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Assessment of the DRT System Based on an Optimal Routing Strategy

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  • Jooyoung Kim

    (Department of Transport Planning & Management, Korea National University of Transportation, Uiwang-si, Gyeonggi-do 16106, Korea)

Abstract

Demand responsive transport (DRT) is operated according to flexible routes, dispatch intervals, and dynamic demand, is attracting a lot of attention. The biggest characteristic of the DRT service is that the vehicle routes and schedules are operated optimally based on real-time travel requests of using passengers without fixed operating schedules. This study analyzed the feasibility of implementing the DRT service by analyzing the benefits for the users and cost of the operator from the effects of increasing public transportation use and providing personalized mobility service based on DRT implementation by the introduction of DRT using multi-agent transport simulation (MATSim). Through the simulation, the DRT is expected to provide convenient, fast, and cost-effective mobility services to customers; provide an optimal vehicle scale to providers; and, ultimately, achieve a safe and efficient transportation system.

Suggested Citation

  • Jooyoung Kim, 2020. "Assessment of the DRT System Based on an Optimal Routing Strategy," Sustainability, MDPI, vol. 12(2), pages 1-9, January.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:2:p:714-:d:310445
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    References listed on IDEAS

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    1. Chao Wang & Mohammed Quddus & Marcus Enoch & Tim Ryley & Lisa Davison, 2014. "Multilevel modelling of Demand Responsive Transport (DRT) trips in Greater Manchester based on area-wide socio-economic data," Transportation, Springer, vol. 41(3), pages 589-610, May.
    2. Jones, Peter & Lucas, Karen, 2012. "The social consequences of transport decision-making: clarifying concepts, synthesising knowledge and assessing implications," Journal of Transport Geography, Elsevier, vol. 21(C), pages 4-16.
    3. Mulley, Corinne & Nelson, John D., 2009. "Flexible transport services: A new market opportunity for public transport," Research in Transportation Economics, Elsevier, vol. 25(1), pages 39-45.
    4. Giannopoulos, G. A., 2004. "The application of information and communication technologies in transport," European Journal of Operational Research, Elsevier, vol. 152(2), pages 302-320, January.
    5. Jean-François Cordeau & Gilbert Laporte, 2007. "The dial-a-ride problem: models and algorithms," Annals of Operations Research, Springer, vol. 153(1), pages 29-46, September.
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    Cited by:

    1. András Lakatos & János Tóth & Péter Mándoki, 2020. "Demand Responsive Transport Service of ‘Dead-End Villages’ in Interurban Traffic," Sustainability, MDPI, vol. 12(9), pages 1-17, May.
    2. Raoul Rothfeld & Mengying Fu & Miloš Balać & Constantinos Antoniou, 2021. "Potential Urban Air Mobility Travel Time Savings: An Exploratory Analysis of Munich, Paris, and San Francisco," Sustainability, MDPI, vol. 13(4), pages 1-20, February.
    3. Peter Džupka & Radovan Dráb & Marek Gróf & Tomáš Štofa, 2024. "Exploring Willingness to Pay across Different Passenger Traits," Sustainability, MDPI, vol. 16(2), pages 1-16, January.
    4. Hyunmyung Kim & Jaeheon Choi & Sungjin Cho & Feng Liu & Hyungmin Jin & Suhwan Lim & Dongjun Kim & Jun Lee & Chang-Hyeon Joh, 2022. "Identifying Different Sources of the Benefit: Simulation of DRT Operation in the Heartland and Hinterland Regions," Sustainability, MDPI, vol. 14(24), pages 1-20, December.

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