Short‐term traffic forecasting: Overview of objectives and methods

Transport Reviews

Volumn 24, Issue 5, 2004, Pages 533-557

  Vlahogianni, Eleni I ^a   Golias, John C ^a   Karlaftis, Matthew G ^a  

^a NATIONAL TECHNICAL UNIVERSITY OF ATHENS   (Greece)

Author keywords

[No Author keywords available]

Indexed keywords

FORECASTING METHOD; INTELLIGENT TRANSPORTATION SYSTEM; METHODOLOGY; REAL TIME; TRAFFIC MANAGEMENT;

EID: 4444369422     PISSN: 01441647     EISSN: 14645327     Source Type: Journal    
DOI: 10.1080/0144164042000195072     Document Type: Article

References (88)

1. Abdenour, A. and Al-Ghamdi, A. S. (2002) Artificial neural networks application to the estimation of vehicle headways in freeway sections, in: Proceedings of the 81st Transportation Research Board Annual Meeting, Washington, DC.
2. Abdulhai, B., Porwal, H. and Recker, W. (1999) Short-term Freeway Traffic Flow Prediction Using Genetically-optimized Time-delay-based Neural Networks. UCB, UCB-ITS-PWP-99-1 (Berkeley, CA: Institute of Transportation Studies, University of California, Berkeley).
3. Ahmed, M. S. and Cook, A. R. (1979) Analysis of freeway traffic time-series data by using Box-Jenkins techniques, Transportation Research Board, 722, pp. 1-9.
4. Chang, G.-L. and Su, C.-C. (1995) Predicting intersection queue with neural network models, Transportation Research Part C, 3(3), pp. 175-191.
5. Chatfield, C. (1992) A commentary on error measurements, International Journal of Forecasting, 8, pp. 100-102.
6. Chen, H. and Grant-Muller, S. (2001) Use of sequential learning for short-term traffic flow forecasting. Transportation Research Part C, 9, pp. 319-336.
7. Chen, H., Grant-Muller, S., Mussone, L. and Montgomery, F. (2001) A study of hybrid neural network approaches and the effects of missing data on traffic forecasting, Neural Computing and Applications, 10, pp. 277-286.
8. Chen, M. and Chien, S. I. J. (2001) Dynamic freeway travel time prediction using probe vehicle data: link-based vs. path-based, in: Proceedings of the Transportation Research Board 80th Annual Meeting, Washington, DC.
9. Cheslow, M., Hatcher, S. G. and Patel, V. M. (1992) An Initial Evaluation of Alternative Intelligent Vehicle Highway Systems Architectures. Report 92W0000063 (McLean, VA: MITRE).
10. Chien, S. I. J. and Kuchipudi, C. M. (2002) Dynamic travel time prediction with real-time and historical data, in: Proceedings of the Transportation Research Board 81st Annual Meeting, Washington, DC.
11. Chung, E. and Rosalion, N. (2001) Short term traffic flow prediction, in: Proceedings of the 24th Australian Transportation Research Forum, Hobart, Tasmania, April.
12. Clark, S. (2003) Traffic prediction using multivariate nonparametric regression. Journal of Transportation Engineering, 129(2), pp. 161-168.
13. Clark, S. D., Dougherty, M. S. and Kirby, H. R. (1993) The use of neural networks and time series models for short-term traffic forecasting: a comparative study, in: Proceedings of the PTRC 21st Summer Annual Meeting.
14. Clemens, M. P. and Hendry, D. F. (1993) On the limitations of the comparison of mean square error forecasts, Journal of Forecasting, 12, pp. 615-637.
15. Dailey, D. J. (1997) Travel time estimates using a series of single loop volume and occupancy measurements, in: Proceedings of the Transportation Research Board 76th annual meeting, Washington, DC.
16. Danech-Pajouh, M. and Aron, M. (1991) ATHENA: a method for short-term inter-urban motorway traffic forecasting, Recherche Transport Securite, 6, pp. 11-16.
17. Davis, G. A., Niham, N. L., Hamed, M. M. and Jacobson, L. N. (1991) Adaptive forecasting of freeway traffic congestion. Transportation Research Record, 1287, pp. 29-33.
18. Dia, H. (2001) An object-oriented neutral network approach to short-term traffic forecasting. European Journal of Operational Research, 131, pp. 253-261.
19. Dougherty, M. (1995) A review of neural networks applied to transport, Transportation Research Part C, 3(4), pp. 247-260.
20. Dougherty, M. S. and Cobbet, M. R. (1997) Short-term inter-urban traffic forecasts using neural networks, International Journal of Forecasting, 13, pp. 21-31.
21. Florio, L. and Mussone, L. (1996) Neural network models for classification and forecasting of freeway traffic flow stability, Control Engineering Practice, 4(2), pp. 153-164.
22. Gartner, N. H. (1982) Prescription for demand responsive urban traffic control, Transportation Research, 881, pp. 73-76.
23. Gartner, N. H., Stamatiadis, C. and Tarnoff, P. J. (1995) Development of advanced traffic signal control strategies for intelligent transportation systems: multilevel design, Transportation Research Record, 1494, pp. 98-105.
24. Gilmore, J. E. and Abe, N. (1995) Neural network models for traffic control and congestion prediction. IVHS Journal, 2(3), pp. 231-252.
25. Hamed, M. M., Al-Masaeid, H. R. and Bani Said, Z. M. (1995) Short-term prediction of traffic volume in urban arterials, ASCE Journal of Transportation Engineering, 121(3), pp. 249-254.
26. Haykin, S. (1994) NN: A Comprehensive Foundation (New York, NY: Macmillan).
27. Head, L. K. (1995) Event-based short-term traffic flow prediction model, Transportation Research Board, 1510, pp. 45-52.
28. Highway Capacity Manual (2000) (Washington, DC: Transportation Research Board, National Research Council).
29. Huang, P. X. and Xu, G. (1996) A neural network approach for predicting network traffic conditions, in: Proceedings of the 1996 Annual Meeting of ITS America, pp. 507-516.
30. Huisken, G. and Van Berkum, E. C. (2003) A comparative analysis of short-range travel time prediction methods, in: Proceedings of the Transportation Research Board 82nd Annual Meeting, Washington, DC.
31. Innamaa, S. (2000) Short-term prediction of traffic situation using MLP-neural networks, in: Proceedings of the 7th World Congress on Intelligent Transportation Systems, Turin, Italy.
32. Ishak, S. and Al-Deek, H. (2002) Performance of short-term time series traffic prediction model, Journal of Transportation Engineering, 128(6), pp. 490-498.
33. Ishak, S. and Al-Deek, H. (2003) Statistical evaluation of I-4 traffic prediction system, in: Proceedings of the Transportation Research Board 82nd Annual Meeting, Washington, DC.
34. Ishak, S. and Alecsandru, C. (2003) Optimizing traffic prediction performance of Neural networks under various topological, input and traffic condition settings, in: Proceedings of the Transportation Research Board 82nd Annual Meeting, Washington, DC.
35. Ishak, S., Kotha, P. and Alecsandru, C. (2003) Optimization of dynamic neural networks performance for short-term traffic forecasting, in: Proceedings of the Transportation Research Board 82nd Annual Meeting, Washington, DC.
36. Kamarianakis, Y. and Prastakos, P. (2003) Forecasting traffic flow conditions in an urban network: comparison of multivariate and univariate approaches, in: Proceedings of the Transportation Research Board 82nd Annual Meeting, Washington, DC.
37. Kaysi, I., Ben-Akiva, M. and Koutsopoulos, H. (1993) An integrated approach to vehicle routing and congestion prediction for real-time driver guidance, in: Transportation Research Record, 1408, pp. 66-74.
38. Kirby, H., Dougherty, M. and Watson, S. (1997) Should we use neural networks or statistical models for short term motorway forecasting, International Journal of Forecasting, 13, pp. 45-50.
39. Kwon, E., Coifman, B. and Bickel, P. (2002) Day-to-day travel time trends and travel time prediction from loop detector data, in: Proceedings of the Transportation Research Board 81st Annual Meeting, Washington, DC.
40. Kwon, E. and Stephanedes, Y. J. (1994) Comparative evaluation of adaptive and neural-network exit demand prediction for freeway control, Transportation Research Record, 1446, pp. 66-76.
41. Lan, C.-J. and Miaou, S.-P. (1999) Real-time prediction of traffic flows using dynamic generalized linear models, Transportation Research Record, 1678, pp. 168-178.
42. Ledoux, C. (1997) An urban traffic flow model integrating neural networks, Transportation Research Part C, 5(5), pp. 287-300.
43. Lee, S. and Fambro, D. B. (1999) Application of subset autoregressive moving average model for shortterm freeway traffic volume forecasting, Transportation Research Record, 1678, pp. 179-188.
44. Levin, M. and Tsao, Y.-D. (1980) On forecasting freeway occupancies and volumes, Transportation Research Record, 773, pp. 47-49.
45. Levinson, H. S. and Lomax, T. J. (1996) Developing a travel time congestion index, Transportation Research Record, 1564, pp. 1-10.
46. Lieu, H. C. (2000) Traffic estimation and prediction system. TR News, 208, pp. 3-6.
47. Lin, W.-H., Lu, Q. and Dahlgren, J. (2002) A dynamic procedure for short-term prediction of traffic congestion, in: Proceedings of the 81st Transportation Research Record Annual Meeting, Washington, DC.
48. Lingras, P. and Mountford, P. (2001) Time delay neural networks designed using genetic algorithms for short-term inter-city traffic forecasting, IEA/AIE 2001, LNAI 2070, pp. 290-299.
49. Lu, J. (1990) Prediction of traffic flow by an adaptive prediction system, TRB, 1287, pp. 54-61.
50. Lyons, G. D., McDonald, M., Hounsell, N. B., Williams, B., Cheese, J. and Radia, B. (1996) Urban traffic management: the viability of short-term congestion forecasting using artificial neural networks, in: Proceedings of the 24th European Transport Forum, PTRC.
51. Makridakis, S., Wheelright, S. C. and McGee, V. E. (1989) Forecasting: Methods and Applications, 3rd edn (Chichester: Wiley).
52. Matsoukis, E. C. and Roidakis, A. (1998) Short-term prediction of traffic volume in urban arterials and its use in dynamic traffic signal control, in: Proceedings of the 8th World Conference on Transport Research, paper 290, Area C3, Antwerp, Belgium, July.
53. Okutani, I. and Stephanedes, Y. J. (1984) Dynamic prediction of traffic volume through Kalman filtering theory, Transportation Research, Part B, 18(1), pp. 1-11.
54. Park, D. and Rilett, L. R. (1998) Forecasting multiple-period freeway link travel times using modular neural networks, Transportation Research Record, 1617, pp. 163-170.
55. Park, D., Messer, C. J. and Urbanik, T. (1998) Short-term freeway traffic volume forecasting using radial basis function neural networks, Transportation Research Record, 1651, pp. 39-47.
56. Park, D., Rilett, L. R. and Han, G. (1999) Spectral basis neural networks for real-time travel time forecasting, Journal of Transportation Engineering, 125(6), pp. 515-523.
57. Sheu, J.-B., 1999, A stochastic modeling approach to dynamic prediction of section-wide inter-lane and intra-lane traffic variables using point detector data, Transportation Research Part A, 33(2), pp. 79-100.
58. Smith, B. L. and Demetsky, M. J. (1994) Short-term traffic flow prediction: neural network approach, Transportation Research Board, 1453, pp. 98-104.
59. Smith, B. L. and Demetsky, M. J. (1996) Multiple-interval freeway traffic flow forecasting, Transportation Research Record, 1554, pp. 136-141.
60. Smith, B. L. and Demetsky, M. J. (1997) Traffic flow forecasting: comparison of modelling approaches, Journal of Transportation Engineering, 123(4), pp. 261-266.
61. Smith, B. L., Williams, B. M. and Oswald, K. R. (2000) Parametric and nonparametric traffic volume forecasting, in: Proceedings of the 80th TRB Annual Meeting, Washington, DC.
62. Smith, B. L., Williams, B. M. and Oswald, K. R. (2002) Comparison of parametric and non-parametric models for traffic flow forecasting, Transportation Research Part C: Emerging Technologies, 10(4), pp. 303-321.
63. Stathopoulos, A. and Karlaftis, M. G. (2003) A multivariate state-space approach for urban traffic flow modelling and prediction, Transportation Research Part C, 11(2), pp. 121-135.
64. Stephanedes, Y. J., Kwon, E. and Michalopoulos, P. (1990) On-line diversion prediction for dynamic control and vehicle guidance in freeway corridors, Transportation Research Board, 1287, pp. 11-19.
65. Sun, H., Liu, H. X., Xiao, H., He, R. R. and Ran, B. (2003) Short-term traffic forecasting using the local linear regression model, in: Proceedings of the Transportation Research Board 82nd Annual Meeting, Washington, DC.
66. Tebaldi, C., West, M. and Karr, A. F. (2002) Statistical analysis of freeway traffic flows, Journal of Forecasting, 21, pp. 39-68.
67. Travel Time Data Collection Handbook (1998) FHWA-PL-98-035, March (College Station, TX: Texas Transportation Institute, The Texas A&M University System).
68. Ulbricht, C. (1994) Multi-recurrent networks for traffic forecasting, Proceedings of the Twelfth National Conference on Artificial Intelligence, AAAI '94, Vol 11, pp. 883-888 (Cambridge, MA: AAAI Press/MIT Press).
69. Van Arem, B., Van der Vlist, M. J. M., Muste, M. and Smulders, S. A. (1997) Travel time estimation in the Gerdien project, International Journal of Forecasting, 13, pp. 73-85.
70. Van der Voort, M., Dougherty, M. and Watson, S. (1996) Combining Kohonen maps with ARIMA time-series models to forecast traffic flow, Transportation Research, Part C, 4(5), pp. 307-318.
71. Van Lint, J. W. C., Hoogendoorn, S. P. and Van Zuylen, H. J. (2002) Freeway travel time prediction with state-space neural networks: modelling state-space dynamics with recurrent neural networks, in: Proceedings of the Transportation Research Board 81st Annual Meeting, Washington, DC.
72. Van Lint, J. W. C., Hoogendoorn, S. P. and Van Zuylen, H. J. (2003) Towards a robust framework for freeway travel time prediction: experiments with simple imputation and state-space neural networks, in: Proceedings of the Transportation Research Board 82nd Annual Meeting, Washington, DC.
73. Van Vuren, T. and Leonard, D. R. (1994) Urban traffic congestion caused by incidents, Traffic Engineering and Control, July/August, pp. 422-429.
74. Vlahogianni, E. I., Karlaftis, M. G. and Golias, J. C. (2003) A multivariate neural network predictor for short term traffic forecasting in urban signalized arterial, in: Proceedings of the 10th IFAC Symposium on Control in Transportation Systems, 4-6 August 2003, Tokyo, Japan.
75. Vythoulkas, P. C. (1993) Alternative approaches to short-term traffic forecasting for use in driver information systems, in: Transportation and Traffic Theory, Proceedings of the 12th International Symposium on Traffic Flow Theory and Transportation, Berkley, CA, 21-23 July, a Symposium in Honor of Gordon F. Newell.
76. Wang, Y. and Nihan, N. L. (2000) Freeway traffic speed estimation with single-loop outputs, Transportation Research Record, 1727, pp. 120-126.
77. Washington, S. P., Karlaftis, M. G. and Mannering, F. L. (2003) Statistical and Econometric Methods for Transportation Data Analysis (London: Chapman & Hall/CRC Press).
78. Whitlock, M. E. and Queen, C. M. (2000) Modelling a traffic network with missing data, Journal of Forecasting, 19, pp. 561-574.
79. Whittaker, J., Garside, S. and Lindeveld, K. (1997) Tracking and predicting network traffic process, International Journal of Forecasting, 13, pp. 51-61.
80. Wild, D. (1997) Short-term forecasting based on a transformation and classification of traffic volume time series, International Journal of Forecasting, 13, pp. 63-72.
81. Williams, B. M. (2001) Multivariate vehicular traffic flow prediction: an evaluation of ARIMAX modeling, Transportation Research Record, 1776, pp. 194-200.
82. Williams, B. M., Durvasula, P. K. and Brown, D. E. (1998) Urban traffic flow prediction: application of seasonal autoregressive integrated moving average and exponential smoothing models, Transportation Research Record, 1644, pp. 132-144.
83. Yasdi, R. (1999) Prediction of road traffic using a neural network approach, Neural Computation and Application, 8, pp. 135-142.
84. Yin, H., Wong, S. C. and Xu, J. (2002) Urban traffic flow prediction using fuzzy-neural approach, Transportation Research Part C, 10, pp. 85-98.
85. Yun, S.-Y., Namkoong, S., Rho, J.-H., Shin, S.-W. and Choi, J.-U. (1998) A performance evaluation of neural network models in traffic volume forecasting, Mathematical Computer Modelling, 27(9-11), pp. 293-310.
86. Zhang, G., Patuwo, B. E. and Hu, M. Y. (1998) Forecasting with artificial neural networks: the state of art, International Journal of Forecasting, 14, pp. 35-62.
87. Zhang, H. M. (2000) Recursive prediction of traffic conditions with neural networks, Journal of Transportation Engineering, 126(6), pp. 472-481.
88. Zhang, X. and Rice, J. A. (2001) Short-term travel time prediction using a time-varying coefficient linear model, Transportation Research part C, 11(3-4), pp. 187-210.