IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v672y2025ics0378437125003541.html

Adaptive congestion index-based A* algorithm for dynamic vehicle path planning optimization

Author

Listed:
  • Du, Yulun
  • Liu, Gang
  • Jiang, Yunhao
  • Cai, Siteng
  • He, Jing

Abstract

To enhance traffic efficiency of urban transportation networks, it is essential to comprehensively consider factors such as the congestion index and road network topology. The congestion index serves as a core metric for objectively evaluating urban traffic conditions, primarily used to assess the traffic performance of transportation networks. This study introduces the principle of probability density segmentation to address the limitations of existing congestion index models, which inadequately account for the spatiotemporal characteristics of traffic flow speed. By segmenting vehicle speed based on distance and time, a congestion index model with adaptive adjustment capabilities is established. Based on this, an improved A* algorithm (MVPP-ACI-IA) is proposed based on dynamic multi-objective path-planning mechanism and adaptive congestion index. Results demonstrate that, compared to the traditional A* algorithm, the proposed method dynamically adjusts vehicle routes, improving traffic efficiency by approximately 10.95 %. Our approach significantly mitigates road congestion under high traffic load scenarios.

Suggested Citation

  • Du, Yulun & Liu, Gang & Jiang, Yunhao & Cai, Siteng & He, Jing, 2025. "Adaptive congestion index-based A* algorithm for dynamic vehicle path planning optimization," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 672(C).
    • Handle: RePEc:eee:phsmap:v:672:y:2025:i:c:s0378437125003541
    DOI: 10.1016/j.physa.2025.130702
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437125003541
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2025.130702?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

    for a different version of it.

    References listed on IDEAS

    as
    1. Mondschein, Andrew & Taylor, Brian D., 2017. "Is traffic congestion overrated? Examining the highly variable effects of congestion on travel and accessibility," Journal of Transport Geography, Elsevier, vol. 64(C), pages 65-76.
    2. Tiezheng Guo & Jie Wang & Zhiming Wang & Wei Chen & Guojun Chen & Shishi Zhang & Amandeep Kaur, 2022. "Research on Path Planning of Mobile Robot with a Novel Improved Artificial Potential Field Algorithm," Mathematical Problems in Engineering, Hindawi, vol. 2022, pages 1-13, September.
    3. Tanvi Maheshwari & Kay W. Axhausen, 2021. "How Will the Technological Shift in Transportation Impact Cities? A Review of Quantitative Studies on the Impacts of New Transportation Technologies," Sustainability, MDPI, vol. 13(6), pages 1-21, March.
    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. Mohammad A. R. Abdeen & Ansar Yasar & Mohamed Benaida & Tarek Sheltami & Dimitrios Zavantis & Youssef El-Hansali, 2022. "Evaluating the Impacts of Autonomous Vehicles’ Market Penetration on a Complex Urban Freeway during Autonomous Vehicles’ Transition Period," Sustainability, MDPI, vol. 14(16), pages 1-12, August.
    2. Pot, Felix Johan & Koster, Sierdjan & Tillema, Taede, 2023. "Perceived accessibility and residential self-selection in the Netherlands," Journal of Transport Geography, Elsevier, vol. 108(C).
    3. Zuo, Ting & Wei, Heng & Liu, Hao & Yang, Y. Jeffrey, 2019. "Bi-level optimization approach for configuring population and employment distributions with minimized vehicle travel demand," Journal of Transport Geography, Elsevier, vol. 74(C), pages 161-172.
    4. Niaz Mahmud Zafri & Sadia Afroj & Mohammad Ashraf Ali & Md Musleh Uddin Hasan & Md Hamidur Rahman, 2021. "Effectiveness of containment strategies and local cognition to control vehicular traffic volume in Dhaka, Bangladesh during COVID-19 pandemic: Use of Google Map based real-time traffic data," PLOS ONE, Public Library of Science, vol. 16(5), pages 1-16, May.
    5. Pennetti, Cody A. & Fontaine, Michael D. & Jun, Jungwook & Lambert, James H., 2020. "Evaluating capacity of transportation operations with highway travel time reliability," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    6. Konstantina Anastasiadou, 2021. "Sustainable Mobility Driven Prioritization of New Vehicle Technologies, Based on a New Decision-Aiding Methodology," Sustainability, MDPI, vol. 13(9), pages 1-27, April.
    7. Thomas, Trevor & Mondschein, Andrew & Osman, Taner & Taylor, Brian D., 2018. "Not so fast? Examining neighborhood-level effects of traffic congestion on job access," Transportation Research Part A: Policy and Practice, Elsevier, vol. 113(C), pages 529-541.
    8. Yan, Xiang & Bejleri, Ilir & Zhai, Liang, 2022. "A spatiotemporal analysis of transit accessibility to low-wage jobs in Miami-Dade County," Journal of Transport Geography, Elsevier, vol. 98(C).
    9. Maximilian Braun & Jan Kunkler & Florian Kellner, 2020. "Towards Sustainable Cities: Utilizing Floating Car Data to Support Location-Based Road Network Performance Measurements," Sustainability, MDPI, vol. 12(19), pages 1-22, October.
    10. Laura McCarthy & Alexa Delbosc & Graham Currie & Andrew Molloy, 2020. "Parenthood and cars: A weakening relationship?," Transportation, Springer, vol. 47(3), pages 1127-1145, June.
    11. Darius Bazaras & Margarita Išoraitė & Kristina Vaičiūtė, 2022. "A Study of the Relationship between Marketing and Investment in Technology Development in Transport Company," Sustainability, MDPI, vol. 14(19), pages 1-17, October.
    12. Amparo Moyano & Marcin Stępniak & Borja Moya-Gómez & Juan Carlos García-Palomares, 2021. "Traffic congestion and economic context: changes of spatiotemporal patterns of traffic travel times during crisis and post-crisis periods," Transportation, Springer, vol. 48(6), pages 3301-3324, December.
    13. Beibei Hu & Yue Sun & Huijun Sun & Xianlei Dong, 2020. "A Contrastive Study on Travel Costs of Car-Sharing and Taxis Based on GPS Trajectory Data," IJERPH, MDPI, vol. 17(24), pages 1-28, December.
    14. Tobias Biehle, 2022. "Social Sustainable Urban Air Mobility in Europe," Sustainability, MDPI, vol. 14(15), pages 1-17, July.
    15. Kan, Zihan & Kwan, Mei-Po & Liu, Dong & Tang, Luliang & Chen, Yang & Fang, Mengyuan, 2022. "Assessing individual activity-related exposures to traffic congestion using GPS trajectory data," Journal of Transport Geography, Elsevier, vol. 98(C).
    16. Ben-Elia, Eran & Benenson, Itzhak, 2019. "A spatially-explicit method for analyzing the equity of transit commuters' accessibility," Transportation Research Part A: Policy and Practice, Elsevier, vol. 120(C), pages 31-42.
    17. Santiago Sánchez González & Felipe Bedoya-Maya & Agustina Calatayud, 2021. "Understanding the Effect of Traffic Congestion on Accidents Using Big Data," Sustainability, MDPI, vol. 13(13), pages 1-19, July.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:eee:phsmap:v:672:y:2025:i:c:s0378437125003541. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    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.