Thesis/Capstone
Publication Date
Authored by
Bobby Kheny, Claire Urbi
Advisor(s): David Correll
Topic(s) Covered:
  • Transportation
Abstract

Anticipating fluctuations in Less-than-Truckload (LTL) volume presents challenges for shippers, carriers, and freight brokers alike. This capstone addresses this issue by developing a predictive model for LTL volume, leveraging insights gained from the cyclical nature of demand shifts between Truckload and LTL freight. Through analyzing various truckload metrics, this study identifies key indicators that precede changes in LTL volume. The resulting prediction model offers valuable insights and informs the creation of practical heuristics for freight brokers, enabling them to respond proactively to market fluctuations. By securing contract rates timelier, freight brokers can effectively navigate changing market dynamics, thus enhancing operational flexibility and adaptability within the industry.
 

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