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Improvement of urban taxi services by using a mobile application

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Traditionally, besides personal vehicles, individual passenger transportation is carried out by taxi services. These services have both positive sides, such as, for example, door-to-door delivery and comfort, as well as negative sides, such as the high cost of the trip. On the one hand, the services, such as Uber and Gett, attract customers with the help of low prices, but, on the other hand, they have a number of disadvantages, the main of which is the problem concerning transportation security. In addition, it must be mentioned that the usage of individual transport is often associated with incomplete load of vehicles, which causes an additional negative impact on the environment as well as on the road network. This article presents a decision support system for taxi dispatch services based on the model of optimal route choice. Optimization is carried out with the help of multifactor analysis of transportation requirements and selection of the optimal route in accordance with given priorities. Such a system will reduce the cost of transportation through the selection of fellow travelers as well as a negative impact on the environment by using a mobile application.
Rocznik
Strony
48--54
Opis fizyczny
Bibliogr. 15 poz.
Twórcy
  • Cracow University of Technology, Warszawska 24, 31-155 Krakow, Poland
autor
  • Cracow University of Technology, Warszawska 24, 31-155 Krakow, Poland
  • Kazan Federal University, Av. Syuyumbike 10a, 423810 Naberezhnye Chelny, Russia
Bibliografia
  • [1] CETIN T., DEAKIN E.: Regulation of taxis and the rise of ridesharing. In: Transport Policy, 2017. URL: http://dx.doi. org/10.1016/j.tranpol.2017.09.002 [date of access: 17.01.2018]
  • [2] DELOITTE: Mobility of the future: How transport technology and social trends affect the development of a new business ecosystem, 2015. URL: https://www2.deloitte.com/content/dam/insights/us/articles/transportation-technology/DUP-1374_Future-of-mobility_vFINAL_4.15.16.pdf [date of access: 17.01.2018]
  • [3] FLORES O., et al.: How cities use regulation for innovation: the case of Uber, Lyft and Sidecar in San Francisco. In: Transportation Research Procedia, 25, 2017, p. 3756–3768
  • [4] HE F., et al.: Pricing and penalty/compensation strategies of a taxi-hailing platform. In: Transportation Research Part C, 86, 2018, p. 263–279
  • [5] HONG Z., et al.: Commuter ride-sharing using topologybased vehicle trajectory clustering: Methodology, application and impact evaluation. In: Transportation Research Part C, 85, 2017, p. 573-590
  • [6] KE J., et al.: Short-term forecasting of passenger demand under on-demand ride services: A spatio-temporal deep learning approach. In: Transportation Research Part C, 85, 2017, p. 591–608
  • [7] KIM K.: Exploring the difference between ridership patterns of subway and taxi: Case study in Seoul. In: Journal of Transport Geography, 66, 2018, p. 213-223
  • [8] KIM S.W., et al.: Autonomous Campus Mobility Services Using Driverless Taxi. In: IEEE Transactions on inteligent transportation systems, 18 (12), December 2017, p. 2213-2226
  • [9] NASSEREDDINE M., ESKANDARI H.: An integrated MCDM approach to evaluate public transportation systems in Tehran.: Transportation Research Part A, 106, 2017, p. 427–439
  • [10] TANG J., et al.: Taxi trips distribution modeling based on Entropy-Maximizing theory: A case study in Harbin city –China. In: Physica A, 493, 2018, p. 430-443
  • [11] TONG L.C., et al.: Customized bus service design for jointly optimizing passenger-to-vehicle assignment and vehicle routing. In: Transportation Research Part C, 85, 2017, p. 451-475
  • [12] Transport for London: London Dial-a-Ride, 2017. URL: https://tfl.gov.uk/corporate/about-tfl/what-we-do/dial-a-ride. [date of access: 17.01.2018]
  • [13] WENG G.S., et al.: Mobile taxi booking application service’s continuance usage intention by users. In: Transportation Research Part D, 57, 2017, p. 207-216
  • [14] YANG ZH., et al.: Analysis of Washington, DC taxi demand using GPS and land-use data. In: Journal of Transport Geography, 66, 2018, p. 35-44
  • [15] ZHU G., et al.: Analysing journey-to-work data using complex networks. In: Journal of Transport Geography, 66, 2018, p. 65–79
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-33dbdd3e-c799-4e4b-8e8d-7a23454fabe0
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