Social Media Analysis for Transit Assessment

The impact of personal opinions, attitudes, and belief is significant in decision-making processes for public transportation services. Therefore, stakeholders and transportation planners have been trying to collect various information on public transit service and performance to assess quality and management strategies. In this regime, social network service (SNS) can be considered as a large but unorganized database of information where individuals exchange event base attitude and sentiment (i.e., experience from individual transportation activity). This information often leads a chain effect that encourages others to react the message (e.g., a single post on a Twitter is visible to those who are connected to the commenter and recursively propagates beyond them). While these posts reflect users’ exhaustive experience on transportation service quality and performance, it is extremely difficult to derive meaningful information by human force since the data are large, arbitrary and complex. This study will employ recent advances in artificial intelligence (AI) for big and complicated data analysis. Using big data collected from social media such as Twitter, the research team proposes to 1) capture transit riders’ perception and sentiment when there are changes in the transit system in various temporal and spatial spans and 2) evaluate transit service including efficiency, equity and reliability and 3) implement a web-based interactive platform with a real-time data streaming and geographic information systems (GIS) map system. To achieve the objectives above, Twitter data containing transit-related texts will be collected from population-dense and transit operating cities (e.g., Los Angeles, New York, Atlanta, and Dallas-Fort Worth) and analyzed using techniques from state-of-the-art Machine Learning (ML) algorithms. The team expects that the proposed study will produce feedbacks for policy makers who explore communication and information technology to create strategies and employ big data to improve system efficiency and transit ridership.