IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i4p2473-d754827.html
   My bibliography  Save this article

Commuter Bus Operation Rules under Two Traffic Scenarios and Two Weather Conditions: Naturalistic Driving Study on Vehicle Speed and Clearance

Author

Listed:
  • Qiuju Huang

    (School of Transportation Science and Engineering, Harbin Institute of Technology, Harbin 150001, China
    School of Automobile, Harbin Vocation and Technical College, Harbin 150081, China)

  • Shumin Feng

    (School of Transportation Science and Engineering, Harbin Institute of Technology, Harbin 150001, China)

  • Guosheng Zhang

    (Research Institute of Highway, Ministry of Transport, Beijing 100088, China)

  • Yu Zhang

    (School of Automobile, Harbin Vocation and Technical College, Harbin 150081, China)

  • Yong Adilah Shamsul Harumain

    (Center of Transportation, University Malaya, Kuala Lumpur 50603, Malaysia)

Abstract

This study provides insights into the building of environmentally and socially sustainable and livable cities by investigating commuter buses’ vehicle speed and clearance, considering two traffic scenarios (working days and weekends) and two weather conditions on urban roads. Thirty participants drove ten different routes during natural driving. The drivers were observed under the following traffic conditions: free flow (Grade I), steady flow (Grades II and III), unsteady flow (Grade IV), working days and weekends, and sunny and heavy snow weather. A method for collecting accurate traffic flow data was developed using a radar sensor to measure the real-time distance between vehicles. The real-time vehicle spacing and speed were detected using a radar sensor and a mobile app, respectively. The results showed that speed decreased obviously from 11.2% to 16.5% on working days compared to a similar situation on weekends, especially in heavy snow weather (from 33.8% to 40.7%). The lowest average speed was obtained in the traffic environment Grade IV. Commuter buses maintained a minimum vehicle clearance during working or heavy snow days in traffic environment Grade IV. Policymakers should consider the insights of this study to develop new, dynamic commuter schedules under diverse conditions.

Suggested Citation

  • Qiuju Huang & Shumin Feng & Guosheng Zhang & Yu Zhang & Yong Adilah Shamsul Harumain, 2022. "Commuter Bus Operation Rules under Two Traffic Scenarios and Two Weather Conditions: Naturalistic Driving Study on Vehicle Speed and Clearance," Sustainability, MDPI, vol. 14(4), pages 1-15, February.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:4:p:2473-:d:754827
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/4/2473/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/4/2473/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zhenbao Wang & Sicheng Wang & Haitao Lian, 2021. "A route-planning method for long-distance commuter express bus service based on OD estimation from mobile phone location data: the case of the Changping Corridor in Beijing," Public Transport, Springer, vol. 13(1), pages 101-125, March.
    2. Jing Li & Yongbo Lv & Jihui Ma & Yuan Ren, 2019. "Factor Analysis of Customized Bus Attraction to Commuters with Different Travel Modes," Sustainability, MDPI, vol. 11(24), pages 1-13, December.
    3. Hongguo Ren & Zhenbao Wang & Yanyan Chen, 2020. "Optimal Express Bus Routes Design with Limited-Stop Services for Long-Distance Commuters," Sustainability, MDPI, vol. 12(4), pages 1-14, February.
    4. Shin, Eun Jin, 2020. "Commuter benefits programs: Impacts on mode choice, VMT, and spillover effects," Transport Policy, Elsevier, vol. 94(C), pages 11-22.
    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. Xiaoquan Wang & Weifeng Wang & Chaoying Yin, 2023. "Exploring the Relationships between Multilevel Built Environments and Commute Durations in Dual-Earner Households: Does Gender Matter?," IJERPH, MDPI, vol. 20(6), pages 1-17, March.
    2. Xing, Jiping & Wu, Wei & Cheng, Qixiu & Liu, Ronghui, 2022. "Traffic state estimation of urban road networks by multi-source data fusion: Review and new insights," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 595(C).
    3. Chen, Peng & Yang, Xiankui, 2023. "Revisit employer-based travel demand management: A longitudinal analysis," Transport Policy, Elsevier, vol. 131(C), pages 22-31.
    4. Alejandro Sánchez-Atondo & Leonel García & Julio Calderón-Ramírez & José Manuel Gutiérrez-Moreno & Alejandro Mungaray-Moctezuma, 2020. "Understanding Public Transport Ridership in Developing Countries to Promote Sustainable Urban Mobility: A Case Study of Mexicali, Mexico," Sustainability, MDPI, vol. 12(8), pages 1-21, April.
    5. Shin, Eun Jin, 2021. "Exploring the causal impact of transit fare exemptions on older adults’ travel behavior: Evidence from the Seoul metropolitan area," Transportation Research Part A: Policy and Practice, Elsevier, vol. 149(C), pages 319-338.
    6. Ivan Pechkurov & Dmitry Plotnikov & Andrey Gorev & Tatiana Kudryavtseva & Aushra Banite & Angi Skhvediani, 2023. "Development of a Method for Selecting Bus Rapid Transit Corridors Based on the Economically Viable Passenger Flow Criterion," Sustainability, MDPI, vol. 15(3), pages 1-17, January.
    7. Yong Wang & Qin Li & Xiangyang Guan & Jianxin Fan & Yong Liu & Haizhong Wang, 2020. "Collaboration and Resource Sharing in the Multidepot Multiperiod Vehicle Routing Problem with Pickups and Deliveries," Sustainability, MDPI, vol. 12(15), pages 1-33, July.
    8. Itani, Ibrahim & Cassidy, Michael J. & Daganzo, Carlos, 2021. "Synergies of combining demand- and supply-side measures to manage congested streets," Transportation Research Part A: Policy and Practice, Elsevier, vol. 151(C), pages 172-179.
    9. Min-ho Suh & Minjoong Jeong, 2022. "Development of Bus Routes Reorganization Support Software Using the Naïve Bayes Classification Method," Sustainability, MDPI, vol. 14(8), pages 1-19, April.

    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:14:y:2022:i:4:p:2473-:d:754827. 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.