Learning Drivers' Behavior From Social Networking. (November 2019)

• Record Type: Journal Article
• Authors: Li, Yueqing; Kaneria, Acyut; Qian, Chao; Craig, Brian
• Is Part Of: Proceedings of the Human Factors and Ergonomics Society ... Annual Meeting. Volume 63:Part 1(2019)
• Page Start: 1792
• Page End: 1796
• Publication Date: 2019-11
• Subjects: Human engineering -- Congresses; 620.8205
• DOI: 10.1177/1071181319631479
• Languages: English
• ISSNs: 1541-9312

Abstract

In today's fast developing civilization, transportation plays an important part in people's economic growth and daily activities. This study analyzes the driving behavior and accidents related to traffic accidents using Twitter tweets as a tool for text mining. Active users when encounter any traffic incidents, post instant messages on Twitter. Various analyses were performed on these tweets and was represented graphically using tableau analy-sis software and Rstudio. This method proved to be an effective and inexpensive method to study peoples' real time approach on traffic accident throughout the world. It proved to be a strong approach towards learning traf-fic accident behaviors.