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Tytuł artykułu

Przegląd zastosowań sieci neuronowych w transporcie

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Warianty tytułu
EN
The rewiev of the applications of neural networks in transport
Języki publikacji
PL
Abstrakty
PL
Zdolność sieci neuronowej do odwzorowania nieliniowych zależności między zmiennymi opisującymi zachowanie obiektów oraz możliwość opracowania efektywnej konfiguracji sprzyja zastosowaniom w transporcie. W artykule przedstawiono reprezentatywne przykłady z zakresu: predykcji parametrów ruchu drogowego, sterowania ruchem drogowym, pomiarów parametrów ruchu, zachowania kierowców i prowadzenia autonomicznych pojazdów, ekonomii i polityki transportowej oraz omówiono własności proponowanych rozwiązań. Najczęściej wybieranymi sieciami neuronowymi są jednokierunkowe wielowarstwowe, trenowane z użyciem algorytmu propagacji wstecznej. W przeglądzie wzięto pod uwagę artykuły opublikowane w czasopismach w ciągu ostatnich pięciu lat.
EN
The neuron networks capability to map nonlinear functions of variables describing the behaviour of objects and the simplicity of designing their configuration favours the applications in transport. The paper presents representative examples in the scope of: prediction of road traffic parameters, road traffic control, measurement of road traffic parameters, drivers behaviour and autonomous vehicles, economy and transport policies. The features of the solutions are examined. Feedforward multilayer neural networks, trained using backpropagation, are the most often utilised configurations in transport applications. In the review was taken into account the articles published in journals over the past five years.
Rocznik
Strony
1186--1190, CD
Opis fizyczny
Bibliogr. 34 poz., tab.
Twórcy
autor
  • Politechnika Śląska, Wydział Transportu
Bibliografia
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  • 3. Tadeusiewicz R.: Sieci neuronowe. Akademicka Oficyna Wydaw. RM, Warszawa, (1993)
  • 4. Kehagias D., Salamanis A., Tzovaras D.: Speed Pattern Recognition Technique for Short–Term Traffic Forecasting based on Traffic Dynamics, IET Intelligent Transport Systems, Vol. 9, No. 6, (2015), 646-653
  • 5. Van Hinsbergen, C.P.I.J.; Hegyi, A.; van Lint, J.W.C.; van Zuylen, H.J.: Bayesian neural networks for the prediction of stochastic travel times in urban networks, IET Intelligent Transport Systems, vol.5, no.4, (2011) 259-265
  • 6. Qing Ye; Szeto, W.Y.; Wong, S.C., Short-Term Traffic Speed Forecasting Based on Data Recorded at Irregular Intervals, IEEE Transactions on Intelligent Transportation Systems, vol.13, no.4, (2012), 1727-1737
  • 7. Kranti Kumar, M. Parida, V.K. Katiyar, Short Term Traffic Flow Prediction for a Non Urban Highway Using Artificial Neural Network, Procedia - Social and Behavioral Sciences, Vol. 104, (2013), 755-764
  • 8. Pamuła T.: Classification and Prediction of Traffic Flow Based on Real Data Using Neural Networks, Archives of Transport 4/2012, pp. 519-529
  • 9. Kit Yan Chan; Dillon, Tharam S.; Singh, J.; Chang, E.: Neural-Network-Based Models for Short-Term Traffic Flow Forecasting Using a Hybrid Exponential Smoothing and Levenberg–Marquardt Algorithm, IEEE Transactions on Intelligent Trans-portation Systems, vol.13, no.2, (2012), 644-654
  • 10. Dongbin Zhao; Yujie Dai; Zhen Zhang: Computational Intelligence in Urban Traffic Signal Control: A Survey, IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applica-tions and Reviews, vol.42, no.4, (2012), 485-494
  • 11. Box S., Waterson B.: An automated signalized junction controller that learns strategies from a human expert, Engineering Applications of Artificial Intelligence, Vol. 25, 1, (2012), 107-118
  • 12. Singh R. R., Conjeti S., Banerjee R.: A comparative evaluation of neural network classifiers for stress level analysis of automotive drivers using physiological signals, Biomedical Signal Processing and Control, Vol.8, 6, (2013), 740-754
  • 13. Patel M., Lal S.K.L., Kavanagh D., Rossiter P.: Applying neural network analysis on heart rate variability data to assess driver fatigue, Expert Systems with Applications, Vol. 38, 6, (2011), 7235-7242
  • 14. Qichang He, Wei Li, Xiumin Fan, Zhimin Fei: Driver fatigue evaluation model with integration of multi-indicators based on dynamic Bayesian network, IET Intelligent Transport Systems, vol.9, no.5, (2015) 547-554
  • 15. Larue, G.S., Rakotonirainy, A., Pettitt, A.N.: Predicting Reduced Driver Alertness on Monotonous Highways, IEEE Per-vasive Computing, vol.14, no.2, (2015), 78-85
  • 16. Wollmer, M., Blaschke, C., Schindl, T., Schuller, B., Farber, B., Mayer, S., Trefflich, B.: Online Driver Distraction Detection Using LongShort-Term Memory, IEEE Transactions on Intelligent Transportation Systems, vol.12, no.2, (2011), 574-582
  • 17. Chong L., Abbas M. M., Flintsch A. M., Higgs B.: A rule-based neural network approach to model driver naturalistic behavior in traffic, Transportation Research Part C: Emerging Technolo-gies, Vol. 32, (2013), 207-223
  • 18. Xu, L., Hu, J., Jiang, H., Meng, W.: Establishing Style-Oriented Driver Models by Imitating Human Driving Behaviors, IEEE Transactions on Intelligent Transportation Systems, vol.16, no.5, (2015), 2522-2530
  • 19. Souza J. R., Pessin G., Shinzato P. Y., Osorio F. S., Wolf D. F.: Vision-based waypoint following using templates and artificial neural networks, Neurocomputing, Vol. 107, 1 (2013), 77-86
  • 20. Borenovic, M.; Neskovic, A.; Neskovic, N.: Vehicle Positioning Using GSM and Cascade-Connected ANN Structures, IEEE Transactions on Intelligent Transportation Systems, vol.14, no.1, (2013), 34-46
  • 21. Chi-Feng Wu, Cheng-Jian Lin, Chi-Yung Lee: Applying a Func-tional Neurofuzzy Network to Real-Time Lane Detection and Front-Vehicle Distance Measurement, IEEE Transactions on Systems Man and Cybernetics, Part C, vol.42, no.4, (2012), 577-589
  • 22. Lin Cai, Rad, A.B., Wai-Lok Chan: An Intelligent Longitudinal Controller for Application in Semiautonomous Vehicles, IEEE Transactions on Industrial Electronics, vol.57, no.4, (2010), 1487-1497
  • 23. Zhang J., Zhao X., He X.: A Minimum Resource Neural Net-work Framework for Solving Multiconstraint Shortest Path Problems, IEEE Transactions on Neural Networks and Learn-ing Systems, vol.25, no.8, (2014), 1566-1582
  • 24. Nazemi A., Omidi F.: An efficient dynamic model for solving the shortest path problem, Transportation Research Part C: Emerging Technologies, Vol. 26, (2013), 1-19
  • 25. Lorenzo M., Matteo M.: OD Matrices Network Estimation from Link Counts by Neural Networks, Journal of Transportation Systems Engineering and Information Technology, Vol. 13, 4, (2013), 84-92
  • 26. Jungme Park, Y.L. Murphey, R. McGee, J.G. Kristinsson, M.L. Kuang, A.M. Phillips: Intelligent Trip Modeling for the Prediction of an Origin–Destination Traveling Speed Profile, IEEE Trans-actions on Intelligent Transportation Systems, vol.15, no.3, (2014), 1039-1053
  • 27. Naranjo J.E., Jime nez F., Serradilla F.J., Zato J.G.: Floating Car Data Augmentation Based on Infrastructure Sensors and Neural Networks, IEEE Transactions on Intelligent Transporta-tion Systems, vol.13, no.1, (2012), 107-114
  • 28. Deka L., Quddus M.: Network-level accident-mapping: Dis-tance based pattern matching using artificial neural network, Accident Analysis & Prevention, Vol. 65, (2014), 105-113
  • 29. Durduran S. S.: A decision making system to automatic recog-nize of traffic accidents on the basis of a GIS platform, Expert Systems with Applications, Vol. 37, 12, (2010), 7729-7736
  • 30. Garcia T. R., Cancelas N. G., Soler-Flores F.: The Artificial Neural Networks to Obtain Port Planning Parameters, Procedia - Social and Behavioral Sciences, Vol. 162, (2014), 168-177
  • 31. Chen M. Li, W.: Application of BP Neural Network Algorithm in Sustainable Development of Highway Construction Projects, Physics Procedia, Vol. 25, (2012), 1212-1217
  • 32. Yingjun Y., Cui H., Shaoyang Z.: A prediction Model of the Number of Taxicabs Based on Wavelet Neural Network, Procedia Environmental Sciences, Vol.12, B, (2012), 1010-1016
  • 33. Gonzalez P.A., Weinstein J.S., Barbeau S.J., Labrador M.A., Winters P.L., Georggi N.L., Perez R.: Automating mode detection for travel behaviour analysis by using global positioning systems enabled mobile phones and neural networks, IET Intelligent Transport Systems, vol.4, no.1, (2010), 37-49
  • 34. Zhao Y., Triantis K., Teodorovic D., Edara P.: A travel demand management strategy: The downtown space reservation system, European Journal of Operational Research, Vol. 205, 3, (2010), 584-594.
Typ dokumentu
Bibliografia
Identyfikator YADDA
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