Dissertation/Thesis Abstract

Real-Time Road Traffic Events Detection and Geo-Parsing
by Kumar, Saurabh, M.S.E.C.E., Purdue University, 2018, 70; 10842958
Abstract (Summary)

In the 21st century, there is an increasing number of vehicles on the road as well as a limited road infrastructure. These aspects culminate in daily challenges for the average commuter due to congestion and slow moving traffic. In the United States alone, it costs an average US driver $1200 every year in the form of fuel and time. Some positive steps, including (a) introduction of the push notification system and (b) deploying more law enforcement troops, have been taken for better traffic management. However, these methods have limitations and require extensive planning. Another method to deal with traffic problems is to track the congested area in a city using social media. Next, law enforcement resources can be re-routed to these areas on a real-time basis.

Given the ever-increasing number of smartphone devices, social media can be used as a source of information to track the traffic-related incidents.

Social media sites allow users to share their opinions and information. Platforms like Twitter, Facebook, and Instagram are very popular among users. These platforms enable users to share whatever they want in the form of text and images. Facebook users generate millions of posts in a minute. On these platforms, abundant data, including news, trends, events, opinions, product reviews, etc. are generated on a daily basis.

Worldwide, organizations are using social media for marketing purposes. This data can also be used to analyze the traffic-related events like congestion, construction work, slow-moving traffic etc. Thus the motivation behind this research is to use social media posts to extract information relevant to traffic, with effective and proactive traffic administration as the primary focus. I propose an intuitive two-step process to utilize Twitter users' posts to obtain for retrieving traffic-related information on a real-time basis. It uses a text classifier to filter out the data that contains only traffic information. This is followed by a Part-Of-Speech (POS) tagger to find the geolocation information. A prototype of the proposed system is implemented using distributed microservices architecture.

Indexing (document details)
Advisor: Koskie, Sarah
Commitee: King, Brian, Luo, Xiao
School: Purdue University
Department: Electrical and Computer Engineering
School Location: United States -- Indiana
Source: MAI 58/01M(E), Masters Abstracts International
Subjects: Computer Engineering, Transportation, Artificial intelligence
Keywords: Deep learning, Distributed architecture, Machine learning
Publication Number: 10842958
ISBN: 978-0-438-37544-4
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