IDEAS home Printed from https://ideas.repec.org/a/spr/pubtra/v16y2024i1d10.1007_s12469-023-00334-7.html
   My bibliography  Save this article

Artificial intelligence for improving public transport: a mapping study

Author

Listed:
  • Å. Jevinger

    (Malmö University)

  • C. Zhao
  • J. A. Persson

    (Malmö University)

  • P. Davidsson

    (Malmö University)

Abstract

The objective of this study is to provide a better understanding of the potential of using Artificial Intelligence (AI) to improve Public Transport (PT), by reviewing research literature. The selection process resulted in 87 scientific publications constituting a sample of how AI has been applied to improve PT. The review shows that the primary aims of using AI are to improve the service quality or to better understand traveller behaviour. Train and bus are the dominant modes of transport investigated. Furthermore, AI is mainly used for three tasks; the most frequent one is prediction, followed by an estimation of the current state, and resource allocation, including planning and scheduling. Only two studies concern automation; all the others provide different kinds of decision support for travellers, PT operators, PT planners, or municipalities. Most of the reviewed AI solutions require significant amounts of data related to the travellers and the PT system. Machine learning is the most frequently used AI technology, with some studies applying reasoning or heuristic search techniques. We conclude that there still remains a great potential of using AI to improve PT waiting to be explored, but that there are also some challenges that need to be considered. They are often related to data, e.g., that large datasets of high quality are needed, that substantial resources and time are needed to pre-process the data, or that the data compromise personal privacy. Further research is needed about how to handle these issues efficiently.

Suggested Citation

  • Å. Jevinger & C. Zhao & J. A. Persson & P. Davidsson, 2024. "Artificial intelligence for improving public transport: a mapping study," Public Transport, Springer, vol. 16(1), pages 99-158, March.
  • Handle: RePEc:spr:pubtra:v:16:y:2024:i:1:d:10.1007_s12469-023-00334-7
    DOI: 10.1007/s12469-023-00334-7
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s12469-023-00334-7
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s12469-023-00334-7?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Sebastián M. Palacio, "undated". "Machine Learning Forecasts of Public Transport Demand: A comparative analysis of supervised algorithms using smart card data," Working Papers XREAP2018-3, Xarxa de Referència en Economia Aplicada (XREAP).
    2. Rusul Abduljabbar & Hussein Dia & Sohani Liyanage & Saeed Asadi Bagloee, 2019. "Applications of Artificial Intelligence in Transport: An Overview," Sustainability, MDPI, vol. 11(1), pages 1-24, January.
    3. Zhou, Xiaolu & Wang, Mingshu & Li, Dongying, 2019. "Bike-sharing or taxi? Modeling the choices of travel mode in Chicago using machine learning," Journal of Transport Geography, Elsevier, vol. 79(C), pages 1-1.
    4. Anil NP Koushik & M. Manoj & N. Nezamuddin, 2020. "Machine learning applications in activity-travel behaviour research: a review," Transport Reviews, Taylor & Francis Journals, vol. 40(3), pages 288-311, May.
    5. Nachtigall, K., 1995. "Time depending shortest-path problems with applications to railway networks," European Journal of Operational Research, Elsevier, vol. 83(1), pages 154-166, May.
    6. Liping Ge & Malek Sarhani & Stefan Voß & Lin Xie, 2021. "Review of Transit Data Sources: Potentials, Challenges and Complementarity," Sustainability, MDPI, vol. 13(20), pages 1-37, October.
    7. Shefang Wang & Chaoru Lu & Chenhui Liu & Yue Zhou & Jun Bi & Xiaomei Zhao, 2020. "Understanding the Energy Consumption of Battery Electric Buses in Urban Public Transport Systems," Sustainability, MDPI, vol. 12(23), pages 1-12, November.
    8. Paul Davidsson & Banafsheh Hajinasab & Johan Holmgren & Åse Jevinger & Jan A. Persson, 2016. "The Fourth Wave of Digitalization and Public Transport: Opportunities and Challenges," Sustainability, MDPI, vol. 8(12), pages 1-16, November.
    9. Sohani Liyanage & Hussein Dia & Rusul Abduljabbar & Saeed Asadi Bagloee, 2019. "Flexible Mobility On-Demand: An Environmental Scan," Sustainability, MDPI, vol. 11(5), pages 1-39, February.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zhang, Ning & Wu, Yiping & Rong, Jian & Shao, Juan & Chen, Jiayuan & Zhou, Chenjing, 2023. "Analysis of truckers’ intentions in choosing freeways or parallel national and provincial roads," Research in Transportation Economics, Elsevier, vol. 101(C).
    2. Phattarasuda Witchayaphong & Surachet Pravinvongvuth & Kunnawee Kanitpong & Kazushi Sano & Suksun Horpibulsuk, 2020. "Influential Factors Affecting Travelers’ Mode Choice Behavior on Mass Transit in Bangkok, Thailand," Sustainability, MDPI, vol. 12(22), pages 1-18, November.
    3. Yousefzadeh Barri, Elnaz & Farber, Steven & Jahanshahi, Hadi & Beyazit, Eda, 2022. "Understanding transit ridership in an equity context through a comparison of statistical and machine learning algorithms," Journal of Transport Geography, Elsevier, vol. 105(C).
    4. Matthew Callcut & Jean-Paul Cerceau Agliozzo & Liz Varga & Lauren McMillan, 2021. "Digital Twins in Civil Infrastructure Systems," Sustainability, MDPI, vol. 13(20), pages 1-32, October.
    5. Tianxing Dai & Brian D. Taylor, 2023. "Three’s a crowd? Examining evolving public transit crowding standards amidst the COVID-19 pandemic," Public Transport, Springer, vol. 15(2), pages 321-341, June.
    6. Wang, Kaili & Gao, Ya & Nurul Habib, Khandker, 2024. "Modelling household online shopping and home delivery demand using latent class & ordinal generalized extreme value (GEV) models," Journal of choice modelling, Elsevier, vol. 53(C).
    7. Karol Tucki & Małgorzata Krzywonos & Olga Orynycz & Adam Kupczyk & Anna Bączyk & Izabela Wielewska, 2021. "Analysis of the Possibility of Fulfilling the Paris Agreement by the Visegrad Group Countries," Sustainability, MDPI, vol. 13(16), pages 1-21, August.
    8. Merkebe Getachew Demissie & Lina Kattan, 2022. "Understanding the temporal and spatial interactions between transit ridership and urban land-use patterns: an exploratory study," Public Transport, Springer, vol. 14(2), pages 385-417, June.
    9. Iván López & Pedro Luis Calvo & Gonzalo Fernández-Sánchez & Carlos Sierra & Roberto Corchero & Cesar Omar Chacón & Carlos de Juan & Daniel Rosas & Francisco Burgos, 2022. "Different Approaches for a Goal: The Electrical Bus-EMT Madrid as a Successful Case Study," Energies, MDPI, vol. 15(17), pages 1-24, August.
    10. Serap Turkyilmaz & Erkut Altindað, 2022. "Analysis of Smart Home Systems in the Context of the Internet of Things in Terms of Consumer Experience," International Review of Management and Marketing, Econjournals, vol. 12(1), pages 19-31.
    11. Elżbieta Szymańska & Eugenia Panfiluk & Halina Kiryluk, 2021. "Innovative Solutions for the Development of Sustainable Transport and Improvement of the Tourist Accessibility of Peripheral Areas: The Case of the Białowieża Forest Region," Sustainability, MDPI, vol. 13(4), pages 1-23, February.
    12. Ghasri, Milad & Ardeshiri, Ali & Zhang, Xiang & Waller, S. Travis, 2024. "Analysing preferences for integrated micromobility and public transport systems: A hierarchical latent class approach considering taste heterogeneity and attribute non-attendance," Transportation Research Part A: Policy and Practice, Elsevier, vol. 181(C).
    13. Sybille Bauriedl & Anke Strüver, 2020. "Platform Urbanism: Technocapitalist Production of Private and Public Spaces," Urban Planning, Cogitatio Press, vol. 5(4), pages 267-276.
    14. Marya Butt & Ander de Keijzer, 2022. "Using Transfer Learning to Train a Binary Classifier for Lorrca Ektacytometery Microscopic Images of Sickle Cells and Healthy Red Blood Cells," Data, MDPI, vol. 7(9), pages 1-21, September.
    15. Yang, Hongtai & Luo, Peng & Li, Chaojing & Zhai, Guocong & Yeh, Anthony G.O., 2023. "Nonlinear effects of fare discounts and built environment on ridesplitting adoption rates," Transportation Research Part A: Policy and Practice, Elsevier, vol. 169(C).
    16. Volodymyr N. Skoropad & Stevica Deđanski & Vladan Pantović & Zoran Injac & Slađana Vujičić & Marina Jovanović-Milenković & Boris Jevtić & Violeta Lukić-Vujadinović & Dejan Vidojević & Ištvan Bodolo, 2025. "Dynamic Traffic Flow Optimization Using Reinforcement Learning and Predictive Analytics: A Sustainable Approach to Improving Urban Mobility in the City of Belgrade," Sustainability, MDPI, vol. 17(8), pages 1-31, April.
    17. Sohani Liyanage & Hussein Dia & Rusul Abduljabbar & Saeed Asadi Bagloee, 2019. "Flexible Mobility On-Demand: An Environmental Scan," Sustainability, MDPI, vol. 11(5), pages 1-39, February.
    18. Ichoua, Soumia & Gendreau, Michel & Potvin, Jean-Yves, 2003. "Vehicle dispatching with time-dependent travel times," European Journal of Operational Research, Elsevier, vol. 144(2), pages 379-396, January.
    19. Lin, Boqiang & Huang, Chenchen, 2023. "How will promoting the digital economy affect electricity intensity?," Energy Policy, Elsevier, vol. 173(C).
    20. Lisa Dang & Widar von Arx & Jonas Frölicher, 2021. "The Impact of On-Demand Collective Transport Services on Sustainability: A Comparison of Various Service Options in a Rural and an Urban Area of Switzerland," Sustainability, MDPI, vol. 13(6), pages 1-27, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:pubtra:v:16:y:2024:i:1:d:10.1007_s12469-023-00334-7. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.