Development of recurrent neural network considering temporal-spatial input dynamics for freeway travel time modeling

Computer-Aided Civil and Infrastructure Engineering

Volumn 28, Issue 5, 2013, Pages 359-371

  Zeng, Xiaosi ^a   Zhang, Yunlong ^a  

^a Texas AandM University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ANN PREDICTION; ENSEMBLE TECHNIQUES; INPUT DELAYS; INPUT SELECTION; PREDICTION ACCURACY; STATE-SPACE MODELS; TOLL COLLECTION SYSTEMS; TRAVEL TIME PREDICTION;

RECURRENT NEURAL NETWORKS; TIME VARYING CONTROL SYSTEMS; TRAVEL TIME;

FORECASTING;

ACCURACY ASSESSMENT; ARTIFICIAL NEURAL NETWORK; ENSEMBLE FORECASTING; MODELING; MOTORWAY; PREDICTION; TRAVEL TIME; VELOCITY; VOLUME;

HOUSTON; TEXAS; UNITED STATES;

EID: 84876407045     PISSN: 10939687     EISSN: 14678667     Source Type: Journal    
DOI: 10.1111/mice.12000     Document Type: Article

References (46)

1. Abdulhai, B., Porwal, H. & Recker, W. W. (1998), Short-Term Freeway Traffic Flow Prediction Using Genetically Optimized Time-Delay-Based Neural Networks, Report UCI-ITS-WP-98-14, University of California, Irvine.
2. Adeli, H. (2001), Neural networks in civil engineering: 1989 - 2000, Computer-Aided Civil and Infrastructure Engineering, 16(2), 126-42.
3. Adeli, H. & Huang, S.-L. (1995), Machine Learning: Neural Networks, Genetic Algorithms and Fuzzy Systems, Wiley, New York.
4. Adeli, H. & Jiang, X. (2003), Neuro-fuzzy logic model for freeway work zone capacity estimation, Journal of Transportation Engineering, 129(5), 484-94.
5. Adeli, H. & Jiang, X. (2006), Dynamic fuzzy wavelet neural network model for structural system identification, Journal of Structural Engineering, 132(1), 102-11.
6. Bhaskar, A., Chung, E. & Dumont, A.-G. (2011), Fusing loop detector and probe vehicle data to estimate travel time statistics on signalized urban networks, Computer-Aided Civil and Infrastructure Engineering, 26(6), 433-50.
7. Chih-Chieh Huang, C.-H. L. (2001), Nonlinear identification of dynamic systems using neural networks. Computer-Aided Civil and Infrastructure Engineering, 16(1), 28-41.
8. D'Angelo, M. P., Al-Deek, H. M. & Wang, M. C. (1999), Travel-time prediction for freeway corridors. Transportation Research Record No. 1676, Transportation Research Board of the National Academies, Washington DC, pp. 184-91.
9. Dharia, A. & Adeli, H. (2003), Neural network model for rapid forecasting of freeway link travel time, Engineering Applications of Artificial Intelligence, 16(7-8), 607-13.
10. Dougherty, M. (1995), A review of neural networks applied to transport, Transportation Research Part C: Emerging Technologies, 3(4), 247-60.
11. Dougherty, M. S., Kirby, H. R. & Boyle, R. D. (1993), The use of neural networks to recognize and predict traffic congestion, Traffic Engineering and Control, 34(6), 311-14.
12. Ghosh-Dastidar, S. & Adeli, H. (2009), Spiking neural networks, International Journal of Neural Systems, 19(4), 295-308.
13. Haykin, S. (1998), Neural Networks: A Comprehensive Foundation, Prentice-Hall, Inc., New Jersey.
14. Hua, J. & Faghri, A. (1994), Applications of artificial neural networks to intelligent vehicle-highway systems. Transportation Research Record No. 1453, Transportation Research Board of the National Academies, Washington DC, pp. 83-90.
15. Huisken, G. (2006), Inter-urban short-term traffic congestion prediction. Ph.D. dissertation, University of Twente, Oldenzaal, the Netherlands.
16. Ishak, S., Kotha, P. & Alecsandru, C. (2003), Optimization of dynamic neural network performance for short-term traffic prediction. Transportation Research Record No. 1836, Transportation Research Board of the National Academies, Washington DC, pp. 45-56.
17. Jiang, X. & Adeli, H. (2005), Dynamic wavelet neural network model for traffic flow forecasting, Journal of Transportation Engineering, 131(10), 771-80.
18. Karlaftis, M. G. & Vlahogianni, E. I. (2011), Statistical methods versus neural networks in transportation research: differences, similarities and some insights, Transportation Research Part C: Emerging Technologies, 19(3), 387-99.
19. Liu, H., Zuylen, H. v., Lint, H. v. & Salomons, M. (2006), Predicting urban arterial travel time with state-space neural networks and Kalman filters. Transportation Research Record No. 1968, Transportation Research Board of the National Academies, Washington DC, pp. 99-108.
20. Mackay, D. J. C. (1995), Probable networks and plausible predictions: a review of practical bayesian methods for supervised neural networks, Network Computation in Neural System 6(3), 469-505.
21. Margiotta, R., Lomax, T., Hallenbeck, M., Turner, S., Skabardonis, A., Ferrell, C. & Eisele, B. (2006), Guide to Effective Freeway Performance Measurement: Final Report and Guidebook. Report NCHRP Web-Only Document 97, National Cooperative Highway Research Program, TRB, Washington DC. Available at: http://onlinepubs.trb.org/onlinepubs/nchrp/nchrp_w97.pdf, accessed January 10, 2010.
22. Mark, C. D. & Sadek, A. W. (2004), Learning systems for predicting experiential travel times in the presence of incidents: insights and lessons learned. Transportation Research Record No. 1879, Transportation Research Board of the National Academies, Washington DC, pp. 51-58.
23. Markovic, H., Basic, B. D., Gold, H., Dong, F. & Hirota, K. (2010), Data-based non-parametric regression for predicting travel times in urban traffic networks, Promet Traffic-Traffico, 22(1), 1-13.
24. Mazloumi, E., Currie, G., Rose, G. & Sarvi, M (2010). Statistical confidence estimation measures for artificial neural networks: application in bus travel time prediction, in TRB 89th Annual Meeting Compendium of Papers (DVD).
25. Medsker, L. R. & Jain, L. C. (2000), Recurrent Neural Networks: Design and Applications, CRC Press LLC, Boca Raton, Florida.
26. Min, W. & Wynter, L. (2011), Real-time road traffic prediction with spatio-temporal correlations, Transportation Research Part C: Emerging Technologies, 19(4), 606-16.
27. Mohemmed, A., Schliebs, S., Matsuda, S. & Kasabov, N. (2012), Span: spike pattern association neuron for learning spatio-temporal spike patterns, International Journal of Neural Systems, 22(4), DOI: 10.1142/S0129065712500128.
28. Papageorgiou, M., Blosseville, J.-M. & Hadj-Salem, H. (1989), Macroscopic modelling of traffic flow on the boulevard périphérique in Paris, Transportation Research Part B: Methodological, 23(1), 29-47.
29. Park, D. & Rilett, L. R. (1998), Forecasting multiple-period freeway link travel times using modular neural networks. Transportation Research Record No. 1617, Transportation Research Board of the National Academies, Washington DC, pp. 163-70.
30. Park, D. & Rilett, L. R. (1999), Forecasting freeway link travel times with a multilayer feedforward neural network, Journal of Computer-Aided Civil and Infrastructure Engineering, 14(5), 357-67.
31. Park, D., Rilett, L. R., Gajewski, B. J., Spiegelman, C. H. & Choi, C. (2009), Identifying optimal data aggregation interval sizes for link and corridor travel time estimation and forecasting, Transportation, 36(1), 77-95.
32. Park, D., Rilett, L. R. & Han, G. (1999), Spectral basis neural networks for real-time travel time forecasting, Journal of Transportation Engineering, 125(6), 515-23.
33. Ruiz Juri, N., Unnikrishnan, A. & Waller, S. T. (2007), Integrated traffic simulation-statistical analysis framework for online prediction of freeway travel time. Transportation Research Record No. 2039, Transportation Research Board of the National Academies, Washington DC, pp. 24-31.
34. Samant, A. & Adeli, H. (2001), Enhancing neural network traffic incident-detection algorithms using wavelet, Computer-Aided Civil and Infrastructure Engineering, 16(4), 239-45.
35. Songchitruksa, P., Balke, K., Zeng, X., Chu, C. L. & Zhang, Y. (2009), A Guidebook for Effective Use of Incident Data at Texas Transportation Management Centers. Report FHWA/TX-09/0-5485-P2, Texas Transportation Institute, College Station, Texas.
36. Stathopoulos, A., Dimitriou, L. & Tsekeris, T. (2008), Fuzzy modeling approach for combined forecasting of urban traffic flow, Computer-Aided Civil and Infrastructure Engineering, 23(7), 521-35.
37. Stathopoulos, A. & Karlaftis, M. G. (2003), A multivariate state space approach for urban traffic flow modeling and prediction, Transportation Research Part C: Emerging Technologies, 11(2), 121-35.
38. Vanajakshi, L. D., Williams, B. M. & Rilett, L. R. (2009), Improved flow-based travel time estimation method from point detector data for freeways, Journal of Transportation Engineering, 135(1), 26-36.
39. van Hinsbergen, D. J. J., van Lint, J. W. C. & van Zuylen, H. J. (2009), Bayesian training and committees of state space neural networks for online travel time prediction. Transportation Research Record No. 2105, Transportation Research Board of the National Academies, Washington DC, pp. 118-26.
40. Van Lint, J. W. C. (2006), Reliable real-time framework for short-term freeway travel time prediction, Journal of Transportation Engineering, 132(12), 921-32.
41. Van Lint, J. W. C., Hoogendoorn, S. P. & Van Zuylen, H. J. (2002), Freeway travel time prediction with state-space neural networks: modeling state-space dynamics with recurrent neural networks. Transportation Research Record No. 1811, Transportation Research Board of the National Academies, Washington DC, pp. 30-9.
42. Van Lint, J. W. C., Hoogendoorn, S. P. & Van Zuylen, H. J. (2005), Accurate freeway travel time prediction with state-space neural networks under missing data, Transportation Research Part C: Emerging Technologies, 13, 347-69.
43. Vlahogianni, E. I., Golias, J. C. & Karlaftis, M. G. (2004), Short-term traffic forecasting: overview of objectives and methods, Transport Reviews, 24(5), 533-57.
44. Wei, C.-H. & Lee, Y. (2007), Development of freeway travel time forecasting models by integrating different sources of traffic data, IEEE Transactions on Vehicular Technology, 56(6), 3682-94.
45. Williams, B. M. (2001), Multivariate vehicular traffic flow prediction: evaluation of ARIMAX modeling. Transportation Research Record No. 1776, Transportation Research Board of the National Academies, Washington DC, pp. 194-200.
46. Zhang, X. & Rice, J. A. (2003), Short-term travel time prediction, Transportation Research Part C: Emerging Technologies, 11(3), 187-210.