Przegląd zastosowań sieci neuronowych w transporcie

Pamuła, T.

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Przegląd zastosowań sieci neuronowych w transporcie

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Pamuła T.

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The rewiev of the applications of neural networks in transport

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Abstrakty

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.

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.

Słowa kluczowe

sieć neuronowa transport ruch drogowy sterowanie ruchem polityka transportowa ekonomia transportu

neuron network transport traffic motion control transport policy transport economy

Wydawca

Instytut Naukowo-Wydawniczy "TTS" Sp. z o.o

Czasopismo

TTS Technika Transportu Szynowego

Rocznik

2015

Tom

R. 22, nr 12

Strony

1186--1190, CD

Opis fizyczny

Bibliogr. 34 poz., tab.

Twórcy

autor

Pamuła T.

teresa.pamula@polsl.pl

Politechnika Śląska, Wydział Transportu

Bibliografia

1. M. Dougherty: A review of neural networks applied to transport, Transportation Research Part C 3 (1995) 247–260
2. Karlaftis M.G., Vlahogianni E.I.: Statistical methods versus neural networks in transportation research: Differences, similarities and some insights, Transportation Research Part C 19 (2011) 387–399
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.

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