IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i11p8763-d1158675.html
   My bibliography  Save this article

Sustainable Road Planning for Trucks in Urbanized Areas of Chinese Cities Using Deep Learning Approaches

Author

Listed:
  • Haopeng Wang

    (School of Economics and Management, China University of Petroleum (East China), Qingdao 266580, China)

  • Zhenzhi Zhao

    (School of Economics and Management, China University of Petroleum (East China), Qingdao 266580, China)

  • Yingying Ma

    (School of Management Engineering, Qingdao University of Technology, Qingdao 266520, China)

  • Hao Wu

    (School of Economics and Management, Zhejiang University of Science and Technology, Hangzhou 310023, China)

  • Fei Bao

    (School of Economics and Management, China University of Petroleum (East China), Qingdao 266580, China)

Abstract

Nowadays, urban areas are experiencing heavy traffic, and governments are implementing various policies to manage it. For example, in China, trucks are prohibited from entering urban areas during the daytime to reduce traffic congestion. However, we have found that this policy is not cost-efficient for logistics, which includes gas fees, air pollution fees, and wear and tear expenses, as it cannot adjust to real-time traffic conditions. To minimize logistics costs in real-time, we propose DeepPlan, a deep-learning-based model that optimizes urban planning. Our model calculates the optimal route for each truck based on real-time traffic data in urban areas. We learned the optimal route from the trace data of taxi drivers who are experienced in minimizing logistics costs. Our experimental results show that DeepPlan outperforms existing urban plans by 25% and works well in various circumstances, including different weather and unexpected events.

Suggested Citation

  • Haopeng Wang & Zhenzhi Zhao & Yingying Ma & Hao Wu & Fei Bao, 2023. "Sustainable Road Planning for Trucks in Urbanized Areas of Chinese Cities Using Deep Learning Approaches," Sustainability, MDPI, vol. 15(11), pages 1-19, May.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:11:p:8763-:d:1158675
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/11/8763/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/11/8763/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jing Xue & Jina Cui, 2022. "A cooperative game model of supply chain logistics information based on collaborative immune quantum particle swarm optimisation," International Journal of Manufacturing Technology and Management, Inderscience Enterprises Ltd, vol. 36(2/3/4), pages 196-212.
    2. Sim Jia Jin & Abdul Halim Abdullah & Mahani Mokhtar & Umar Haiyat Abdul Kohar, 2022. "The Potential of Big Data Application in Mathematics Education in Malaysia," Sustainability, MDPI, vol. 14(21), pages 1-23, October.
    3. Tommi Inkinen & Esa Hämäläinen, 2020. "Reviewing Truck Logistics: Solutions for Achieving Low Emission Road Freight Transport," Sustainability, MDPI, vol. 12(17), pages 1-11, August.
    4. Jenelius, Erik & Petersen, Tom & Mattsson, Lars-Göran, 2006. "Importance and exposure in road network vulnerability analysis," Transportation Research Part A: Policy and Practice, Elsevier, vol. 40(7), pages 537-560, August.
    5. Chen, Xiaodong & Wu, Ge & Li, Ding, 2019. "Efficiency measure on the truck restriction policy in China: A non-radial data envelopment model," Transportation Research Part A: Policy and Practice, Elsevier, vol. 129(C), pages 140-154.
    6. Haijun Mao & Long Chen & Zhihan Lv, 2021. "E-Commerce Enterprise Supply Chain Cost Control under the Background of Big Data," Complexity, Hindawi, vol. 2021, pages 1-11, February.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Mengmeng Liu & Hao Wu & Haopeng Wang, 2023. "Will Trade Protection Trigger a Surge in Investment-Related CO 2 Emissions? Evidence from Multi-Regional Input–Output Model," Sustainability, MDPI, vol. 15(13), pages 1-21, June.

    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, Nan & Huang, Hong & Su, Boni & Zhao, Jinlong, 2015. "Analysis of dynamic road risk for pedestrian evacuation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 430(C), pages 171-183.
    2. Richard Connors & David Watling, 2015. "Assessing the Demand Vulnerability of Equilibrium Traffic Networks via Network Aggregation," Networks and Spatial Economics, Springer, vol. 15(2), pages 367-395, June.
    3. Filip Škultéty & Dominika Beňová & Jozef Gnap, 2021. "City Logistics as an Imperative Smart City Mechanism: Scrutiny of Clustered EU27 Capitals," Sustainability, MDPI, vol. 13(7), pages 1-16, March.
    4. Cats, Oded & Jenelius, Erik, 2015. "Planning for the unexpected: The value of reserve capacity for public transport network robustness," Transportation Research Part A: Policy and Practice, Elsevier, vol. 81(C), pages 47-61.
    5. Mengying Cui & David Levinson, 2018. "Accessibility analysis of risk severity," Transportation, Springer, vol. 45(4), pages 1029-1050, July.
    6. Dowds, Jonathan & Aultman-Hall, Lisa, 2015. "Challenges and Opportunities for Integrating Climate Adaptation Efforts across State, Regional and Local Transportation Agencies," Institute of Transportation Studies, Working Paper Series qt5t88h66m, Institute of Transportation Studies, UC Davis.
    7. Bucar, Raif C.B. & Hayeri, Yeganeh M., 2020. "Quantitative assessment of the impacts of disruptive precipitation on surface transportation," Reliability Engineering and System Safety, Elsevier, vol. 203(C).
    8. Peter Mako & Andrej Dávid & Patrik Böhm & Sorin Savu, 2021. "Sustainable Transport in the Danube Region," Sustainability, MDPI, vol. 13(12), pages 1-21, June.
    9. Jenelius, Erik, 2010. "User inequity implications of road network vulnerability," The Journal of Transport and Land Use, Center for Transportation Studies, University of Minnesota, vol. 2(3), pages 57-73.
    10. Mohamad Darayi & Kash Barker & Joost R. Santos, 2017. "Component Importance Measures for Multi-Industry Vulnerability of a Freight Transportation Network," Networks and Spatial Economics, Springer, vol. 17(4), pages 1111-1136, December.
    11. Jenelius, Erik & Mattsson, Lars-Göran, 2012. "Road network vulnerability analysis of area-covering disruptions: A grid-based approach with case study," Transportation Research Part A: Policy and Practice, Elsevier, vol. 46(5), pages 746-760.
    12. Xiaodong Chen & Anda Guo & Jiahao Zhu & Fang Wang & Yanqiu He, 2022. "Accessing performance of transport sector considering risks of climate change and traffic accidents: joint bounded-adjusted measure and Luenberger decomposition," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 111(1), pages 115-138, March.
    13. Almoghathawi, Yasser & Barker, Kash & Rocco, Claudio M. & Nicholson, Charles D., 2017. "A multi-criteria decision analysis approach for importance identification and ranking of network components," Reliability Engineering and System Safety, Elsevier, vol. 158(C), pages 142-151.
    14. Baroud, Hiba & Barker, Kash & Ramirez-Marquez, Jose E. & Rocco S., Claudio M., 2014. "Importance measures for inland waterway network resilience," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 62(C), pages 55-67.
    15. Safirova, Elena & Gillingham, Kenneth & Houde, Sébastien, 2007. "Measuring marginal congestion costs of urban transportation: Do networks matter?," Transportation Research Part A: Policy and Practice, Elsevier, vol. 41(8), pages 734-749, October.
    16. Tony H. Grubesic & Timothy C. Matisziw, 2008. "Prospects for Assessing and Managing Vulnerable Infrastructures: Policy and Practice," Growth and Change, Wiley Blackwell, vol. 39(4), pages 543-547, December.
    17. Vijay Prakash Sharma & Surya Prakash & Ranbir Singh, 2022. "What Prevents Sustainable Last-Mile Delivery in Industry 4.0? An Analysis and Decision Framework," Sustainability, MDPI, vol. 14(24), pages 1-20, December.
    18. Erik Jenelius & Lars-Göran Mattsson, 2011. "The impact of network density, travel and location patterns on regional road network vulnerability," ERSA conference papers ersa10p448, European Regional Science Association.
    19. Rolf Nyberg & Magnus Johansson, 2013. "Indicators of road network vulnerability to storm-felled trees," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 69(1), pages 185-199, October.
    20. Zhu, Jingjing & Xu, Xiangdong & Wang, Zijian, 2023. "Economic evaluation of redundancy design for transportation networks under disruptions: Framework and case study," Transport Policy, Elsevier, vol. 142(C), pages 70-83.

    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:15:y:2023:i:11:p:8763-:d:1158675. 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.