Travel Time Estimation Using Freeway Point Detector Data Based on Evolving Fuzzy Neural Inference System

PLoS ONE

Volumn 11, Issue 2, 2016, Pages

  Tang, Jinjun ^a,b   Zou, Yajie ^c   Ash, John ^b   Zhang, Shen ^a   Liu, Fang ^d   Wang, Yinhai ^b  

^a HARBIN INSTITUTE OF TECHNOLOGY   (China)

^b UNIVERSITY OF WASHINGTON   (United States)

^c Tongji University   (China)

^d INNER MONGOLIA AGRICULTURAL UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

COMPARATIVE EFFECTIVENESS; INTERMETHOD COMPARISON; LEARNING; MODEL; MULTIPLE LINEAR REGRESSION ANALYSIS; NERVOUS SYSTEM; STATISTICAL MODEL; TIME PERCEPTION; TRAVEL; VELOCITY; ALGORITHM; ARTIFICIAL NEURAL NETWORK; CLUSTER ANALYSIS; COMPUTER SIMULATION; FUZZY LOGIC; REGRESSION ANALYSIS; TIME FACTOR; TRAFFIC AND TRANSPORT;

ALGORITHMS; CLUSTER ANALYSIS; COMPUTER SIMULATION; FUZZY LOGIC; NEURAL NETWORKS (COMPUTER); REGRESSION ANALYSIS; TIME FACTORS; TRANSPORTATION;

EID: 84959017687     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0147263     Document Type: Article

References (49)

1. Hellinga B., Izadpanah P., Takada H., Fu L., (2008). Decomposing travel times measured by probebased traffic monitoring systems to individual road links. Transportation Research Part C 16: 768-782.
2. Hofleitner A., Herring R., Abbeel P., Bayen A., (2012). Learning the dynamics of arterial traffic from probe data using a dynamic Bayesian network. IEEE Transactions on Intelligent Transportation Systems 13(4):1679-1693.
3. Zheng F., Van Zuylen H., (2012). Urban link travel time estimation based on sparse probe data. Transportation Research Part C 31: 145-157.
4. Jenelius E., Koutsopoulos H., (2013). Travel time estimation for urban road networks using low frequency probe vehicle data. Transportation Research Part B 53: 64-81.
5. Ma X., Yu H., Wang Y., Wang Y., (2015). Large-scale transportation network congestion evolution prediction using deep learning theory. PloS one 10(3): E0119044. doi: 10.1371/journal.pone.0119044 PMID: 25780910
6. Kim S., Lim K., Lee Y., (2007). A travel time fusion algorithm based on point and interval detector data. International Journal of Multimedia and Ubiquitous Engineering 2(4): 67-84.
7. Shin C., Kim S., (1998). Development of a low-cost AVI system and a travel time estimation algorithm for the Olympic expressway traffic management system in Seoul," In: Proceedings of the 5th World Congress on ITS, Seoul, October, paper No. 4056.
8. Rouphil N. M., Tarko A., Nelson P. C., Palacharla P., (1993). Travel time data fusion in ADVANCE-a preliminary design concept, ADVANCE Working Paper Series, No. 21, University of Illinois at Chicago.
9. Bachmann C., Abdulhai B., Roorda M. J., Moshiri B., (2013). A comparative assessment of multi-sensor data fusion techniques for freeway traffic speed estimation using microsimulation modeling. Transportation Research Part C 26: 33-48.
10. Wasson J.S., Sturdevant J.R., Bullock D.M., (2008). Real-time travel time estimates using media access control address matching. ITE Journal 78 (6): 20-23.
11. Haghani A., Hamedi M., Sadabadi K.F., Young S., Tarnoff P., (2010). Freeway travel time ground truth data collection using Bluetooth sensors. Transportation Research Record, Journal of Transportation Research Board 2160: 60-68.
12. Martchouk M., Mannering F., Bullock D., (2011). Analysis of freeway travel time variability using Bluetooth detection. Journal of Transportation Engineering 137 (10): 697-705.
13. Cheu R. L., Xie C., Lee D., (2002). Probe vehicles population and sample size for arterial speed estimation. Computer-Aided Civil Infrastructure Engineering 17(1):53-60.
14. Hong J., Zhang X., Wei Z., Li L., Ren Y., (2007), Spatial and temporal analysis of probe vehicle-based sampling for real-time traffic information system. In: Proceedings of IEEE Intelligent Vehicles Symposium, Istanbul, Turkey, June, pp: 1234-1239.
15. Srinivasan K.K., Jovanis P.P., (1996). Determination of number of probe vehicles required for reliable travel time measurement in urban network. Transportation Research Record: Journal of the Transportation Research Board 1537(1): 15-22.
16. Messelodi S., Modena M., Zanin M., De Natale F. G. B., Granelli F., Betterle E., et al., (2009). Intelligent extended floating car data collection. Expert systems with Applications 36(3): 4213-4227.
17. Brockfeld E., Wagner P., Lorkowski S., Mieth P., (2007). Benefits and limits of recent floating car data technology-an evaluation study. In: Proceedings of the 11th World Conference Transportation Research, Berkeley, CA, pp: 24-28.
18. Hegyi A., Girimonte D., Babuska R., (2006). A comparison of filter configurations for freeway traffic state estimation. In: Proceedings of Intelligent Transportation Systems Conference, 1029-1034.
19. Tampère C., Immers L., (2007). An extended Kalman filter application for traffic state estimation using CTM with implicit mode switching and dynamic parameters. In: Proceedings of Intelligent Transportation Systems Conference, 209-216.
20. Wang Y., Papageorgiou M., (2005). Real-time freeway traffic state estimation based on extended Kalman filter: A general approach. Transportation Research Part B: Methodological 39(2): 141-167.
21. Wang Y., Papageorgiou M., Messmer A., (2007). Real-time freeway traffic state estimation based on extended Kalman filter: A case study. Transportation Science 41(2): 167-181.
22. Wang Y., Papageorgiou M., Messmer A., (2008). Real-time freeway traffic state estimation based on extended Kalman filter: Adaptive capabilities and real data testing. Transportation Research Part A: Policy and Practice 42(10): 1340-1358.
23. Yuan Y., Van Lint J., Wilson R., (2012). Real-time lagrangian traffic state estimator for freeways. Intelligent Transportation Systems, IEEE Transactions on 13(1): 59-70.
24. Li R., Rose G., Sarvi M., (2006). Evaluation of speed-based travel time estimation models. Journal of Transportation Engineering 132(7): 540-547.
25. Van Lint J. W. C., Van Der Zijpp N. J., (2003). Improving a travel-time estimation algorithm by using dual loop detectors. Transportation Research Record: Journal of the Transportation Research Board 1855 (1): 41-48.
26. Cortes C. E., Lavanya R. Jun-Seok O., Jayakrishnan R., (2002). General-purpose methodology for estimating link travel time with multiple-point detection of traffic. Transportation Research Record: Journal of the Transportation Research Board 1802: 181-189.
27. Coifman B., (2001). Improved velocity estimation using single loop detectors. Transportation Research Part A 35: 863-880.
28. Kloot G., (1999). Melbourne's arterial travel time system. In: Proceedings of 6th World Congress on ITS, Toronto.
29. Bhaskar A Chung E, Dumont A. G., (2010). Analysis for the use of cumulative plots for travel time estimation on signalized network. International Journal of Intelligent Transportation Systems Research 8 (3): 151-163.
30. Bhaskar A, Chung E, Dumont A. G., (2009). Estimation of travel time on urban networks with midlink sources and sinks. Transportation Research Record: Journal of the Transportation Research Board 2121(1): 41-54.
31. Daganzo C. F., (1997). Fundamentals of Transportation and Traffic Operations. Pergamum, Oxford.
32. Newell G. F., (1982). Applications of queuing theory London New York-N.Y., Applications of queuing theory.
33. Zhang M., He J. C., (1998). Estimating arterial travel time using loop data, Public Policy Center, University of Iowa.
34. Kwon J., Coifman B., Bickel P., (2000). Day to day travel time trends and travel time prediction from loop detector data. Transportation Research Record: Journal of the Transportation Research Board 1717: 120-129.
35. Sisiopiku V., Rouphail N., Santiago A., (1994). Analysis of correlation between arterial travel time and detector data from simulation and field studies. Transportation Research Record: Journal of the Transportation Research Board 1457: 166-173.
36. Cherrett T., Bell H. A., McDonald M., (2001). Estimating vehicle speed using single inductive loop detectors. In: Proceedings of the Institution of Civil Engineers Transport 147:23-32.
37. Naranjo J. E., Jimenez F., Serradilla F. J., Zato J. G., (2012). Floating car data augmentation based on infrastructure sensors and neural networks. IEEE Transactions on Intelligent Transportation Systems 13(1): 107-114.
38. Liu H., Van Lint H., Van Zuylen H., (2007). Neural network based traffic flow model for urban arterial travel time prediction. In: Proceedings of the 86th Annual Meeting of the Transportation Research Board, Washington, DC. January: 21-25.
39. Cherrett T., McLeod F. N., Bell H., McDonald M., (2002). Journey time estimation using single inductive loop detectors on non-signalized links. Journal of the Operational Research Society 53: 610-619.
40. Wei C. H., Lin S. C., Li Y., (2003). Empirical validation of freeway bus travel time forecasting. Transportation Planning Journal 32: 651-679.
41. Nikola K., (1998). Evolving fuzzy neural networks-algorithms, applications and biological motivation. In Methodologies for the Conception, Design and Application of Soft Computing, Yamakawa T. and Matsumoto G., Eds, Singapore: World Scientific: 271-274.
42. Kasabov N., (2001). Evolving fuzzy neural networks for supervised/unsupervised online knowledgebased learning. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 31(6): 902-918.
43. Kasabov N., Song Q., (2002). DENFIS: Dynamic Evolving Neural-Fuzzy Inference System and Its Application for Time-Series Prediction. IEEE Transactions on Fuzzy Systems 10(2): 144-154.
44. Goodwin G. C., Sin K. S., (1984). Adaptive Filtering Prediction and Control. Upper Saddle River, NJ: Prentice-Hall.
45. Hsia T. C., (1977). System Identification: Least-Squares Methods. Boston, MA: D.C. Heath.
46. Dem?ar J., (2006). Statistical comparisons of classifiers over multiple data sets. The Journal of Machine Learning Research, 7: 1-30.
47. Gastaldi M., Gecchele G., Rossi R., (2014). Estimation of Annual Average Daily Traffic from one-week traffic counts, a combined ANN-Fuzzy approach. Transportation Research Part C: Emerging Technologies 47: 86-99.
48. Aydilek I. B., Arslan A., (2013). A hybrid method for imputation of missing values using optimized fuzzy c-means with support vector regression and a genetic algorithm. Information Sciences 233: 25-35.
49. Tang J., Zhang G., Wang Y., Wang H., Fang L., (2015). A hybrid approach to integrate fuzzy C-means based imputation method with genetic algorithm for missing traffic volume data estimation. Transportation Research Part C: EmErGiNg Technologies, 51: 29-40.