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2017 | 5 | nr 4 Trends and Challenges of the Contemporary Management | 85--97
Tytuł artykułu

Urban Traffic Modeling and Simulation

Autorzy
Treść / Zawartość
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The article describes the use of different methods of building both micro- and macro-models of urban traffic. Traffic at intersections can be modeled with a fixed time increment allowing microscopic traffic analysis at the intersection. Attention was drawn to the importance of event-based models, exemplified by solutions based on hybrid and colored Petri nets. One of the newer solutions is a model that uses agent-based technology to take account of the impact of all traffic participants in the city. The article also describes the use of neural networks in the construction and implementation of urban traffic models. Generative model of artificial neural networks can complement data not reachable in actual traffic measurement, deep learning can be used to posses data from video and impute of missing data. A combination of macroscopic intersection models constructed from deep multilayer neural networks can be used to construct a traffic light control system in a network of streets and intersections. (original abstract)
Rocznik
Tom
5
Strony
85--97
Opis fizyczny
Twórcy
  • University of Dąbrowa Górnicza, Poland
Bibliografia
  • Badura, D. (1983), Makroskopowy model ruchu na ciągu ulic i skrzyżowań, Sympozjum nt. Transport pasażerski - energetyczne aspekty transportu zbiorowego w konurbacji Górnośląskiej, Katowice.
  • Badura D., Wrona W. (1986), Symulacja ruchu drogowego w systemie wieloprocesorowym przy zastosowaniu modelu opartego na sieci Petriego, Zeszyty Naukowe Politechniki Sląskiej, 5: 27-46.
  • Barcelos de Oliveira, L., Camponogara, E. (2010), Multi-agent model predictive control of signaling split in urban traffic networks, Elsevier Transportation Research Part C: Emerging Technologies, 18(1): 120-139.
  • Brilon, W., Wu, N. (1998), Evaluation of cellular automata for traffic flow simulation on freeways and urban streets, Tagungsband zum Ergebnis-Workshop: Verkehr und Mobität, (pp. 111-117), Aachen: Rheinisch-Westfälische Technische Hochschule Aachen.
  • David, R., Alla, H. (2001), On hybrid Petri nets, Discrete Event Dynamic Systems: Theory and Applications, 11: 9-40.
  • Di Febbraro, A, Giglio, D., Sacco, N. (2004), Urban traffic control structure based on hybrid Petri nets, IEEE Transactions on Intelligent Transportation Systems, 5(4): 224-237.
  • Di Febbraro, A., Giglio, D., Sacco, N. (2001), Modular representation of urban traffic systems based on hybrid Petri nets, Intelligent Transportation Systems Proceedings: 866-871.
  • Dotoli, M., Pia Fanti, M. (2006), An urban traffic network model via coloured timed Petri nets, Control Engineering Practice, 14(10): 1213-1229.
  • Gerlough, D.L. (1955), Simulation of freeway traffic on a general-purpose discrete variable computer, Los Angeles: University of California.
  • Hongbin Yin, Wong, S.C., Jianmin, Xu, Wong, C.K. (2002), Urban traffic flow prediction using a fuzzy-neural approach, Transportation Research Part C: Emerging Technologies, 10(2): 85-98.
  • Ledoux, C. (1997), An urban traffic flow model integrating neural networks, Transportation Research Part C: Emerging Technologies, 5(5): 287-300.
  • Lippi, M., Bertini, M., Frasconi, P. (2010), Collective traffic forecasting, in: J.L. Balcázar, F. Bonchi, A. Gionis, M. Sebag (Eds.), Machine learning and knowledge discovery in databases. ECML PKDD 2010. Lecture Notes in Computer Science, vol. 6322 (pp. 259-273), Berlin- Heidelberg: Springer.
  • Murata, T. (1989), Petri nets: Properties, analysis, and applications, Proceedings of the IEEE, 77: 541-580.
  • Nagui, M. R., Byungkyu, B.P., Sacks, J. (2000), Direct signal timing optimization: Strategy development and results, CitySeer'10M, paper presented at the XI Pan American Conference in Traffic and Transportation Engineering, Gramado, Brazil, May 2000, pp. 1-13.
  • Payne, H.J. (1971), Models of freeway traffic and control, in: G.A. Bekey (Ed.), Mathematical models of public systems, Simulation Council, 1: 51-61.
  • Su, Haowei, Yu, Shu (2007), Hybrid GA based online support vector machine model for short-term traffic flow forecasting, in: Xu Ming, Zhan Yinwei, Cao Jiannong, Liu Yijun, (Eds.), Advanced Parallel Processing Technologies (pp. 743-752), Berlin- Heidelberg: Springer.
  • Shu, L., Yugeng, X. (2008), An efficient model for urban traffic network control, Proceedings of the 17th World Congress, The International Federation of Automatic Control, Seoul, Korea, 6-11 July: 743-752.
  • Xiaolei, Ma, Zhuang, Dai, Zhengbing, He, Jihui, Ma, Yong Wang, Yunpeng, Wang (2017), Learning traffic as images: A deep convolutional neural network for large-scale transportation network speed prediction, Sensors, 17: 818.
  • Yanjie, D., Yisheng L., Wenwen, K., Yifei, Z. (2014), A deep learning based approach for traffic data imputation, paper presented at the IEEE 17th International Conference on Intelligent Transportation Systems (ITSC), Qingdao, China, 8-11 October.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.ekon-element-000171533072

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