IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v11y2019i1p189-d194382.html
   My bibliography  Save this article

Applications of Artificial Intelligence in Transport: An Overview

Author

Listed:
  • Rusul Abduljabbar

    (Department of Civil and Construction Engineering; Swinburne University of Technology, Hawthorn, VIC 3122, Australia)

  • Hussein Dia

    (Department of Civil and Construction Engineering; Swinburne University of Technology, Hawthorn, VIC 3122, Australia)

  • Sohani Liyanage

    (Department of Civil and Construction Engineering; Swinburne University of Technology, Hawthorn, VIC 3122, Australia)

  • Saeed Asadi Bagloee

    (Department of Civil and Construction Engineering; Swinburne University of Technology, Hawthorn, VIC 3122, Australia)

Abstract

The rapid pace of developments in Artificial Intelligence (AI) is providing unprecedented opportunities to enhance the performance of different industries and businesses, including the transport sector. The innovations introduced by AI include highly advanced computational methods that mimic the way the human brain works. The application of AI in the transport field is aimed at overcoming the challenges of an increasing travel demand, CO 2 emissions, safety concerns, and environmental degradation. In light of the availability of a huge amount of quantitative and qualitative data and AI in this digital age, addressing these concerns in a more efficient and effective fashion has become more plausible. Examples of AI methods that are finding their way to the transport field include Artificial Neural Networks (ANN), Genetic algorithms (GA), Simulated Annealing (SA), Artificial Immune system (AIS), Ant Colony Optimiser (ACO) and Bee Colony Optimization (BCO) and Fuzzy Logic Model (FLM) The successful application of AI requires a good understanding of the relationships between AI and data on one hand, and transportation system characteristics and variables on the other hand. Moreover, it is promising for transport authorities to determine the way to use these technologies to create a rapid improvement in relieving congestion, making travel time more reliable to their customers and improve the economics and productivity of their vital assets. This paper provides an overview of the AI techniques applied worldwide to address transportation problems mainly in traffic management, traffic safety, public transportation, and urban mobility. The overview concludes by addressing the challenges and limitations of AI applications in transport.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:1:p:189-:d:194382
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/11/1/189/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/11/1/189/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Barabino, Benedetto & Di Francesco, Massimo & Mozzoni, Sara, 2015. "Rethinking bus punctuality by integrating Automatic Vehicle Location data and passenger patterns," Transportation Research Part A: Policy and Practice, Elsevier, vol. 75(C), pages 84-95.
    2. Firnkorn, Jörg & Müller, Martin, 2011. "What will be the environmental effects of new free-floating car-sharing systems? The case of car2go in Ulm," Ecological Economics, Elsevier, vol. 70(8), pages 1519-1528, June.
    3. Ceylan, Halim & Bell, Michael G. H., 2004. "Traffic signal timing optimisation based on genetic algorithm approach, including drivers' routing," Transportation Research Part B: Methodological, Elsevier, vol. 38(4), pages 329-342, May.
    4. Jihui Ma & Cuiying Song & Avishai (Avi) Ceder & Tao Liu & Wei Guan, 2017. "Fairness in optimizing bus-crew scheduling process," PLOS ONE, Public Library of Science, vol. 12(11), pages 1-19, November.
    5. Liu, Tao & Ceder, Avishai (Avi), 2015. "Analysis of a new public-transport-service concept: Customized bus in China," Transport Policy, Elsevier, vol. 39(C), pages 63-76.
    6. Dia, Hussein, 2001. "An object-oriented neural network approach to short-term traffic forecasting," European Journal of Operational Research, Elsevier, vol. 131(2), pages 253-261, June.
    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. 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.
    2. Gong, Manlin & Hu, Yucong & Chen, Zhiwei & Li, Xiaopeng, 2021. "Transfer-based customized modular bus system design with passenger-route assignment optimization," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 153(C).
    3. Zhang, Jie & Wang, David Z.W. & Meng, Meng, 2018. "Which service is better on a linear travel corridor: Park & ride or on-demand public bus?," Transportation Research Part A: Policy and Practice, Elsevier, vol. 118(C), pages 803-818.
    4. Lei, Chao & Ouyang, Yanfeng, 2024. "Average minimum distance to visit a subset of random points in a compact region," Transportation Research Part B: Methodological, Elsevier, vol. 181(C).
    5. Joseph Y. J. Chow & Amelia C. Regan, 2011. "Real Option Pricing of Network Design Investments," Transportation Science, INFORMS, vol. 45(1), pages 50-63, February.
    6. Meng, Zhiyi & Li, Eldon Y. & Qiu, Rui, 2020. "Environmental sustainability with free-floating carsharing services: An on-demand refueling recommendation system for Car2go in Seattle," Technological Forecasting and Social Change, Elsevier, vol. 152(C).
    7. Golalikhani, Masoud & Oliveira, Beatriz Brito & Carravilla, Maria Antónia & Oliveira, José Fernando & Antunes, António Pais, 2021. "Carsharing: A review of academic literature and business practices toward an integrated decision-support framework," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 149(C).
    8. Kaffash, Sepideh & Nguyen, An Truong & Zhu, Joe, 2021. "Big data algorithms and applications in intelligent transportation system: A review and bibliometric analysis," International Journal of Production Economics, Elsevier, vol. 231(C).
    9. Nataša Glišović & Miloš Milenković & Nebojša Bojović & Libor Švadlenka & Zoran Avramović, 2016. "A hybrid model for forecasting the volume of passenger flows on Serbian railways," Operational Research, Springer, vol. 16(2), pages 271-285, July.
    10. Yang Yue & Anthony Gar-On Yeh, 2008. "Spatiotemporal Traffic-Flow Dependency and Short-Term Traffic Forecasting," Environment and Planning B, , vol. 35(5), pages 762-771, October.
    11. Lee, Enoch & Cen, Xuekai & Lo, Hong K., 2022. "Scheduling zonal-based flexible bus service under dynamic stochastic demand and Time-dependent travel time," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 168(C).
    12. Magalhães, David José Ahouagi Vaz de & Rivera-Gonzalez, Carlos, 2021. "Car users’ attitudes towards an enhanced bus system to mitigate urban congestion in a developing country," Transport Policy, Elsevier, vol. 110(C), pages 452-464.
    13. Ozan, Cenk & Haldenbilen, Soner & Ceylan, Halim, 2011. "Estimating emissions on vehicular traffic based on projected energy and transport demand on rural roads: Policies for reducing air pollutant emissions and energy consumption," Energy Policy, Elsevier, vol. 39(5), pages 2542-2549, May.
    14. Philipp Ströhle & Christoph M. Flath & Johannes Gärttner, 2019. "Leveraging Customer Flexibility for Car-Sharing Fleet Optimization," Service Science, INFORMS, vol. 53(1), pages 42-61, February.
    15. Stokkink, Patrick & Geroliminis, Nikolas, 2021. "Predictive user-based relocation through incentives in one-way car-sharing systems," Transportation Research Part B: Methodological, Elsevier, vol. 149(C), pages 230-249.
    16. Aihua Fan & Xumei Chen, 2020. "Exploring the Relationship between Transport Interventions, Mode Choice, and Travel Perception: An Empirical Study in Beijing, China," IJERPH, MDPI, vol. 17(12), pages 1-19, June.
    17. Haldenbilen, Soner, 2006. "Fuel price determination in transportation sector using predicted energy and transport demand," Energy Policy, Elsevier, vol. 34(17), pages 3078-3086, November.
    18. A. Assaf, 2011. "Accounting for technological differences in modelling the performance of airports: a Bayesian approach," Applied Economics, Taylor & Francis Journals, vol. 43(18), pages 2267-2275.
    19. Yan Zhou & Sangmoon Park, 2020. "The Regional Determinants of the New Venture Formation in China’s Car-Sharing Economy," Sustainability, MDPI, vol. 13(1), pages 1-22, December.
    20. Guan, Yunlin & Xiang, Wang & Wang, Yun & Yan, Xuedong & Zhao, Yi, 2023. "Bi-level optimization for customized bus routing serving passengers with multiple-trips based on state–space–time network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 614(C).

    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:gam:jsusta:v:11:y:2019:i:1:p:189-:d:194382. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.