IDEAS home Printed from https://ideas.repec.org/a/eee/transa/v97y2017icp55-68.html
   My bibliography  Save this article

A forecasting approach for truckload spot market pricing

Author

Listed:
  • Budak, Aysenur
  • Ustundag, Alp
  • Guloglu, Bulent

Abstract

Logistics is an important sector considering the increasingly competitive nature of industry today. Large-scale companies and third-party logistics providers want the most economical and reliable forecasting mechanism for pricing the truckload spot market in the sphere of logistics and supply chains. This paper investigates the price forecasting of the truckload spot market, which is an important area for the determination of future value from the viewpoint of truckers by considering comprehensive variables. Two methodologies are used to determine truckers’ spot price in the freight transport process, which are the artificial neural network and quantile regression, and a price forecasting framework is created. The framework is applied to two approaches: a route-based model and a general model in which all routes are considered together. Real data are used to demonstrate the applicability and feasibility of the proposed method. In this scope forecast performances can be assessed, the best methodology and approach can be selected, and projections can be carried out.

Suggested Citation

  • Budak, Aysenur & Ustundag, Alp & Guloglu, Bulent, 2017. "A forecasting approach for truckload spot market pricing," Transportation Research Part A: Policy and Practice, Elsevier, vol. 97(C), pages 55-68.
    • Handle: RePEc:eee:transa:v:97:y:2017:i:c:p:55-68
    DOI: 10.1016/j.tra.2017.01.002
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0965856416302257
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.tra.2017.01.002?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 search for a different version of it.

    References listed on IDEAS

    as
    1. Tjokroamidjojo, Darsono & Kutanoglu, Erhan & Taylor, G. Don, 2006. "Quantifying the value of advance load information in truckload trucking," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 42(4), pages 340-357, July.
    2. Hammoudeh, Shawkat & Nguyen, Duc Khuong & Sousa, Ricardo M., 2014. "Energy prices and CO2 emission allowance prices: A quantile regression approach," Energy Policy, Elsevier, vol. 70(C), pages 201-206.
    3. Timothy Madden & Robert A. Russell, 2009. "Evaluating and pricing lanes in the truckload motor freight industry," International Journal of Revenue Management, Inderscience Enterprises Ltd, vol. 3(3), pages 235-251.
    4. Coria, Jessica & Bonilla, Jorge & Grundström, Maria & Pleijel, Håkan, 2015. "Air pollution dynamics and the need for temporally differentiated road pricing," Transportation Research Part A: Policy and Practice, Elsevier, vol. 75(C), pages 178-195.
    5. Zhong, Shaopeng & Wang, Shusheng & Jiang, Yao & Yu, Bo & Zhang, Wenhao, 2015. "Distinguishing the land use effects of road pricing based on the urban form attributes," Transportation Research Part A: Policy and Practice, Elsevier, vol. 74(C), pages 44-58.
    6. Kang, Hsin-Hong & Liu, Shu-Bing, 2014. "The impact of the 2008 financial crisis on housing prices in China and Taiwan: A quantile regression analysis," Economic Modelling, Elsevier, vol. 42(C), pages 356-362.
    7. Marcucci, Edoardo & Gatta, Valerio & Scaccia, Luisa, 2015. "Urban freight, parking and pricing policies: An evaluation from a transport providers’ perspective," Transportation Research Part A: Policy and Practice, Elsevier, vol. 74(C), pages 239-249.
    8. Gabriel Bitran & René Caldentey, 2003. "An Overview of Pricing Models for Revenue Management," Manufacturing & Service Operations Management, INFORMS, vol. 5(3), pages 203-229, August.
    9. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    10. Jammazi, Rania & Aloui, Chaker, 2012. "Crude oil price forecasting: Experimental evidence from wavelet decomposition and neural network modeling," Energy Economics, Elsevier, vol. 34(3), pages 828-841.
    11. Smith, L. Douglas & Campbell, James F. & Mundy, Ray, 2007. "Modeling net rates for expedited freight services," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 43(2), pages 192-207, March.
    12. Warren B. Powell & Yosef Sheffi & Kenneth S. Nickerson & Kevin Butterbaugh & Susan Atherton, 1988. "Maximizing Profits for North American Van Lines' Truckload Division: A New Framework for Pricing and Operations," Interfaces, INFORMS, vol. 18(1), pages 21-41, February.
    13. Movagharnejad, Kamyar & Mehdizadeh, Bahman & Banihashemi, Morteza & Kordkheili, Masoud Sheikhi, 2011. "Forecasting the differences between various commercial oil prices in the Persian Gulf region by neural network," Energy, Elsevier, vol. 36(7), pages 3979-3984.
    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. Michael F. Gorman & John-Paul Clarke & René Koster & Michael Hewitt & Debjit Roy & Mei Zhang, 2023. "Emerging practices and research issues for big data analytics in freight transportation," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 25(1), pages 28-60, March.
    2. Wang, Kelly Yujie & Wen, Yuan & Yip, Tsz Leung & Fan, Zuojun, 2021. "Carrier-shipper risk management and coordination in the presence of spot freight market," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 149(C).
    3. Jason W. Miller, 2018. "ARIMA Time Series Models for Full Truckload Transportation Prices," Forecasting, MDPI, vol. 1(1), pages 1-14, September.
    4. Park, Arim & Chen, Roger & Cho, Soohyun & Zhao, Yao, 2023. "The determinants of online matching platforms for freight services," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 179(C).
    5. Zhuang, Zheng-Yun & Chung, Cheng-Kung, 2024. "Dissecting the visiting willingness of driving visitors facing a retail market's dual-pricing policy for parking," Journal of Retailing and Consumer Services, Elsevier, vol. 78(C).

    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. Jose Torres-Pruñonosa & Pablo García-Estévez & Josep Maria Raya & Camilo Prado-Román, 2022. "How on Earth Did Spanish Banking Sell the Housing Stock?," SAGE Open, , vol. 12(1), pages 21582440221, March.
    2. Stelios Bekiros & Amanda Dahlström & Gazi Salah Uddin & Oskar Ege & Ranadeva Jayasekera, 2020. "A tale of two shocks: The dynamics of international real estate markets," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 25(1), pages 3-27, January.
    3. Wang, Bin & Wang, Jun, 2021. "Energy futures price prediction and evaluation model with deep bidirectional gated recurrent unit neural network and RIF-based algorithm," Energy, Elsevier, vol. 216(C).
    4. Karim, Muhammad Mahmudul & Kawsar, Najmul Haque & Ariff, Mohamed & Masih, Mansur, 2022. "Does implied volatility (or fear index) affect Islamic stock returns and conventional stock returns differently? Wavelet-based granger-causality, asymmetric quantile regression and NARDL approaches," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 77(C).
    5. Yu, Lean & Wang, Zishu & Tang, Ling, 2015. "A decomposition–ensemble model with data-characteristic-driven reconstruction for crude oil price forecasting," Applied Energy, Elsevier, vol. 156(C), pages 251-267.
    6. Zolfagharinia, Hossein & Haughton, Michael, 2016. "Effective truckload dispatch decision methods with incomplete advance load information," European Journal of Operational Research, Elsevier, vol. 252(1), pages 103-121.
    7. Godarzi, Ali Abbasi & Amiri, Rohollah Madadi & Talaei, Alireza & Jamasb, Tooraj, 2014. "Predicting oil price movements: A dynamic Artificial Neural Network approach," Energy Policy, Elsevier, vol. 68(C), pages 371-382.
    8. Zheng, Yan & Yin, Hua & Zhou, Min & Liu, Wenhua & Wen, Fenghua, 2021. "Impacts of oil shocks on the EU carbon emissions allowances under different market conditions," Energy Economics, Elsevier, vol. 104(C).
    9. Zolfagharinia, Hossein & Haughton, Michael A., 2017. "Operational flexibility in the truckload trucking industry," Transportation Research Part B: Methodological, Elsevier, vol. 104(C), pages 437-460.
    10. Ding, Yishan, 2018. "A novel decompose-ensemble methodology with AIC-ANN approach for crude oil forecasting," Energy, Elsevier, vol. 154(C), pages 328-336.
    11. Cheng, Fangzheng & Li, Tian & Wei, Yi-ming & Fan, Tijun, 2019. "The VEC-NAR model for short-term forecasting of oil prices," Energy Economics, Elsevier, vol. 78(C), pages 656-667.
    12. Tan, Xue-Ping & Wang, Xin-Yu, 2017. "Dependence changes between the carbon price and its fundamentals: A quantile regression approach," Applied Energy, Elsevier, vol. 190(C), pages 306-325.
    13. Maciejowska, Katarzyna, 2020. "Assessing the impact of renewable energy sources on the electricity price level and variability – A quantile regression approach," Energy Economics, Elsevier, vol. 85(C).
    14. Acocella, Angela & Caplice, Chris & Sheffi, Yossi, 2020. "Elephants or goldfish?: An empirical analysis of carrier reciprocity in dynamic freight markets," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 142(C).
    15. Niu, Shuwen & Jia, Yanqin & Ye, Liqiong & Dai, Runqi & Li, Na, 2016. "Does electricity consumption improve residential living status in less developed regions? An empirical analysis using the quantile regression approach," Energy, Elsevier, vol. 95(C), pages 550-560.
    16. Zolfagharinia, Hossein & Haughton, Michael, 2018. "The importance of considering non-linear layover and delay costs for local truckers," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 109(C), pages 331-355.
    17. Huber, Julian & Dann, David & Weinhardt, Christof, 2020. "Probabilistic forecasts of time and energy flexibility in battery electric vehicle charging," Applied Energy, Elsevier, vol. 262(C).
    18. Wu, Chunying & Wang, Jianzhou & Hao, Yan, 2022. "Deterministic and uncertainty crude oil price forecasting based on outlier detection and modified multi-objective optimization algorithm," Resources Policy, Elsevier, vol. 77(C).
    19. Zhong, Shaopeng & Bushell, Max, 2017. "Impact of the built environment on the vehicle emission effects of road pricing policies: A simulation case study," Transportation Research Part A: Policy and Practice, Elsevier, vol. 103(C), pages 235-249.
    20. Haas, Christian & Budin, Constantin & d’Arcy, Anne, 2024. "How to select oil price prediction models — The effect of statistical and financial performance metrics and sentiment scores," Energy Economics, Elsevier, vol. 133(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:eee:transa:v:97:y:2017:i:c:p:55-68. 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.elsevier.com/wps/find/journaldescription.cws_home/547/description#description .

    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.