PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

User habits and multimodal route planning

Autorzy
Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The results of route planning researches are monitored by logistic and automotive industries. The economic aspects of the cost saving are in the focus of the attention. An optimal route could cause time or fuel savings. An effective driving or an optimal route is a good basis to achieve an economical aim. Moreover the spread of new automotive solutions especially in case of electric cars the optimisation has particular significance regarding the limited battery storage. Additionally the autonomous car development could not be neglected. As a result the society could expect safer roads, better space usage and effective resource management. Nevertheless the requirements of users are extremely diverse, which is not negligible. Supporting these aims, in this paper the connection between the multimodal route planning and the user requirements are investigated. The examination is focused to a sensitivity analysis and a survey to evaluate the data and support the settings of a user habit effect to the final route.
Rocznik
Tom
Strony
22--27
Opis fizyczny
Bibliogr. 19 poz., rys., tab.
Twórcy
autor
  • Department of Automotive Technologies, Faculty of Transport Engineering and Vehicle Engineering (KJK), Budapest University of Technology and Economics H-1111 Budapest Sztoczek u. 6., Building J, 5th floor., Hungary
autor
  • Department of Transport Technology and Economics, Faculty of Transportation Engineering and Vehicle Engineering (KJK), Budapest University of Technology and Economics H-1111, Budapest Stoczek u. 2., Building St, Hungary
Bibliografia
  • 1. CENTRE FOR BUDAPEST TRANSPORT (BKK), 2011-2017. Centre for Budapest Transport (BKK) - For developers. [Online] Available at: http://www.bkk.hu/en/developers/ [Accessed 10 01 2017].
  • 2. DIJKSTRA E.W. 1959. A Note on Two Problems in Connexion with Graphs. Numerische Mathematik, 269-271.
  • 3. DORIGO M. 1992. “Optimization, learning and natural algorithms” (in Italian). Dipartimento di Elettronica, Politecnico di Milano, Italy: s.n.
  • 4. DORIGO M., BLUM C. 2005. Ant colony optimization theory: A survey. Theoretical Computer Science 344, 243-278.
  • 5. ESZTERGÁR-KISS D., CSISZÁR C. 2015. Evaluation of multimodal journey planners and definition of service levels. International journal of intelligent transportation systems research, 13(3), 154-165.
  • 6. FRIEDRICH M. 1994. Rechnergestütztes Entwurfsverfahren für den ÖPNV im ländlichen Raum (Dissertation), München: Schriftenreihe des Lehrstuhls für Verkehrs- und Stadtplanung, Heft 5, Technische Universität München.
  • 7. GOOGLE INC. 2017. Google play. [Online] Available at: https://play.google.com/store/apps/details?id=com.waze [Accessed 20 01 2017].
  • 8. HERNÁTH Z. 2012. Real-time adaptive A* routeplanning algorithm (in hungarian: Valós idejű adaptív A* útkeresési algoritmus). Budapest(Budapest): Budapest University of Technology and Economics, Faculty of Electrical Engineering and Informatics (VIK), Department of Measurement and Information Systems (MIT), Intelligent Systems Research Group (in hun:BME VIK MIT Intelligens Rendszerek Kutatócsoport).
  • 9. HÖGBERG P. 1976. Estimation of parameters in models for traffic prediction: A non-linear regression approach. Transportation Research, 10(4), 263-265.
  • 10. KATONA G., JUHÁSZ J. 2017. User habit effected multimodal route planning.In: 34th International Colloquium on Advanced Manufacturing and Repairing Technologies in Vehicle Industry. Budapest: Budapest University of Technology and Economics Faculty of Transportation Engineering and Vehicle Engineering Department of Automotive Technologies, 69-72.
  • 11. KATONA G., LÉNÁRT B., JUHÁSZ J., 2016. Multimodális útvonaltervezés. In: Közlekedéstudományi Konferencia. Győr: Széchenyi István Egyetem, 367-379.
  • 12. KATONA G., LÉNÁRT B., JUHÁSZ J. 2015. Compare Ant-colony and Genetic algorithm for shortest path problem and introduce their parallel implementations. Budapest, Budapest University of Technology and Economics, Faculty of Transportation Engineering and Vehicle Engineering, 312-319.
  • 13. KIRCHLER D. 2013. Effcient routing on multi-modal transportation networks. Ecole Polytechnique X: s.n.
  • 14. NOREIKIS M., BUTKUS P., NURMINEN J.K. 2014. In-Vehicle Application for Multimodal Route Planning and Analysis, Aalto: Aalto University, Finland.
  • 15. PODOBNI K. 2009. Algorithms for solving shortest path problem (in hungarian: Legrövidebb útkereső algoritmusok diplomamunka). Budapest(Budapest): Eötvös Loránd University, Faculty of Science (in hungarian: Eötvös Loránd Tudományegyetem, Természettudományi Kar).
  • 16. PTV AG, 2014. PTV VISUM 14 Manual, 76131 Karlsruhe Germany: PTV AG, Karlsruhe, Germany.
  • 17. SHEFFI Y. 1985. Urban transportation network: Equilibrium analysis with mathematical programming methods. Englewood Cliffs, N.J. 07632: Prentice-Hall, Inc.
  • 18. YU H., LU F. 2011. Multi-modal route planning approach with an improved genetic algorithm. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 38(2), 343-348.
  • 19. ZHANG J., ARENTZE T., TIMMERMANS H. 2012. A Multimodal Transport Network Model for Advanced Traveler Information System. Journal of Ubiquitous Systems & Pervasive Networks, 4(1), 21-47.
Uwagi
Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-a89b330d-8beb-44ed-b4ac-3f4c8a7d90bc
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.