Metrohelper: A Real-time Web-based System for Metro Incident Detection Using Social Media

In recent years the usage of public transit services has been rapidly increased, thanks to huge progress on network technologies. However, the disruptions in modern public transit services also increased, due to aging infrastructure, non-comprehensive system design and the needs for maintenance. Any disruptions happened in current transit networks can cause to major disasters on passengers who use these networks for their daily commutes. Although we have lots of usage on transit network, still most current disruptions detection systems either lack of network coverage or did not have real-time system. The goal of this thesis was to create a system that can leverage Twitter data to help in detecting service disruptions in their early stage. This work involves a web applications which contains front-end, back-end and database, along with data mining techniques that obtain Tweets from a live Twitter stream related to the Washington Metropolitan Area Transit Authority (WMATA) metro system. The fundamental features of the system includes real-time incidents panel, historical events review, activities search near specific metro station and recent news review, which allowing people to have more relatively information based on their needs. After the initial functionalities is being settled, we further developed storytelling and sentiment analysis applications, which allowed people have more comprehensive information about the incidents that are happened around metro stations. Also, with the emergency report we developed, the developer can have immediate notification when an urgent event occurred. After fully testified the system's case study on storytelling, sentiment analysis and emergency report, the outcomes are extreme convincing and trustworthy.

Name thesis:degree_name

Master of Science

Level thesis:degree_level

masters

Discipline thesis:degree_discipline

Computer Science and Applications

Department dc:contributor.department

Computer Science

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2022

Author dc:creator

• Chen, Chih Fang

Chair dc:contributor.committeechair

• Lu, Chang Tien

Committee members dc:contributor.committeemember

• Reddy, Chandan K.
• Cho, Jin-Hee

• Information Retrieval
• Twitter
• Social Media
• Data Mining

Statement dc:rights

• In Copyright

Licence dc:rights.uri

• http://rightsstatements.org/vocab/InC/1.0/

Language dc:language.iso

en

Dc Identifier Other

vt_gsexam:35185

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/110349