A method and application to identify reasons for decreasing vehicles’ driving speed in cities

• Autorzy: Wala M. , Nowakowski P.

Identyfikatory

DOI

10.20858/sjsutst.2018.98.17

• Języki publikacji: EN

Abstrakty

EN

Vehicle onboard travel planning systems have been developed in recent years. Since the development of GPS-based devices equipped with digital mapping applications for many vehicles, route planning has become easier and more convenient for drivers. Although such systems are used by drivers, for delivery or courier companies, it is especially important to provide a high-quality service, which involves the timely delivery of goods. Traffic management authorities are also interested in acquiring data on road and traffic conditions to verify the effectiveness and smoothness of the flow of vehicles. This paper proposes a method for traffic data collection and an application for recording data of variable factors having impact on a vehicle speed in cities and agglomerations. Data acquisition and identification of factors having impact on reduction of vehicle speed in the cities has been presented for a case study of Gliwice. The results can be useful for traffic management authorities, municipal traffic, road planning departments and mobile apps designers.

Słowa kluczowe

EN

vehicle driving speed; mobile application; traffic factors

PL

prędkość jazdy pojazdu; aplikacja mobilna; czynniki ruchowe

• Wydawca: Wydawnictwo Politechniki Śląskiej
• Czasopismo: Zeszyty Naukowe. Transport / Politechnika Śląska
• Rocznik: 2018
• Tom: z. 98
• Strony: 181--190
• Opis fizyczny: Bibliogr. 24 poz.

Twórcy

autor

Wala M.

• PST Transgór S.A. Jankowicka 9 Street, 44-201 Rybnik, Poland

autor

Nowakowski P.

• Faculty of Transport, Silesian University of Technology, Krasińskiego 8 Street, 40-019 Katowice, Poland

Bibliografia

• 1. Savrasovs M., Pticina I. 2017. “Methodology of OD matrix estimation based on video recordings and traffic counts”. Procedia Engineering 178: 289-297. DOI: 10.1016/j.proeng.2017.01.116.
• 2. Yang H., T. Sasaki, Y. Iida, Y. Asakura. 1992. “Estimation of origin-destination matrices from link traffic counts on congested networks”. Transportation Research Part B: Methodological 26: 417-434. DOI: 10.1016/0191-2615(92)90008-K.
• 4. Gartner N.H., C.J. Messer, A.K. Rathi. 1997. Monograph on Traffic Flow Theory. Washington, DC: Federal Highway Administration.
• 3. Duduta N., C. Adriazola, D. Hidalgo, L.A. Lindau, R. Jaffe. 2015. “Traffic safety in surface public transport systems: a synthesis of research”. Public Transport 7(2): 121-137.
• 4. Weisbrod G., D. Vary, G. Treyz. 2003. “Measuring economic costs of urban traffic congestion to business”. Transportation Research Record: Journal of the Transportation Research Board 1839: 98-106.
• 5. Banister D. 1996. “Energy, quality of life and the environment: the role of transport”. Transport Reviews 16: 23-35.
• 6. Pandian S., S. Gokhale, A.K. Ghoshal. 2009. “Evaluating effects of traffic and vehicle characteristics on vehicular emissions near traffic intersections”. Transportation Research Part D: Transport and Environment 14: 180-196.
• 7. Ando Y., Y. Fukazawa, O. Masutani, H. Iwasaki, S. Honiden. 2006. “Performance of pheromone model for predicting traffic congestion”. In Proceedings of the Fifth International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS 2006): 73-80.
• 8. Lee Y.M., S.Y. Chong, K. Goonting, E. Sheppard. 2017. “The effect of speed limit credibility on drivers’ speed choice”. Transportation Research Part F: Traffic Psychology and Behaviour 45: 43-53. DOI: 10.1016/j.trf.2016.11.011.
• 9. Gargoum S.A., K. El-Basyouny, A. Kim. 2016. “Towards setting credible speed limits: Identifying factors that affect driver compliance on urban roads”. Accident Analysis & Prevention 95: 138-148. DOI: 10.1016/j.aap.2016.07.001.
• 10. Topolšek D., T. Cvahte Ojsteršek. 2017. „Do drivers behave differently when driving a car or riding a motorcycle?”. Transport\Transporti Europei 66(4): 1-16. ISSN: 1825-3997.
• 11. Viti F., S. Hoogendoorn, L. Immers, C. Tampère, S. Lanser. 2008. “National data warehouse: how the Netherlands is creating a reliable, widespread, accessible data bank for traffic information, monitoring, and road network control”. Transportation Research Record: Journal of the Transportation Research Board 2049: 176-185.
• 12. Mínguez R., S. Sánchez-Cambronero, E. Castillo, P. Jiménez. 2010. “Optimal traffic plate scanning location for OD trip matrix and route estimation in road networks”. Transportation Research Part B: Methodological 44: 282-298.
• 13. Dion F., H. Rakha H. 2006. “Estimating dynamic roadway travel times using automatic vehicle identification data for low sampling rates”. Transportation Research Part B: Methodological 40: 745-766.
• 14. Lu J., L. Cao. 2003. “Congestion evaluation from traffic flow information based on fuzzy logic”. In Proceedings: IEEE Intelligent Transportation Systems Vol. 1: 50-53.
• 15. Ashton W.D. 1996. The Theory of Road Traffic Flow. London: Methuen & Co.
• 16. Szczuraszek T. 2008. Prędkość pojazdów w warunkach drogowego ruchu swobodnego. Warsaw: Studia z Zakresu Inżynierii; Komitet Inżynierii Lądowej i Wodnej PAN, 2008; ISBN 978-83-89687-39-5.
• 17. Szczuraszek, T. 2008. Bezpieczeństwo ruchu miejskiego. Wydaw: Komunikacji i Łączności.
• 18. Jägerbrand A.K., J. Sjöbergh. 2016. “Effects of weather conditions, light conditions, and road lighting on vehicle speed”. SpringerPlus 5: 505. DOI: 10.1186/s40064-016-2124-6.
• 19. Fleischmann B., S. Gnutzmann, E. Sandvoß. 2004. “Dynamic vehicle routing based on online traffic information”. Transportation Science 38: 420-433.
• 20. Henclewood D., M. Hunter, R. Fujimoto. 2008. “Proposed methodology for a data-driven simulation for estimating performance measures along signalized arterials in real-time”. In Winter Simulation Conference: 2761-2768.
• 21. Florian M., M. Mahut, N. Tremblay. 2008. “Application of a simulation-based dynamic traffic assignment model”. European Journal of Operational Research 189: 1381-1392.
• 22. Schneider W. “Mobile phones as a basis for traffic state information”. In Proceedings: 2005 IEEE Intelligent Transportation Systems, 2005: 782-784.
• 23. Dimitriou L., T. Tsekeris, A. Stathopoulos. 2008. “Adaptive hybrid fuzzy rule-based system approach for modeling and predicting urban traffic flow”. Transportation Research Part C: Emerging Technologies 16, 554-573.
• 24. Castro-Neto M., Y.-S. Jeong, M.-K. Jeong, L.D. Han. 2009. “Online-SVR for short-term traffic flow prediction under typical and atypical traffic conditions”. Expert Systems with Applications 36: 6164-6173.