01660404

Component

Reliable data on travel behavior is a prerequisite for any kind of urban and transportation planning process. In large cities, with a significant number of tourists, conventional travel surveys are difficult to conduct as tourists are the most dynamic population group whose size and travel choices change rapidly compared to the residents. Ubiquitous uses of social media platforms in smartphones have created a tremendous opportunity to gather digital traces of travelers at a large scale. In this paper, we present a framework on how to use location-based data from social media to gather and analyze travel behavior of tourists. We have collected data of about 67,000 users from Twitter using its search interface for the State of Florida. We first propose several filtering steps to create a reliable sample from the collected Twitter data. Then a rule-based classification technique is developed to classify tourists and residents from user coordinates. The accuracy of the proposed classifier has been compared against the state-of-the-art classification methods. Finally, different clustering methods have been used to find the spatial patterns of destination choices of tourists. Promising results have been found from the output clusters as they reveal most popular tourist spots as well as some of the emerging tourist attractions in Florida. Proposed filtering, identification, and clustering techniques will be significantly useful for building individual-level travel behavior models based on social media data.

This paper was sponsored by TRB committee ADB20 Standing Committee on Effects of Information and Communication Technologies (ICT) on Travel Choices.

18-03569

English

6p

2018

Transportation Research Board 97th Annual Meeting

Digital/other

Figures; Maps; References; Tables

Data analysis; Methodology; Origin and destination; Social media; Tourism; Tourists

Florida

Data and Information Technology; Planning and Forecasting; Transportation (General)

Transportation Research Board Annual Meeting 2018 Paper #18-03569

TRIS, TRB, ATRI

Jan 8 2018 10:53AM