Complementing Travel Diary Surveys with Twitter Data: Application of Text Mining Techniques on Activity Location, Type and Time

Mojtaba Maghrebi · Alireza Abbasi · Taha Hossein Rashidi · Steven Travis Waller

42 citations0/0 files storedIEEE International Conference on Intelligent Transportation Systems (ITSC)

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Human Mobility and Location-Based Analysis Transportation Planning and Optimization Data Management and Algorithms

Abstract

A growing body of literature in social science has been devoted to extracting new information from social media to assist authorities in manage crowd projects. In this paper geolocation (or spatial) based information provided in social media is investigated to utilize intelligent transportation services. Further, the general trend of travel activities during weekdays is studied. For this purpose, a dataset consisting of more than 40,000 tweets in south and west part of the Sydney metropolitan area is utilized. After a data processing effort, the tweets are clustered into seven main categories using text mining techniques, where each category represents a type of activity including shopping, recreation, and work. Unlike the previous studies in this area, the focus of this work is on the content of the tweets rather than only using geotagged data or sentiment analysis. Beside activity type, temporal and spatial distributions of activities are used in the classification exercise. Categories are mapped to the identified regions within the city of Sydney across four time slots (two peak periods and two off-peak periods). Each time slot is used to construct a network with nodes representing people, activities and locations and edges reflecting the association between the nodes. The constructed networks are used to study the trend of activities/locations in a typical working day.

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References

2007Understanding attitudes towards public transport and private car: A qualitative study 2012Using Social Media to Enhance Emergency Situation Awareness 2013Exploring temporal effects for location recommendation on location-based social networks 2013Understanding urban human activity and mobility patterns using large-scale location-based data from online social media 2014Urban activity pattern classification using topic models from online geo-location data 2012Emergency situation awareness from twitter for crisis management 2021Exploring Social-Historical Ties on Location-Based Social Networks

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2021Effects of the autonomous vehicle crashes on public perception of the technology 2018Utilizing Social Media in Transport Planning and Public Transit Quality: Survey of Literature 2021Feasibility of estimating travel demand using geolocations of social media data 2018Geolocation prediction in social media data using text analysis: A review 2016Transportation application of social media: Travel mode extraction 2020Recent Progress in Activity-Based Travel Demand Modeling: Rising Data and Applicability

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