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Big Data Mining to Construct Truck Tours

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  • Mon, 05/28/2018 - 2:00pm - 4:00pm




Big Data Mining to Construct Truck Tours

MSc Thesis Proposal by:

Vidhi Patel

Date:  Monday, May 28th, 2018
Time:  2:00 pm – 4:00 pm
Location: 3105, Lambton Tower

Abstract: Cross Border shipping of goods between different distributors has increased economic and transportation trend across Canada and U.S. This leads to a very high proportion of truck travel between Canada and U.S. This research outlines the identification of tours accomplishing the shipping of goods and returning back to the initial firm from where it started. A truck tour is ideally defined as a round trip where a truck starts its journey from its firm or an industry, performing stops for different purpose including taking rest stops and transferring goods to multiple locations and returns back to its initial firm location. In short, tour is defined as a combination of multiple trips. In this research, a two-step methodology is proposed to discover truck tours. In the first step, various machine learning algorithm such as K-means, DBSCAN, SNN are used for clustering. In second step, various statistic measures like dwell time measure or entropy measure are applied on resulting clusters to identify firms for respective carriers. Hence, this research mainly focuses on mining large set of GPS truck data to identify all the truck tours belonging to different carriers.

Thesis Committee:
Internal Reader: Dr. Pooya Moradian Zadeh
External Reader: Dr. Hanna Maoh
Advisor: Dr. Mehdi Kargar



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