Characterization and short term forecasting of the US long haul truckload spot market

Accurate forecasting of transportation costs is a key step in logistical planning. It helps buyers and sellers of transportation services make better decisions at all stages of a supply chain, thus creating a significant need to develop forecasting techniques that give useful results. First, we study the truckload market in the US by defining indicators that capture the market characteristics. Then we explore techniques for making short term weekly forecasts for truckload spot market rates at a national level and of selected 3-Zip origin regions in the USA. Short term spot rate forecasts help with making operational decisions, estimating budget for shippers, and cash flow for carriers. But making frequent forecasts for volatile time series such as truckload spot rates comes with its challenges. We solve the problem using four models: Naive, Moving Average, Auto Regressive Integrated Moving Average, and Feed-Forward Neural Networks. Additionally, we employ concept drift handling techniques to re-train the models regularly with new information to account for changes that may appear in the underlying data structure over time. Finally, we draw inferences from the MAPEs of the models and comment on their merit.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Civil and Environmental Engineering

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2019

Author dc:creator

• Rana, Shraddha(Shraddha Sudipta)

Advisor dc:contributor.advisor

• Chris Caplice.

• Civil and Environmental Engineering.

Statement dc:rights

• MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/123236

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/123236