Abstract
The newly emerged social media data can collect large quantities of location, time information, as well as the fully detailed text messages, which in turn contribute to existing transportation studies. With the wide spread of mobile device, information acquired from social media appears to be easier and larger than the traditional data collection methods and the related topics cover a wide range of transportation-related events.
This chapter uses one of the social media tools: Twitter to demonstrate the promises of social media in complementing traditional transportation studies. Three major applications in transportation research are examined: traffic event detection, human mobility exploration, and trip purpose and travel demand forecasting. In these applications, we detail the process how to leverage the GPS information to extract displacement; how to automatically extract topics from text messages; how to forecast travel demands toward a social event. The state-of-the-art methods are employed to process the Big Data of social media and the results show the advantages as well as the deficiencies of social media in transportation research and applications.
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This study was partially supported by the National Science Foundation award CMMI-1637604.
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Zhang, Z., He, Q. (2019). Social Media in Transportation Research and Promising Applications. In: Ukkusuri, S., Yang, C. (eds) Transportation Analytics in the Era of Big Data. Complex Networks and Dynamic Systems, vol 4. Springer, Cham. https://doi.org/10.1007/978-3-319-75862-6_2
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