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Dynamic Carpooling in Urban Areas: Design and Experimentation with a Multi-Objective Route Matching Algorith

Author

Listed:
  • Matteo Mallus

    (Dipartimento di Ingegneria Elettrica ed Elettronica (DIEE), University of Cagliari, 09123 Cagliari, Italy
    GreenShare SRL, 09128 Cagliari, Italy)

  • Giuseppe Colistra

    (Dipartimento di Ingegneria Elettrica ed Elettronica (DIEE), University of Cagliari, 09123 Cagliari, Italy
    GreenShare SRL, 09128 Cagliari, Italy)

  • Luigi Atzori

    (Dipartimento di Ingegneria Elettrica ed Elettronica (DIEE), University of Cagliari, 09123 Cagliari, Italy
    GreenShare SRL, 09128 Cagliari, Italy
    Consorzio Nazionale Interuniversitario per le Telecomunicazioni (CNIT), Unità di Ricerca di Cagliari, 09123 Cagliari, Italy)

  • Maurizio Murroni

    (Dipartimento di Ingegneria Elettrica ed Elettronica (DIEE), University of Cagliari, 09123 Cagliari, Italy)

  • Virginia Pilloni

    (Dipartimento di Ingegneria Elettrica ed Elettronica (DIEE), University of Cagliari, 09123 Cagliari, Italy)

Abstract

This paper focuses on dynamic carpooling services in urban areas to address the needs of mobility in real-time by proposing a two-fold contribution: a solution with novel features with respect to the current state-of-the-art, which is named CLACSOON and is available on the market; the analysis of the carpooling services performance in the urban area of the city of Cagliari through emulations. Two new features characterize the proposed solution: partial ridesharing, according to which the riders can walk to reach the driver along his/her route when driving to the destination; the possibility to share the ride when the driver has already started the ride by modelling the mobility to reach the driver destination. To analyse which features of the population bring better performance to changing the characteristics of the users, we also conducted emulations. When compared with current solutions, CLACSOON allows for achieving a decrease in the waiting time of around 55% and an increase in the driver and passenger success rates of around 4% and 10%,respectively. Additionally, the proposed features allowed for having an increase in the reduction of the CO2 emission by more than 10% with respect to the traditional carpooling service.

Suggested Citation

  • Matteo Mallus & Giuseppe Colistra & Luigi Atzori & Maurizio Murroni & Virginia Pilloni, 2017. "Dynamic Carpooling in Urban Areas: Design and Experimentation with a Multi-Objective Route Matching Algorith," Sustainability, MDPI, vol. 9(2), pages 1-21, February.
    • Handle: RePEc:gam:jsusta:v:9:y:2017:i:2:p:254-:d:89980
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    References listed on IDEAS

    as
    1. Agatz, N.A.H. & Erera, A. & Savelsbergh, M.W.P. & Wang, X., 2010. "The Value of Optimization in Dynamic Ride-Sharing: a Simulation Study in Metro Atlanta," ERIM Report Series Research in Management ERS-2010-034-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
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    4. Agatz, Niels & Erera, Alan & Savelsbergh, Martin & Wang, Xing, 2012. "Optimization for dynamic ride-sharing: A review," European Journal of Operational Research, Elsevier, vol. 223(2), pages 295-303.
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    Citations

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    Cited by:

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    2. Anne Aguiléra & Eléonore Pigalle, 2021. "The Future and Sustainability of Carpooling Practices. An Identification of Research Challenges," Sustainability, MDPI, vol. 13(21), pages 1-16, October.
    3. Romanika Okraszewska & Aleksandra Romanowska & Marcin Wołek & Jacek Oskarbski & Krystian Birr & Kazimierz Jamroz, 2018. "Integration of a Multilevel Transport System Model into Sustainable Urban Mobility Planning," Sustainability, MDPI, vol. 10(2), pages 1-20, February.
    4. Georgina Santos, 2018. "Sustainability and Shared Mobility Models," Sustainability, MDPI, vol. 10(9), pages 1-13, September.
    5. Jacek Oskarbski & Krystian Birr & Karol Żarski, 2021. "Bicycle Traffic Model for Sustainable Urban Mobility Planning," Energies, MDPI, vol. 14(18), pages 1-36, September.
    6. Qin Yang & Jinfeng Liu & Xing Liu & Cejun Cao & Wei Zhang, 2019. "A Two-Sided Matching Model for Task Distribution in Ridesharing: A Sustainable Operations Perspective," Sustainability, MDPI, vol. 11(7), pages 1-16, April.
    7. Virginia Pilloni, 2018. "How Data Will Transform Industrial Processes: Crowdsensing, Crowdsourcing and Big Data as Pillars of Industry 4.0," Future Internet, MDPI, vol. 10(3), pages 1-14, March.
    8. Guijun Li & Yongsheng Wang & Jie Luo & Yulong Li, 2018. "Evaluation on Construction Level of Smart City: An Empirical Study from Twenty Chinese Cities," Sustainability, MDPI, vol. 10(9), pages 1-18, September.

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