Prediction intervals to account for uncertainties in neural network predictions: Methodology and application in bus travel time prediction

Engineering Applications of Artificial Intelligence

Volumn 24, Issue 3, 2011, Pages 534-542

  Mazloumi, Ehsan ^a   Rose, Geoff ^a   Currie, Graham ^a   Moridpour, Sara ^b  

^a MONASH UNIVERSITY   (Australia)

^b RMIT UNIVERSITY   (Australia)

Author keywords

Bus travel time; Neural network; Prediction intervals; Prediction uncertainty

Indexed keywords

BUS ROUTE; CONFIDENCE INTERVAL; IN-FIELD; MELBOURNE , AUSTRALIA; NETWORK PREDICTION; PREDICTION INTERVAL; PREDICTION UNCERTAINTY; TRAINING DATA; TRANSPORTATION ENGINEERING; TRAVEL TIME; TRAVEL TIME PREDICTION; TWO SOURCES;

BUS TRANSPORTATION; BUSES; CIVIL ENGINEERING; FINANCIAL DATA PROCESSING; FORECASTING; REACTOR CORES;

NEURAL NETWORKS;

EID: 79951949165     PISSN: 09521976     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.engappai.2010.11.004     Document Type: Article

References (46)

1. P.J. Angeline, G.M. Saunders, and J.B. Pollack An evolutionary algorithm that constructs recurrent neural networks IEEE Transactions on Neural Networks 5 1994 54 65
2. R. Bellman Adaptive Control Processes: A Guided Tour 1961 Princeton University Press
3. C.M. Bishop Neural networks for pattern recognition 1995 Clarendon Press Oxford
4. M. Cai, Y. Yin, and M. Xie Prediction of hourly air pollutant concentrations near urban arterials using artificial neural network approach Transportation Research Part D 14 1 2009 32 41
5. M. Chen, J. Yaw, S.I. Chien, and X. Liu Using automatic passenger counter data in bus arrival time prediction Journal of Advanced Transportation 41 3 2007 267 283 (Pubitemid 47437158)
6. S. Chien, H. Hwang, and T. Pei Using neural networks to synthesize origin-destination flows in a traffic circle Transportation Research Record 1457 1994 134 142
7. A. Dharia, and H. Adeli Neural network model for rapid forecasting of freeway link travel time Engineering Applications of Artificial Intelligence 16 78 2003 607 613
8. H. Dia, and S. Panwai 'Modelling drivers' compliance and route choice behavior in response to travel information Nonlinear Dynamics 49 2007 493 509 (Pubitemid 47179560)
9. H. Dia, and G. Rose Development and evaluation of neural network freeway incident detection models using field data Transportation Research Part C 5 1998 313 331
10. R. Dybowski, and S.J. Roberts Confidence intervals and prediction intervals for feed-forward neural networks R. Dybowski, V. Gant, Clinical Applications of Artificial Neural Networks' 2001 Cambridge University Press 298 327
11. B. Efron The jacknife, the bootstrap, and other resampling plans Society for Industrial and Applied Mathematics 1982 SIAM Philadelphia, PA, USA
12. L. Fu, and L.R. Rilett Expected shortest paths in dynamic and stochastic traffic networks Transportation Research Part B 32 7 1998 499 516 (Pubitemid 128407016)
13. N. Garcia-Pedrajas, C. Hervas-Martinez, and J. Munoz-Perez COVNET: a cooperative coevolutionary model for evolving artificial neural networks IEEE Transactions on Neural Networks 14 3 2003 575 596
14. M.T. Hagan, and M. Menhaj Training feed-forward networks with the Marquardt algorithm IEEE Transactions on Neural Networks 5 6 1999 989 993
15. T. Heskes Practical confidence and prediction interval Advances in Neural Information Processing Systems 9 1997 176 182
16. Hinton, G.; 1987. Learning translation invariant recognition in a massively parallel networks, in: 'PARLE Parallel Architectures and Languages Europe', Edited by, pp. 113.
17. B.A. Jain, and B.N. Nag A performance evaluation of neural network decision models Journal of Management Information Systems 14 1997 201 216
18. Jeong, R.;Rilett, R.L.; 2004.; Bus arrival time prediction using artificial neural network model. In: Proceedings of the IEEE Intelligent Transportation System Conference, Washington, D.C.; USA.
19. I.T. Jolliffe Principal Component Analysis 2002 Springer New York
20. R. Kalaputapu, and M.J. Demetsky Modelling schedule deviations of buses using automatic vehicle location data and artificial neural networks Transportation Research Record 1497 1995 44 52
21. Khosravi, A.; Martinez, T.; Melendez, J.; Colomer, J.; Sanchez, J.; 2007. Integrating a feature selection algorithm for classification of voltage sags originated in transmission and distribution networks. In: Proceeding of the 2007 conference on Artificial Intelligence Research and Development, IOS Press.
22. A. Khosravi, S. Nahavandi, and D. Creighton A prediction interval-based approach to determine optimal structures of neural network metamodels Expert Systems with Applications 37 3 2010 2377 2387
23. A. Khosravi, S. Nahavandi, and D. Creighton 'Construction of optimal prediction intervals for load forecasting problems IEEE Transactions on Power Systems 25 3 2010 1496 1503
24. W. Kim, and L. Rilett Improved transit signal priority system for networks with nearside bus stops Transportation Research Record: Journal of the Transportation Research Board 1925 2005 205 214
25. S.S. Lee, S.H. Lee, Y.T. Oh, and K.C. Choi Development of degree of saturation estimation models for adaptive signal systems KSCE Journal of Civil Engineering 6 3 2002 337 345
26. Li, R.; 2006. Enhancing motorway travel time prediction models through explicit incorporation of travel time variability, Ph.D. thesis, Monash University, Melbourne, Australia.
27. Liu, H.; 2008. Travel time prediction for urban networks, Ph.D. thesis, Delft University of Technology, the Netherlands.
28. Liu, H.; Van Zuylen, H.J.; Van Lint, H.; Chen, Y.; Zhang, K.; 2005. Prediction of urban travel times with intersection delays. In: Proceedings of the Eighth International IEEE Conference on Intelligent Transportation Systems, Vienna, Austria.
29. E. Mazloumi, G. Currie, and G. Rose Using GPS data to gain insight into public transport travel time variability Journal of Transportation Engineering 136 2010 623 631 7 2010 623 631
30. Mazloumi, E.; Currie, G.; Rose, G.; Sarvi, M.; 2009. Using SCATS data to predict bus travel time. In: Proceedings of the 32nd Australian Transport Research Forum (ATRF), Auckland, New Zealand.
31. S. Mukkamala, and A.H. Sung Feature selection for intrusion detection using neural networks and support vector machines Transportation Research Record 1823 2003 33 39
32. Nix, A.D.; Weigend, A.S.; 1994. Estimating the mean and variance of the target probability distribution. In: Proceedings of the IEEE International Conference on Neural Networks.
33. G. Papadopoulos, P.J. Edwards, and A.F. Murray Confidence estimation methods for neural networks: a practical comparison IEEE Transactions on Neural Networks 12 6 2001 1278 1287 (Pubitemid 33106033)
34. Park, T.; Lee, S.; 2004. A Bayesian approach for estimating link travel time on urban arterial road network. In: Computational Science and Its Applications ICCSA 2004, Edited by, pp. 10171025.
35. P. Pattanamekar, D. Park, L.R. Rilett, J. Lee, and C. Lee Dynamic and stochastic shortest path in transportation networks with two components of travel time uncertainty Transportation Research Part C: Emerging Technologies 11 5 2003 331 354 (Pubitemid 37216401)
36. H. Peng, F. Long, and C. Ding Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy IEEE Transactions on Pattern Analysis and Machine Intelligence 27 8 2005 1226 1238 (Pubitemid 41245053)
37. M. Qi, and G.P. Zhang An investigation of model selection criteria for neural network time series forecasting European Journal of Operational Research 132 3 2001 666 680 (Pubitemid 32487106)
38. S.M. Shiva Nagendra, and M. Khare Artificial neural network based line source models for vehicular exhaust emission predictions of an urban roadway Transportation Research Part D 9 3 2004 199 208 (Pubitemid 38462291)
39. B.L. Smith, and M.J. Demetsky Short term traffic flow prediction: neural network approach Transportation Research Record 1453 1995 98 104
40. L. Srivastava, S.N. Singh, and J. Sharma Comparison of feature selection techniques for ANN-based voltage estimation Electric Power Systems Research 53 3 2000 187 195 (Pubitemid 30552804)
41. F. Tillema, K.M. Van Zuilekom, and M.F.A.M. Van Maarseveen Comparison of neural networks and gravity models in trip distribution Computer-Aided Civil and Infrastructure Engineering 21 2006 104 119
42. C.P.I. Van Hinsbergen, J.W.C. Van Lint, and H.J. Van Zuylen Bayesian committee of neural networks to predict travel times with confidence intervals Transportation Research Part C 17 5 2009 498 509
43. Van Lint, J.W.C.; 2004. Reliable travel time prediction for freeways, Ph.D. thesis, Delft University of Technology, the Netherlands.
44. J.W.C. Van Lint, S.P. Hoogendoorn, and H.J. Van Zuylen Accurate freeway travel time prediction with state-space neural networks under missing data Transportation Research Part C 13 56 2005 347 369
45. Y. Xie, D. Lord, and Y. Zhang Predicting motor vehicle collisions using Bayesian neural networks: an empirical analysis Accident Analysis & Prevention 39 2007 922 933 5 2007 922 933 (Pubitemid 47374052)
46. J. Yang, J.J. Lu, M. Gunaratne, and B. Dietrich Modeling crack deterioration of flexible pavements: comparison of recurrent Markov chains and artificial neural networks Transportation Research Record 1974 2006 18 25 (Pubitemid 46219552)