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Demand-Responsive Transport for Urban Mobility: Integrating Mobile Data Analytics to Enhance Public Transportation Systems

Author

Listed:
  • Sandra Melo

    (CEiiA, Center of Engineering and Development, Av. D. Afonso Henriques, 4450-017 Matosinhos, Portugal)

  • Rui Gomes

    (Universidade de Aveiro, Campus Universitário de Santiago, 3810-193 Aveiro, Portugal)

  • Reza Abbasi

    (CEiiA, Center of Engineering and Development, Av. D. Afonso Henriques, 4450-017 Matosinhos, Portugal)

  • Amílcar Arantes

    (CERIS, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais 1, 1049-001 Lisboa, Portugal)

Abstract

Transport-on-demand services, such as demand-responsive transport (DRT), involve a flexible transportation service that offers convenient and personalised mobility choices for public transport users. Integrating DRT with mobile data and data analytics enhances understanding of travel patterns and allows the development of improved algorithms to support design-optimised services. This study introduces a replicable framework for DRT that employs an on-demand transport simulator and routing algorithm. This framework is supported by a mobile data set, enabling a more accurate service design grounded on actual demand data. Decision-makers can use this framework to understand traffic patterns better and test a DRT solution before implementing it in the actual world. A case study was conducted in Porto, Portugal, to demonstrate its practicality and proof of concept. Results show that the DRT solution required 135% fewer stops and travelled 81% fewer kilometres than the existing fixed-line service. Findings highlight the potential of this data-driven framework for urban public transportation systems to improve key performance metrics in required buses, energy consumption, travelled distance, and stop frequency, all while maintaining the number of served passengers. Under specific circumstances, embracing this approach can offer a more efficient, user-centric, and environmentally sustainable urban transportation service.

Suggested Citation

  • Sandra Melo & Rui Gomes & Reza Abbasi & Amílcar Arantes, 2024. "Demand-Responsive Transport for Urban Mobility: Integrating Mobile Data Analytics to Enhance Public Transportation Systems," Sustainability, MDPI, vol. 16(11), pages 1-18, May.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:11:p:4367-:d:1399274
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    References listed on IDEAS

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    1. Filippo Simini & Gianni Barlacchi & Massimilano Luca & Luca Pappalardo, 2021. "A Deep Gravity model for mobility flows generation," Nature Communications, Nature, vol. 12(1), pages 1-13, December.
    2. Yu, Biying & Ma, Ye & Xue, Meimei & Tang, Baojun & Wang, Bin & Yan, Jinyue & Wei, Yi-Ming, 2017. "Environmental benefits from ridesharing: A case of Beijing," Applied Energy, Elsevier, vol. 191(C), pages 141-152.
    3. Parisa Ahani & Amílcar Arantes & Rohollah Garmanjani & Sandra Melo, 2023. "Optimizing Vehicle Replacement in Sustainable Urban Freight Transportation Subject to Presence of Regulatory Measures," Sustainability, MDPI, vol. 15(16), pages 1-18, August.
    4. Daganzo, Carlos F. & Ouyang, Yanfeng, 2019. "A general model of demand-responsive transportation services: From taxi to ridesharing to dial-a-ride," Transportation Research Part B: Methodological, Elsevier, vol. 126(C), pages 213-224.
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    Cited by:

    1. Giovanni Calabrò, 2025. "A New Agent-Based Model to Simulate Demand-Responsive Transit in Small-Sized Cities," Sustainability, MDPI, vol. 17(12), pages 1-20, June.

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