Deriving the Traveler Behavior Information from Social Media: A Case Study in Manhattan with Twitter

TL;DR

This study demonstrates how Twitter's geo-tagged data can be systematically used to analyze traveler behavior patterns in Manhattan, revealing insights into individual and aggregated travel dynamics that complement traditional traffic data.

Contribution

The paper introduces a systematic method to extract travel behavior information from Twitter data and validates its effectiveness in capturing patterns comparable to conventional surveys.

Findings

Twitter users include residents and tourists, providing diverse travel data.

Travel patterns derived from Twitter align with traditional traffic flow patterns.

Potential for identifying origin-destination patterns using social media data.

Abstract

Social media platforms, such as Twitter, provide a totally new perspective in dealing with the traffic problems and is anticipated to complement the traditional methods. The geo-tagged tweets can provide the Twitter users' location information and is being applied in traveler behavior analysis. This paper explores the full potentials of Twitter in deriving travel behavior information and conducts a case study in Manhattan Area. A systematic method is proposed to extract displacement information from Twitter locations. Our study shows that Twitter has a unique demographics which combine not only local residents but also the tourists or passengers. For individual user, Twitter can uncover his/her travel behavior features including the time-of-day and location distributions on both weekdays and weekends. For all Twitter users, the aggregated travel behavior results also show that the time-of-day travel patterns in Manhattan Island resemble that of the traffic flow; the identification of OD pattern is also promising by comparing with the results of travel survey.