Li L., Li Y., Li Z. Efficient missing data imputing for traffic flow by considering temporal and spatial dependence. Transp. Res. Part C: Emerg. Technol. 2013, 34:108-120.
31. Li L., Chen X., Zhang L. Multimodel ensemble for freeway traffic state estimations. IEEE Trans. Intell. Transp. Syst. 2014, 15.
32. Min W., Wynter L. Real-time road traffic prediction with spatio-temporal correlations. Transp. Res. Part C: Emerg. Technol. 2011, 19:606-616.
33. Natekin A., Knoll A. Gradient boosting machines, a tutorial. Front. Neurorobotics 2013, 7.
34. Schapire R.E. The strength of weak learnability. Mach. Learn. 1990, 5:197-227.
35. Smith B., Demetsky M. Traffic flow forecasting: comparison of modeling approaches. J. Transp. Eng. 1997, 123:261-266.
36. Tsay R.S. Analysis of Financial Time Series 2010, Wiley, Cambridge, Mass.
37. van Hinsbergen C., van Lint J., van Zuylen H. Bayesian committee of neural networks to predict travel times with confidence intervals. Transp. Res. Part C: Emerg. Technol. 2009, 17:498-509.
38. van Lint J.W.C., van Hinsbergen C.P.I.J. Short-term traffic and travel time prediction models. Artif. Intell. Appl. Critical Transp. Issues 2012, 22.
39. van Lint J., Hoogendoorn S., van Zuylen H.J. Accurate freeway travel time prediction with state-space neural networks under missing data. Transp. Res. Part C: Emerg. Technol. 2005, 13:347-369.
40. Vlahogianni E.I., Karlaftis M.G., Golias J.C. Short-term traffic forecasting: where we are and where we're going. Transp. Res. Part C: Emerg. Technol. 2014.
41. Wang, Y., 2011. Prediction of weather impacted airport capacity using ensemble learning. In: Digital Avionics Systems Conference (DASC), 2011 IEEE/AIAA 30th, 2011. IEEE, 2D6-1-2D6-11.
42. Wang J., Shi Q. Short-term traffic speed forecasting hybrid model based on Chaos-Wavelet Analysis-Support Vector Machine theory. Transp. Res. Part C: Emerg. Technol. 2012.
43. Wei Y., Chen M.-C. Forecasting the short-term metro passenger flow with empirical mode decomposition and neural networks. Transp. Res. Part C: Emerg. Technol. 2012, 21:148-162.
44. Williams B., Durvasula P., Brown D. Urban freeway traffic flow prediction: application of seasonal autoregressive integrated moving average and exponential smoothing models. Transp. Res. Rec.: J. Transp. Res. Board 1998, 1644:132-141.
45. Yang M., Liu Y., You Z. The reliability of travel time forecasting. Intell. Transp. Syst. IEEE Trans. 2010, 11:162-171.
46. Yeon J., Elefteriadou L., Lawphongpanich S. Travel time estimation on a freeway using Discrete Time Markov Chains. Transp. Res. Part B: Methodol. 2008, 42:325-338.
47. Zhang X., Rice J.A. Short-term travel time prediction. Transp. Res. Part C: Emerg. Technol. 2003, 11:187-210.
48. Zhang Y., Sun R., Haghani A., Zeng X. Univariate volatility-based models for improving quality of travel time reliability forecasting. Transp. Res. Rec.: J. Transp. Res. Board 2013, 2365:73-81.
49. Zhang Y., Zhang Y., Haghani A. A hybrid short-term traffic flow forecasting method based on spectral analysis and statistical volatility model. Transp. Res. Part C: Emerg. Technol. 2013.
50. Zhou Z.-H. Ensemble Methods: Foundations and Algorithms 2012, Taylor & Francis, Boca Raton, FL.