IDEAS home Printed from https://ideas.repec.org/a/eee/transe/v150y2021ics1366554521000703.html
   My bibliography  Save this article

Load factors of less-than-truckload delivery tours: An analysis with operation data

Author

Listed:
  • Ni, Linglin
  • Wang, Xiaokun

Abstract

The load factor is an essential indicator of vehicle utilization efficiency. This paper is the first attempt to explore the factors impacting Less-than-Truckload (LTL) freight load factors at a disaggregated level to the best of our knowledge. The paper analyzes the operation data provided by a leading LTL logistics company in China, using the beta regression finite mixture models. The results show that the under-loaded tours and over-loaded tours behave differently in terms of load factor determination. All variables' impacts vary across the different components, confirming the need to analyze these two scenarios separately using finite mixture models. For under-loaded tours, high shipping demand and the use of small vehicles are always positively and significantly associated with the load factor. For over-loaded LTL tours, the effects of transportation distance and leased vehicles are always positive and significant. Finally, implications for operations strategies and public policies are discussed based on the estimated results. These findings shed light on an in-depth understanding of LTL freight load factors and provide essential references for LTL logistics companies, transportation planners, policymakers, and researchers.

Suggested Citation

  • Ni, Linglin & Wang, Xiaokun, 2021. "Load factors of less-than-truckload delivery tours: An analysis with operation data," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 150(C).
    • Handle: RePEc:eee:transe:v:150:y:2021:i:c:s1366554521000703
    DOI: 10.1016/j.tre.2021.102296
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1366554521000703
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.tre.2021.102296?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. Sağlam, Ümit & Banerjee, Avijit, 2018. "Integrated multiproduct batch production and truck shipment scheduling under different shipping policies," Omega, Elsevier, vol. 74(C), pages 70-81.
    2. Silvia Ferrari & Francisco Cribari-Neto, 2004. "Beta Regression for Modelling Rates and Proportions," Journal of Applied Statistics, Taylor & Francis Journals, vol. 31(7), pages 799-815.
    3. Figueroa-Zúñiga, Jorge I. & Arellano-Valle, Reinaldo B. & Ferrari, Silvia L.P., 2013. "Mixed beta regression: A Bayesian perspective," Computational Statistics & Data Analysis, Elsevier, vol. 61(C), pages 137-147.
    4. Wanke, Peter & Barros, C.P. & Figueiredo, Otávio, 2016. "Efficiency and productive slacks in urban transportation modes: A two-stage SDEA-Beta Regression approach," Utilities Policy, Elsevier, vol. 41(C), pages 31-39.
    5. Lyu, Xiaohui & Chen, Haoxun & Wang, Nengmin & Yang, Zhen, 2019. "A multi-round exchange mechanism for carrier collaboration in less than truckload transportation," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 129(C), pages 38-59.
    6. Chen, Kee Kuo & Chiu, Rong-Her & Chang, Ching-Ter, 2017. "Using beta regression to explore the relationship between service attributes and likelihood of customer retention for the container shipping industry," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 104(C), pages 1-16.
    7. Richard O. Mason & James L. McKenney & Walter Carlson & Duncan Copeland, 1997. "Absolutely, Positively Operations Research: The Federal Express Story," Interfaces, INFORMS, vol. 27(2), pages 17-36, April.
    8. Bermúdez, Lluís & Karlis, Dimitris, 2012. "A finite mixture of bivariate Poisson regression models with an application to insurance ratemaking," Computational Statistics & Data Analysis, Elsevier, vol. 56(12), pages 3988-3999.
    9. Marco Alfo & Giovanni Trovato & Robert J. Waldmann, 2008. "Testing for country heterogeneity in growth models using a finite mixture approach," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 23(4), pages 487-514.
    10. Özkaya, Evren & Keskinocak, PInar & Roshan Joseph, V. & Weight, Ryan, 2010. "Estimating and benchmarking Less-than-Truckload market rates," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 46(5), pages 667-682, September.
    11. Abate, Megersa & de Jong, Gerard, 2014. "The optimal shipment size and truck size choice – The allocation of trucks across hauls," Transportation Research Part A: Policy and Practice, Elsevier, vol. 59(C), pages 262-277.
    12. Kuby, Michael J. & Gray, Robert Gordon, 1993. "The hub network design problem with stopovers and feeders: The case of Federal Express," Transportation Research Part A: Policy and Practice, Elsevier, vol. 27(1), pages 1-12, January.
    13. Simas, Alexandre B. & Barreto-Souza, Wagner & Rocha, Andréa V., 2010. "Improved estimators for a general class of beta regression models," Computational Statistics & Data Analysis, Elsevier, vol. 54(2), pages 348-366, February.
    14. Novas, Juan M. & Ramello, Juan Ignacio & Rodríguez, María Analía, 2020. "Generalized disjunctive programming models for the truck loading problem: A case study from the non-alcoholic beverages industry," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 140(C).
    15. van de Klundert, Joris & Otten, Bernhard, 2011. "Improving LTL truck load utilization on line," European Journal of Operational Research, Elsevier, vol. 210(2), pages 336-343, April.
    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. Phillip Li, 2018. "Efficient MCMC estimation of inflated beta regression models," Computational Statistics, Springer, vol. 33(1), pages 127-158, March.
    2. Francisco Cribari-Neto & Sadraque E.F. Lucena, 2015. "Nonnested hypothesis testing in the class of varying dispersion beta regressions," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(5), pages 967-985, May.
    3. Wagner Hugo Bonat & Paulo Justiniano Ribeiro & Walmes Marques Zeviani, 2015. "Likelihood analysis for a class of beta mixed models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(2), pages 252-266, February.
    4. Cepeda-Cuervo Edilberto & Garrido Liliana, 2015. "Bayesian beta regression models with joint mean and dispersion modeling," Monte Carlo Methods and Applications, De Gruyter, vol. 21(1), pages 49-58, March.
    5. Grün, Bettina & Kosmidis, Ioannis & Zeileis, Achim, 2012. "Extended Beta Regression in R: Shaken, Stirred, Mixed, and Partitioned," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 48(i11).
    6. Cristine Rauber & Francisco Cribari-Neto & Fábio M. Bayer, 2020. "Improved testing inferences for beta regressions with parametric mean link function," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 104(4), pages 687-717, December.
    7. Li-Chu Chien, 2013. "Multiple deletion diagnostics in beta regression models," Computational Statistics, Springer, vol. 28(4), pages 1639-1661, August.
    8. Giovanna Bua & Carmine Trecroci, 2019. "International equity markets interdependence: bigger shocks or contagion in the 21st century?," Review of World Economics (Weltwirtschaftliches Archiv), Springer;Institut für Weltwirtschaft (Kiel Institute for the World Economy), vol. 155(1), pages 43-69, February.
    9. Frank A. La Sorte & Alison Johnston & Toby R. Ault, 2021. "Global trends in the frequency and duration of temperature extremes," Climatic Change, Springer, vol. 166(1), pages 1-14, May.
    10. Pablo Mitnik & Sunyoung Baek, 2013. "The Kumaraswamy distribution: median-dispersion re-parameterizations for regression modeling and simulation-based estimation," Statistical Papers, Springer, vol. 54(1), pages 177-192, February.
    11. O'Kelly, Morton E., 2014. "Air freight hubs in the FedEx system: Analysis of fuel use," Journal of Air Transport Management, Elsevier, vol. 36(C), pages 1-12.
    12. Fábio Bayer & Francisco Cribari-Neto, 2015. "Bootstrap-based model selection criteria for beta regressions," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 24(4), pages 776-795, December.
    13. Yiyun Shou & Michael Smithson, 2015. "Evaluating Predictors of Dispersion: A Comparison of Dominance Analysis and Bayesian Model Averaging," Psychometrika, Springer;The Psychometric Society, vol. 80(1), pages 236-256, March.
    14. Patrícia L. Espinheira & Alisson Oliveira Silva, 2020. "Residual and influence analysis to a general class of simplex regression," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 29(2), pages 523-552, June.
    15. Oscar Melo & Carlos Melo & Jorge Mateu, 2015. "Distance-based beta regression for prediction of mutual funds," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 99(1), pages 83-106, January.
    16. Montoya-Blandón, Santiago & Jacho-Chávez, David T., 2020. "Semiparametric quasi maximum likelihood estimation of the fractional response model," Economics Letters, Elsevier, vol. 186(C).
    17. Edilberto Cepeda-Cuervo & Vicente Núñez-Antón, 2013. "Spatial Double Generalized Beta Regression Models," Journal of Educational and Behavioral Statistics, , vol. 38(6), pages 604-628, December.
    18. Luiz M A Lima-Filho & Tarciana Liberal Pereira & Tatiene C Souza & Fábio M Bayer, 2020. "Process monitoring using inflated beta regression control chart," PLOS ONE, Public Library of Science, vol. 15(7), pages 1-20, July.
    19. Gambetti, Paolo & Gauthier, Geneviève & Vrins, Frédéric, 2019. "Recovery rates: Uncertainty certainly matters," Journal of Banking & Finance, Elsevier, vol. 106(C), pages 371-383.
    20. Carolina Costa Mota Paraíba & Natalia Bochkina & Carlos Alberto Ribeiro Diniz, 2018. "Bayesian truncated beta nonlinear mixed-effects models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 45(2), pages 320-346, January.

    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:transe:v:150:y:2021:i:c:s1366554521000703. 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/600244/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.