DOI: https://doi.org/10.15587/1729-4061.2019.160049

Analysis of the road traffic management system in the neural network development perspective

Anna Brzozowska, Dagmara Bubel, Antonina Kalinichenko

Abstract


The research goal of the paper is to present the issues connected with road traffic management systems and to illustrate a management system that uses Intelligent Transportation Systems and neural networks. The use of Intelligent Transportation Systems (ITS) is a method of improving the conditions of communications, making it independent from the development of communications infrastructure. The attributes of neural networks are focused on solving the problems of optimisation, which involve the development of optimal strategies for traffic management. The proposed road traffic management system that uses ITS and neural networks can be applied in prediction of the conditions of communications in road traffic management.

The paper presents the results of qualitative research carried out in the aspect of traffic volume forecasting on selected national roads, supported by a scientific search and discourse on logistic aspects of traffic management, with particular emphasis on Intelligent Transport Systems, in order to verify the effectiveness of the implementation of neural networks. The above mentioned issues are extremely important due to the necessity of knowing the expected load of routes. Traffic fluctuations related to factors such as time, traffic, road architecture and capacity utilization are important elements of traffic intensity. The study served to verify the effectiveness of four independent neural networks, forecasting the traffic volume, for seven days of the week, at particular time points. Empirical data utilised in presented qualitative research was derived from motion sensors, installed on selected national roads, at specific time intervals. It enabled prospects for the development of neural networks to be determined in a model perspective, constituting a set of artificial intelligence methods, in the context of vehicle traffic volume, which is characterised by certain repetitive regularities. The author's model of introducing an algorithm based on neural networks in application to measurements performed in transport in terms of quality, quantity and methods of data acquisition affects into the presented results. Different systems have been analyzed that are used to obtain data for modeling. As a result of various doubts, system maladjustments or excessive costs, alternative solutions have been proposed that can eliminate the presented problems. Solutions have been proposed that limit some of the problems reported by the authors in this regard. The presented research results justified the use of neural networks in measurements in transport. The results of the measurements were obtained in accordance with the actual observations and compared with the results of other systems. The authors analyze further required work and the possibilities of improving the solutions used

Keywords


Intelligent Transportation Systems; neural networks; road traffic management

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References


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Winkle, T. (2015). Sicherheitspotenzial automatisierter Fahrzeuge: Erkenntnisse aus der Unfallforschung. Autonomes Fahren, 351–376. doi: https://doi.org/10.1007/978-3-662-45854-9_17

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Buttgereit, A. (2017). Prozessorientierte Qualitaetssicherung im kommunalen Strassenbau. Strasse und Autobahn, 68 (7), 507–514.

Ebert, I. (2017). Automatisiertes Fahren aus Sicht der Versicherer. Autonome Systeme und neue Mobilität, 65–72. doi: https://doi.org/10.5771/9783845281667-65

Witkowski, J. (2017). Globaland local logistics strategies in international supply chains. Nauki o zarządzaniu, 31, 4–11. doi: https://doi.org/10.15611/noz.2017.2.01

Mesjasz-Lech, A. (2016). Cooperation in logistics networks – challenges and limitations. Zeszyty Naukowe Politechniki Śląskiej, 90, 81–96.

Schäffer, S. (2017). Verkehrspolitik. Jahrbuch der Europäischen Integration 2017, 317–320. doi: https://doi.org/10.5771/9783845284897-316

Krawczyk, S. (2009). Logistic controlling in transport networks. Prace Naukowe Politechniki Warszawskiej, 69, 89–100.

Nogalski, B., Niewiadomski, P. (2017). Participation in the network as an innovation growth factor and the way towards a flexible organization. Management Forum, 5 (3), 20–30. doi: https://doi.org/10.15611/mf.2017.3.04

Peddinti, V., Povey, D., Khudanpur, S. (2015). A time delay neural network architecture for efficient modeling of long temporal contexts. Sixteenth Annual Conference of the International Speech Communication Association. Dresden.

Golowko, K., Zimmermann, V., Zimmer, D. (2017). Automatisiertes Fahren Einflüsse auf die Rückhaltesysteme. ATZ – Automobiltechnische Zeitschrift, 119 (7-8), 26–33. doi: https://doi.org/10.1007/s35148-017-0070-4

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GOST Style Citations


Faye S., Chaudet C. Characterizing the Topology of an Urban Wireless Sensor Network for Road Traffic Management // IEEE Transactions on Vehicular Technology. 2016. Vol. 65, Issue 7. P. 5720–5725. doi: https://doi.org/10.1109/tvt.2015.2465811 

Konatowski S., Gołgowski M. Koncepcja systemu monitorowania ruchu pojazdów drogowych // Przegląd Elektrotechniczny. 2017. Vol. 1, Issue 10. P. 66–70. doi: https://doi.org/10.15199/48.2017.10.15 

Skowron-Grabowska B. Business models in transport services // Przegląd Organizacji. 2014. Issue 1. P. 35–39.

Ganzheitliche Werkzeugkette für die Entwicklung und Bewertung des automatisierten Fahrens / Zlocki A., Rösener C., Klaudt S., Eckstein L. // ATZextra. 2018. Vol. 23, Issue S5. P. 16–21. doi: https://doi.org/10.1007/s35778-018-0046-3 

Strubbe T., Thenée N., Wieschebrink Ch. IT-Sicherheit in Kooperativen Intelligenten Verkehrssystemen // Datenschutz und Datensicherheit – DuD. 2017. Vol. 41, Issue 4. P. 223–226. doi: https://doi.org/10.1007/s11623-017-0762-7 

Herausforderungen für die Verhaltensplanung kooperativer automatischer Fahrzeuge / Naumann M., Orzechowski P. F., Burger C., Şahin Taş Ö., Stiller C. // AAET Automatisiertes und vernetztes Fahren. Braunschweig, 2017. P. 287–307.

Torbacki W. Evaluation of exploitation factors in fleet vehicles management using fuzzy logic // Autobusy: technika, eksploatacja, systemy transportowe. 2017. Vol. 18, Issue 6. P. 1104–1107.

Rajak S., Parthiban P., Dhanalakshmi R. Sustainable transportation systems performance evaluation using fuzzy logic // Ecological Indicators. 2016. Vol. 71. P. 503–513. doi: https://doi.org/10.1016/j.ecolind.2016.07.031 

Gauda K. The role of genetic algorithms in the process of optimization determining driving routes // Autobusy: technika, eksploatacja, systemy transportowe. 2016. Vol. 17, Issue 11. P. 54–57.

Karakatič S., Podgorelec V. A survey of genetic algorithms for solving multi depot vehicle routing problem // Applied Soft Computing. 2015. Vol. 27. P. 519–532. doi: https://doi.org/10.1016/j.asoc.2014.11.005 

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Drechny M., Kolasa M. Sztuczna sieć neuronowa wspomagająca sterowanie oświetleniem ulicznym // Rynek Energii. 2016. Issue 2 (123). P. 90–95.

Kumar K., Parida M., Katiyar V. K. Short Term Traffic Flow Prediction for a Non Urban Highway Using Artificial Neural Network // Procedia – Social and Behavioral Sciences. 2013. Vol. 104. P. 755–764. doi: https://doi.org/10.1016/j.sbspro.2013.11.170 

Pamuła T. Classification and Prediction of Traffic Flow Based on Real Data Using Neural Networks // Archives of Transport. 2012. Vol. 24, Issue 4. doi: https://doi.org/10.2478/v10174-012-0032-2 

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Lorenzo M., Matteo M. OD Matrices Network Estimation from Link Counts by Neural Networks // Journal of Transportation Systems Engineering and Information Technology. 2013. Vol. 13, Issue 4. P. 84–92. doi: https://doi.org/10.1016/s1570-6672(13)60117-8 

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Deka L., Quddus M. Network-level accident-mapping: Distance based pattern matching using artificial neural network // Accident Analysis & Prevention. 2014. Vol. 65. P. 105–113. doi: https://doi.org/10.1016/j.aap.2013.12.001 

Durduran S. S. A decision making system to automatic recognize of traffic accidents on the basis of a GIS platform // Expert Systems with Applications. 2010. Vol. 37, Issue 12. P. 7729–7736. doi: https://doi.org/10.1016/j.eswa.2010.04.068 

Stuhr R. Grundlagen zur Bilanzierung der Straßeninfrastruktur // Ansätze zur Bilanzierung des staatlichen Straßeninfrastrukturvermögens. 2018. P. 25–78. doi: https://doi.org/10.1007/978-3-658-23609-0_2 

Ten Hompel M., Henke M. Logistik 4.0 – Ein Ausblick auf die Planung und das Management der zukünftigen Logistik vor dem Hintergrund der vierten industriellen Revolution // Handbuch Industrie 4.0 Bd.4. 2017. P. 249–259. doi: https://doi.org/10.1007/978-3-662-53254-6_13 

Haas A. Intelligence Systeme in Transition zwischen Theorie und Praxis – ein Bezugsrahmen des Metamodells // Intelligence Systeme im Logistik- und Supply Chain Management. 2018. P. 93–198. doi: https://doi.org/10.1007/978-3-658-21466-1_3 

Autonomes Fahren / Matthaei R., Reschka A., Rieken J., Dierkes F., Ulbrich S., Winkle T., Maurer M. // Handbuch Fahrerassistenzsysteme. 2015. P. 1139–1165. doi: https://doi.org/10.1007/978-3-658-05734-3_61 

Winkle T. Entwicklungs- und Freigabeprozess automatisierter Fahrzeuge: Berücksichtigung technischer, rechtlicher und ökonomischer Risiken // Autonomes Fahren. 2015. P. 611–635. doi: https://doi.org/10.1007/978-3-662-45854-9_28 

Funktionale Sicherheit von autonomen Transportsystemen in flexiblen I4.0 Fertigungsumgebungen / Kleen P., Albrecht J., Jasperneite J., Richter D. // Informatik aktuell. 2018. P. 11–20. doi: https://doi.org/10.1007/978-3-662-58096-7_2 

Ulusay-Alpay B. Informationstechnologie unter Erreichbarkeit –intelligentes Transportsystem: eine Studie für den Stadtverkehr in Istanbul // REAL CORP 2016–SMART ME UP! How to become and how to stay a Smart City, and does this improve quality of life? Proceedings of 21st International Conference on Urban Planning, Regional Development and Information Society. CORP–Competence Center of Urban and Regional Planning, 2016. P. 369–374.

Winkle T. Sicherheitspotenzial automatisierter Fahrzeuge: Erkenntnisse aus der Unfallforschung // Autonomes Fahren. 2015. P. 351–376. doi: https://doi.org/10.1007/978-3-662-45854-9_17 

Holland H. Connected Cars // Dialogmarketing und Kundenbindung mit Connected Cars. 2019. P. 51–81. doi: https://doi.org/10.1007/978-3-658-22929-0_3 

Buttgereit A. Prozessorientierte Qualitaetssicherung im kommunalen Strassenbau // Strasse und Autobahn. 2017. Vol. 68, Issue 7. P. 507–514.

Ebert I. Automatisiertes Fahren aus Sicht der Versicherer // Autonome Systeme und neue Mobilität. 2017. P. 65–72. doi: https://doi.org/10.5771/9783845281667-65 

Witkowski J. Globaland local logistics strategies in international supply chains // Nauki o zarządzaniu. 2017. Issue 31. P. 4–11. doi: https://doi.org/10.15611/noz.2017.2.01 

Mesjasz-Lech A. Cooperation in logistics networks – challenges and limitations // Zeszyty Naukowe Politechniki Śląskiej. 2016. Vol. 90. P. 81–96.

Schäffer S. Verkehrspolitik // Jahrbuch der Europäischen Integration 2017. 2017. P. 317–320. doi: https://doi.org/10.5771/9783845284897-316 

Krawczyk S. Logistic controlling in transport networks // Prace Naukowe Politechniki Warszawskiej. 2009. Vol. 69. P. 89–100.

Nogalski B., Niewiadomski P. Participation in the network as an innovation growth factor and the way towards a flexible organization // Management Forum. 2017. Vol. 5, Issue 3. P. 20–30. doi: https://doi.org/10.15611/mf.2017.3.04 

Peddinti V., Povey D., Khudanpur S. A time delay neural network architecture for efficient modeling of long temporal contexts // Sixteenth Annual Conference of the International Speech Communication Association. Dresden, 2015.

Golowko K., Zimmermann V., Zimmer D. Automatisiertes Fahren Einflüsse auf die Rückhaltesysteme // ATZ – Automobiltechnische Zeitschrift. 2017. Vol. 119, Issue 7-8. P. 26–33. doi: https://doi.org/10.1007/s35148-017-0070-4 

Salman Y., Ku-Mahamud K., Kamioka E. Distance measurement for self-driving cars using stereo camera // Proceedings of the 6th International Conference of Computing & Informatics. Sintok: School of Computing, 2017. P. 235–242.







Copyright (c) 2019 Anna Brzozowska, Dagmara Bubel, Antonina Kalinichenko

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ISSN (print) 1729-3774, ISSN (on-line) 1729-4061