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Title: Modeling Freight Vehicle Type Choice using Machine Learning and Discrete Choice Methods
Accession Number: 01783550
Record Type: Component
Record URL: Availability: Find a library where document is available Abstract: The choice of vehicle type is one of the important logistics decisions made by firms. The complex nature of the choice process is because of the involvement of multiple agents. This study employs a random forest machine learning algorithm to represent these complex interactions with limited information about shipment transportation. The data are from Commercial Travel Surveys with information about outbound shipment transportation. This study models the choice among four road transport vehicle types: pickup/cube van, single-unit truck, tractor trailer, and passenger car. The characteristics of firms and shipments are used as explanatory variables. SHAP-based variable importance is calculated to interpret the importance of each variable, and shows that employment and weight are the most important variables in determining the choice of vehicle type. The random forest model is also compared with the multinomial and mixed logit models. The model prediction results on the validation data are compared. The results show that random forest model outperforms both the multinomial and mixed logit model with an overall increase in accuracy of about 7.8% and 9.6%, respectively.
Supplemental Notes: Usman Ahmed https://orcid.org/0000-0003-4101-6650
© National Academy of Sciences: Transportation Research Board 2021.
Language: English
Authors: Pagination: pp 541-552
Publication Date: 2022-2
Serial:
Transportation Research Record: Journal of the Transportation Research Board
Volume: 2676 Media Type: Digital/other
Features: Figures; References
(41)
; Tables
TRT Terms: Subject Areas: Freight Transportation; Highways; Planning and Forecasting
Files: TRIS, TRB, ATRI
Created Date: Sep 25 2021 3:18PM
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0000-0003-4101-6650