MIT Libraries logoDSpace@MIT

MIT
View Item 
  • DSpace@MIT Home
  • Supply Chain Management
  • Supply Chain Management Capstone Projects
  • View Item
  • DSpace@MIT Home
  • Supply Chain Management
  • Supply Chain Management Capstone Projects
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

Application of Linear Models, Random Forest, and Gradient Boosting Methods to Identify Key Factors and Predict Truck Dwell Time for a Global 3PL Company

Author(s)
Benjatanont, Sireethorn; Tantuico, Dylan
Thumbnail
DownloadSupply Chain Management capstone research project (1.613Mb)
Metadata
Show full item record
Abstract
Driver dwell time is an important challenge the U.S trucking industry faces. High, unplanned dwell times are costly to all stakeholders in the industry as they result in detention costs, declining performance and decreased driver capacity. With the increasing demand for these services, it is important to maximize the driving time of drivers in the industry by minimizing dwell time to free up capacity and provide competitive wages. This project utilizes the data of a third-party logistics company with the goal to understand the factors that influence dwell time, and to construct the model to predict dwell time of a load. In the analysis, linear models, random forest, and gradient boosting methods were explored based on regression and classification approach. Ultimately, the random forest classification model with one-hour bins is the recommended model as it had the highest predictive performance while the one-hour bins was sufficient to meet the business need. Additionally, the analysis concludes that shipper facilities are the most significant driver of dwell time. Hence, understanding and integrating more granular observations on shipper practices within their facilities will allow a third-party logistics company to improve its driver fleet utilization and increase the predictive performance of their dwell time prediction model.
Date issued
2020-07-24
URI
https://hdl.handle.net/1721.1/126379
Keywords
Data Analytics, Transportation, Machine Learning

Collections
  • Supply Chain Management Capstone Projects

Browse

All of DSpaceCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsThis CollectionBy Issue DateAuthorsTitlesSubjects

My Account

Login

Statistics

OA StatisticsStatistics by CountryStatistics by Department
MIT Libraries
PrivacyPermissionsAccessibilityContact us
MIT
Content created by the MIT Libraries, CC BY-NC unless otherwise noted. Notify us about copyright concerns.