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A forecasting approach for truckload spot market pricing

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  • 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
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    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. 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.
    6. 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.
    7. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    8. 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.
    9. 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.
    10. 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.
    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.
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    Cited by:

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    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. 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).
    4. Jason W. Miller, 2018. "ARIMA Time Series Models for Full Truckload Transportation Prices," Forecasting, MDPI, vol. 1(1), pages 1-14, September.
    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).

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