IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v12y2019i1p238-d302516.html
   My bibliography  Save this article

On the Necessity and Effects of Considering Correlated Stochastic Speeds in Shortest Path Problems Under Sustainable Environments

Author

Listed:
  • Dongqing Zhang

    (Business School, Sichuan University, Chengdu 610065, China
    Department of Industrial Systems Engineering and Management, National University of Singapore, Singapore 117576, Singapore)

  • Zhaoxia Guo

    (Business School, Sichuan University, Chengdu 610065, China)

Abstract

This research addresses how the stochasticity and correlation of travel speeds affect the shortest path solutions in sustainable environments. We consider a shortest path problem with the objective function of minimizing a linear combination of the mean and standard deviation of carbon emissions. By adjusting the proportion of the standard deviation in the objective function, the effects of speed stochasticity and correlation are studied under different preferences of the decision-makers on the fluctuations of carbon emissions. Based on 102-day real speed data from the Los Angeles freeway network, this research conducts extensive numerical experiments on 200 randomly chosen origin-destination pairs. Experimental results demonstrate the necessity of considering speed stochasticity and correlation, especially when the standard deviation of carbon emissions takes a large proportion in the objective function. As the weight of the standard deviation in the objective function increases from 0 to 1.5, the reduction of emission objective values increases from 0.03% to 0.13% by considering speed stochasticity, and increases from 0.02% to 0.20% by considering speed correlation. Taking the city Los Angeles with about 2361 taxis and about 525,945 passenger orders in January 2017 as an example, 0.03% and 0.02% reductions respond to about 3156 kg and 2630 kg carbon emission, respectively.

Suggested Citation

  • Dongqing Zhang & Zhaoxia Guo, 2019. "On the Necessity and Effects of Considering Correlated Stochastic Speeds in Shortest Path Problems Under Sustainable Environments," Sustainability, MDPI, vol. 12(1), pages 1-14, December.
    • Handle: RePEc:gam:jsusta:v:12:y:2019:i:1:p:238-:d:302516
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/12/1/238/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/12/1/238/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ubeda, S. & Arcelus, F.J. & Faulin, J., 2011. "Green logistics at Eroski: A case study," International Journal of Production Economics, Elsevier, vol. 131(1), pages 44-51, May.
    2. Bektas, Tolga & Laporte, Gilbert, 2011. "The Pollution-Routing Problem," Transportation Research Part B: Methodological, Elsevier, vol. 45(8), pages 1232-1250, September.
    3. Huang, He & Gao, Song, 2012. "Optimal paths in dynamic networks with dependent random link travel times," Transportation Research Part B: Methodological, Elsevier, vol. 46(5), pages 579-598.
    4. He Huang & Song Gao, 2018. "Trajectory-Adaptive Routing in Dynamic Networks with Dependent Random Link Travel Times," Transportation Science, INFORMS, vol. 52(1), pages 102-117, January.
    5. Fu, Liping & Rilett, L. R., 1998. "Expected shortest paths in dynamic and stochastic traffic networks," Transportation Research Part B: Methodological, Elsevier, vol. 32(7), pages 499-516, September.
    6. Demir, Emrah & Bektaş, Tolga & Laporte, Gilbert, 2014. "A review of recent research on green road freight transportation," European Journal of Operational Research, Elsevier, vol. 237(3), pages 775-793.
    7. Elise D. Miller-Hooks & Hani S. Mahmassani, 2000. "Least Expected Time Paths in Stochastic, Time-Varying Transportation Networks," Transportation Science, INFORMS, vol. 34(2), pages 198-215, May.
    8. Dekker, Rommert & Bloemhof, Jacqueline & Mallidis, Ioannis, 2012. "Operations Research for green logistics – An overview of aspects, issues, contributions and challenges," European Journal of Operational Research, Elsevier, vol. 219(3), pages 671-679.
    9. Yang, Lixing & Zhou, Xuesong, 2014. "Constraint reformulation and a Lagrangian relaxation-based solution algorithm for a least expected time path problem," Transportation Research Part B: Methodological, Elsevier, vol. 59(C), pages 22-44.
    10. Yang, Lixing & Zhou, Xuesong, 2017. "Optimizing on-time arrival probability and percentile travel time for elementary path finding in time-dependent transportation networks: Linear mixed integer programming reformulations," Transportation Research Part B: Methodological, Elsevier, vol. 96(C), pages 68-91.
    11. Huang, Yixiao & Zhao, Lei & Van Woensel, Tom & Gross, Jean-Philippe, 2017. "Time-dependent vehicle routing problem with path flexibility," Transportation Research Part B: Methodological, Elsevier, vol. 95(C), pages 169-195.
    12. Arslan, Okan & Yıldız, Barış & Karaşan, Oya Ekin, 2015. "Minimum cost path problem for Plug-in Hybrid Electric Vehicles," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 80(C), pages 123-141.
    13. Ehmke, Jan Fabian & Campbell, Ann M. & Thomas, Barrett W., 2018. "Optimizing for total costs in vehicle routing in urban areas," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 116(C), pages 242-265.
    14. Chen, Chao, 2003. "Freeway Performance Measurement System (PeMS)," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt6j93p90t, Institute of Transportation Studies, UC Berkeley.
    15. Prakash, A. Arun, 2018. "Pruning algorithm for the least expected travel time path on stochastic and time-dependent networks," Transportation Research Part B: Methodological, Elsevier, vol. 108(C), pages 127-147.
    16. Tang, Shaolong & Wang, Wenjie & Yan, Hong & Hao, Gang, 2015. "Low carbon logistics: Reducing shipment frequency to cut carbon emissions," International Journal of Production Economics, Elsevier, vol. 164(C), pages 339-350.
    17. Tao Cheng & James Haworth & Jiaqiu Wang, 2012. "Spatio-temporal autocorrelation of road network data," Journal of Geographical Systems, Springer, vol. 14(4), pages 389-413, October.
    18. Michal Kaut & Stein Wallace, 2011. "Shape-based scenario generation using copulas," Computational Management Science, Springer, vol. 8(1), pages 181-199, April.
    19. Alireza Ermagun & Snigdhansu Chatterjee & David Levinson, 2017. "Using temporal detrending to observe the spatial correlation of traffic," PLOS ONE, Public Library of Science, vol. 12(5), pages 1-21, May.
    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. Magdalena Mucowska, 2021. "Trends of Environmentally Sustainable Solutions of Urban Last-Mile Deliveries on the E-Commerce Market—A Literature Review," Sustainability, MDPI, vol. 13(11), pages 1-26, May.

    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, Dongqing & Wallace, Stein W. & Guo, Zhaoxia & Dong, Yucheng & Kaut, Michal, 2021. "On scenario construction for stochastic shortest path problems in real road networks," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 152(C).
    2. Asghari, Mohammad & Mirzapour Al-e-hashem, S. Mohammad J., 2021. "Green vehicle routing problem: A state-of-the-art review," International Journal of Production Economics, Elsevier, vol. 231(C).
    3. Prakash, A. Arun, 2018. "Pruning algorithm for the least expected travel time path on stochastic and time-dependent networks," Transportation Research Part B: Methodological, Elsevier, vol. 108(C), pages 127-147.
    4. Bektaş, Tolga & Ehmke, Jan Fabian & Psaraftis, Harilaos N. & Puchinger, Jakob, 2019. "The role of operational research in green freight transportation," European Journal of Operational Research, Elsevier, vol. 274(3), pages 807-823.
    5. Mohammad Asghari & Seyed Mohammad Javad Mirzapour Al-E-Hashem, 2021. "Green vehicle routing problem: A state-of-the-art review," Post-Print hal-03182944, HAL.
    6. Ehmke, Jan Fabian & Campbell, Ann M. & Thomas, Barrett W., 2018. "Optimizing for total costs in vehicle routing in urban areas," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 116(C), pages 242-265.
    7. Kramer, Raphael & Subramanian, Anand & Vidal, Thibaut & Cabral, Lucídio dos Anjos F., 2015. "A matheuristic approach for the Pollution-Routing Problem," European Journal of Operational Research, Elsevier, vol. 243(2), pages 523-539.
    8. Sam Heshmati & Jannes Verstichel & Eline Esprit & Greet Vanden Berghe, 2019. "Alternative e-commerce delivery policies," EURO Journal on Transportation and Logistics, Springer;EURO - The Association of European Operational Research Societies, vol. 8(3), pages 217-248, September.
    9. Wen, Liang & Çatay, Bülent & Eglese, Richard, 2014. "Finding a minimum cost path between a pair of nodes in a time-varying road network with a congestion charge," European Journal of Operational Research, Elsevier, vol. 236(3), pages 915-923.
    10. Behnke, Martin & Kirschstein, Thomas & Bierwirth, Christian, 2021. "A column generation approach for an emission-oriented vehicle routing problem on a multigraph," European Journal of Operational Research, Elsevier, vol. 288(3), pages 794-809.
    11. Zhou, Leishan & Tong, Lu (Carol) & Chen, Junhua & Tang, Jinjin & Zhou, Xuesong, 2017. "Joint optimization of high-speed train timetables and speed profiles: A unified modeling approach using space-time-speed grid networks," Transportation Research Part B: Methodological, Elsevier, vol. 97(C), pages 157-181.
    12. Zhou, Yizi & Mandania, Rupal & Liu, Jiyin, 2022. "Green vehicle routing and dynamic pricing for scheduling on-site services," International Journal of Production Economics, Elsevier, vol. 254(C).
    13. Shen, Liang & Shao, Hu & Wu, Ting & Fainman, Emily Zhu & Lam, William H.K., 2020. "Finding the reliable shortest path with correlated link travel times in signalized traffic networks under uncertainty," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 144(C).
    14. Yang, Lixing & Zhang, Yan & Li, Shukai & Gao, Yuan, 2016. "A two-stage stochastic optimization model for the transfer activity choice in metro networks," Transportation Research Part B: Methodological, Elsevier, vol. 83(C), pages 271-297.
    15. A. Arun Prakash & Karthik K. Srinivasan, 2017. "Finding the Most Reliable Strategy on Stochastic and Time-Dependent Transportation Networks: A Hypergraph Based Formulation," Networks and Spatial Economics, Springer, vol. 17(3), pages 809-840, September.
    16. Arun Prakash, A., 2020. "Algorithms for most reliable routes on stochastic and time-dependent networks," Transportation Research Part B: Methodological, Elsevier, vol. 138(C), pages 202-220.
    17. Raeesi, Ramin & Zografos, Konstantinos G., 2019. "The multi-objective Steiner pollution-routing problem on congested urban road networks," Transportation Research Part B: Methodological, Elsevier, vol. 122(C), pages 457-485.
    18. Liu, Yang & Blandin, Sebastien & Samaranayake, Samitha, 2019. "Stochastic on-time arrival problem in transit networks," Transportation Research Part B: Methodological, Elsevier, vol. 119(C), pages 122-138.
    19. Canan G. Corlu & Rocio de la Torre & Adrian Serrano-Hernandez & Angel A. Juan & Javier Faulin, 2020. "Optimizing Energy Consumption in Transportation: Literature Review, Insights, and Research Opportunities," Energies, MDPI, vol. 13(5), pages 1-33, March.
    20. Yang, Lixing & Zhou, Xuesong, 2017. "Optimizing on-time arrival probability and percentile travel time for elementary path finding in time-dependent transportation networks: Linear mixed integer programming reformulations," Transportation Research Part B: Methodological, Elsevier, vol. 96(C), pages 68-91.

    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:12:y:2019:i:1:p:238-:d:302516. 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.