Use of data from satellite navigation system in operational and strategic management of transport in cities

• Autorzy: Oskarbski Jacek , Birr Krystian , Żarski Karol

Identyfikatory

DOI

10.2478/jok-2019-0088

Warianty tytułu

PL

Wykorzystanie danych z systemu nawigacji satelitarnej w operacyjnym i strategicznym zarządzaniu transportem w miastach

• Języki publikacji: EN PL

Abstrakty

EN

The article presents the possibilities of using data from the Global Positioning System for the development of traffic models and examples of use this data in the transport management. Traffic models are useful tools in planning and evaluation of transport solutions, but also can be used for current, operational transport management.

PL

W artykule przedstawiono możliwości wykorzystania danych z systemu GPS do budowy modeli ruchu oraz przykłady wykorzystania tych danych w bieżącym, operacyjnym zarządzaniu transportem. Modele ruchu są użytecznymi narzędziami planowania i oceny rozwiązań transportowych, ale mogą być również wykorzystywane do bieżącego, operacyjnego zarządzania transportem.

Słowa kluczowe

EN

Intelligent Transportation Systems; GPS; transport telematics

PL

Inteligentne Systemy Transportu; GPS; telematyka transportu

• Wydawca: Wydawnictwo Instytutu Technicznego Wojsk Lotniczych
• Czasopismo: Journal of KONBiN
• Rocznik: 2019
• Tom: Vol. 49, iss. 4
• Strony: 297--320
• Opis fizyczny: Bibliogr. 24 poz., rys.

Twórcy

autor

Oskarbski Jacek

• Gdansk University of Technology (Politechnika Gdańska)

autor

Birr Krystian

• Gdansk University of Technology (Politechnika Gdańska)

autor

Żarski Karol

• Gdansk University of Technology (Politechnika Gdańska)

Bibliografia

• 1. Adamski A.: Priorytetowe sterowanie w transporcie publicznym z wykorzystaniem metod PIACON-DISCON. Transport Miejski i Regionalny, 4/2006, Kraków 2006.
• 2. Birr K., Oskarbski J., Żarski K.: Module of priorities for public transport vehicles in the TRISTAR system. Logistyka, 4/2014, Poznań 2014.
• 3. Cardoso F., Serrador A., Canas T.: Algorithms for Road Safety Based on GPS and Communications Systems WAVE. Procedia Technology, vol. 17, 2014, http://doi.org/10.1016/j.protcy.2014.10.187
• 4. Ciscal-Terry W., Dell'Amico M., Hadjidimitriou N. S., Iori M.: An analysis of drivers route choice behaviour using GPS data and optimal alternatives. Journal of Transport Geography, vol. 51, 2016, http://doi.org/10.1016/j.jtrangeo.2015.12.003
• 5. Crainic T.G., Gendreau M., Potvin J.Y.: Intelligent freight-transportation systems: Assessment and the contribution of operations research. Transportation Research Part C, no. 17, 2009.
• 6. Dubey P. P., Borkar P.: Review on techniques for traffic jam detection and congestion avoidance. 2nd International Conference on Electronics and Communication Systems, ICECS, 2015, http://doi.org/10.1109/ECS.2015.7124941
• 7. Gattuso D., Pellicanò D.S.: Advanced Methodological Researches Concerning ITS in Freight Transport. Procedia-Social and Behavioral Sciences, vol. 111, 2014.
• 8. Gentile G., Vigo D.: Movement generation and trip distribution for freight demand modelling applied to city logistics. European Transport\ Trasporti Europei, iss. 54, 2013.
• 9. Gondek S. :Uprzywilejowanie pojazdów komunikacji zbiorowej na skrzyżowaniach z sygnalizacją świetlną. Transport Miejski i Regionalny, nr 11, 2004.
• 10. Gonzalez-Feliu J., Routhier J.-L.: Modeling Urban Goods Movement: How to be Oriented with so Many Approaches? Procedia – Social and Behavioral Science, vol. 39, 2012.
• 11. Gupta A., Choudhary S, Paul S.: DTC: A framework to Detect Traffic Congestion by mining versatile GPS data. 1st International Conference on Emerging Trends and Applications in Computer Science (ICETACS), 2013.
• 12. Hoh B., Iwuchukwu T., Jacobson Q., Work D., Bayen A. M., Herring R., Herrera J. C., Gruteser M., Annavaram M., Ban J.: Enhancing Privacy and Accuracy in Probe Vehicle-Based Traffic Monitoring via Virtual Trip Lines. IEEE Transactions On Mobile Computing, vol. 11, iss. 5, 2012.
• 13. Jiménez-Meza A., Arámburo-Lizárraga J., de la Fuente E.: Framework for Estimating Travel Time, Distance, Speed, and Street Segment Level of Service (LOS), based on GPS Data. Procedia Technology, vol. 7, 2013, http://doi.org/10.1016/j.protcy.2013.04.008
• 14. K.L.S.M.H.B.S.T., Thiagarajan A., Ravindranath L., Eriksson J.: VTrack: Accurate, energy-aware road traffic delay estimation using mobile phones. Proceedings of the 7th ACM Conference on Embedded Networked Sensor Systems, Berkeley, California 2009.
• 15. Li M., Zhang L., Yang X.: A Review of Yellow Dilemma Problem and a Dynamic Speed Guidance System Design based on CVIS. Procedia – Social and Behavioral Sciences, vol. 96, 2013, http://doi.org/10.1016/j.sbspro.2013.08.055
• 16. Oliveira-Neto F., Chin S., Hwang H.: Assessing Temporal Effect of Economic Activity on Freight Volumes with Two-Period Cross-Sectional Data. Transportation Research Record, vol. 2285, iss. 2012, 2012.
• 17. Oskarbski J., Birr K., Miszewski M., Żarski K.: Estimating the average speed of public transport vehicles based on traffic control system data. International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS), IEEE, 2015, http://doi.org/10.1109/MTITS.2015.7223269
• 18. Oskarbski J., Kaszubowski D.: Potential for ITS/ICT Solutions in Urban Freight Management. Transportation Research Procedia, vol. 16, 2016, https://doi.org/10.1016/j.trpro.2016.11.041
• 19. Oskarbski J.: Perspectives of Telematics Implementation in Tri-City Transport Systems Management and Planning. 11th International Conference on Transport Systems Telematics (TST 2011). Modern Transport Telematics. Communications in Computer and Information Science. Vol. 239, 2011, https://link.springer.com/chapter/10.1007/978-3-642-24660-9_27
• 20. Oskarbski, J., Zawisza, M., Żarski K.: The structure of the data flow in integrated urban traffic management systems – the case of TRISTAR system. Archives of Transport System Telematics, vol. 9, iss. 4, 2016.
• 21. Taniguchi E., Thompson R.G., Yamada T.: Emerging Techniques for Enhancing the Practical Application of City Logistics Models. Procedia - Social and Behavioral Sciences, vol. 39, 2012.
• 22. Vasantha Kumar S., Vanajakshi L.: Mode-wise travel time estimation on urban arterials using transit buses as probes. Intelligent Vehicles Symposium (IV), IEEE, 2011.
• 23. Velaga N.R., Pangbourne K.: Achieving genuinely dynamic road user charging: issues with a GNSS-based approach. Journal of Transport Geography, vol. 34, 2014, http://doi.org/10.1016/j.jtrangeo.2013.09.013
• 24. Yoon J., Noble B., Liu M.: Surface street traffic estimation in MobiSys. ACM, 2007.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).