Date(s) - March 12, 2021
12:00 pm - 1:00 pm
The choice of vehicle type is one of the important logistics decisions made by firms. The complex nature of the choice process is due to 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. Permutation-based variable importance is calculated to interpret the importance of each variable which 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 testing 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 8.3% and 11%, respectively.
Usman Ahmed is a PhD candidate at the Department of Civil and Mineral Engineering at the University of Toronto, under the supervision of Professor Matthew Roorda.
He received his master’s degree in Transportation Systems in 2018 from the Technical University of Munich and his bachelor’s degree in Civil Engineering from the National University of Sciences and Technology, Islamabad. During his master’s studies, he also worked as a research assistant at Modelling Spatial Mobility research group.
Ahmed’s research interests include transportation modelling, emissions modelling and machine learning applications in transportation.
Presented by University of Toronto ITE Student Chapter, UT-ITE.
Free. All are welcome.
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