A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Understanding Traffic Safety Culture in Washington Using Twitter Mining
Traffic safety culture has emerged as an important factor in support of acceptance of existing traffic safety policy and programs and as a contextual variable to define high-risk behaviors of drivers. However, it is challenging to understand people’s beliefs and attitudes that collectively influence traffic safety-related behaviors. The increasing popularity of social media platforms like Facebook, Twitter, blogs, and online forums have generated massive data that can be collected and used for intensive analysis of people’s perspectives on a certain topic. In this paper, we attempted to understand people’s perception of traffic safety in the state of Washington. To this end, we collected traffic safety-related tweet data over the past four years based on the generation of a set of keywords. The collected tweet data was then cleaned and reprocessed by the NLP (natural language processing) technique and following that, the LIWC (linguistic inquiry and word count) sentiment analysis procedure was applied to measure the public’s beliefs and attitudes towards importance of traffic safety, possibility of zero fatalities, usefulness of traffic law enforcement, and six types of high-risk behaviors including impairment driving, speeding, distracted driving, unrestrained vehicle occupants, teenage drivers, and older drivers. The present study that capitalizes on social media mining overcomes the limitations of the traditional survey method that is time-consuming and expensive. The generated information from this study is expected to facilitate uncovering of and development of solutions to overcome the barriers to prevention of fatal traffic accidents in Washington.
Understanding Traffic Safety Culture in Washington Using Twitter Mining
Traffic safety culture has emerged as an important factor in support of acceptance of existing traffic safety policy and programs and as a contextual variable to define high-risk behaviors of drivers. However, it is challenging to understand people’s beliefs and attitudes that collectively influence traffic safety-related behaviors. The increasing popularity of social media platforms like Facebook, Twitter, blogs, and online forums have generated massive data that can be collected and used for intensive analysis of people’s perspectives on a certain topic. In this paper, we attempted to understand people’s perception of traffic safety in the state of Washington. To this end, we collected traffic safety-related tweet data over the past four years based on the generation of a set of keywords. The collected tweet data was then cleaned and reprocessed by the NLP (natural language processing) technique and following that, the LIWC (linguistic inquiry and word count) sentiment analysis procedure was applied to measure the public’s beliefs and attitudes towards importance of traffic safety, possibility of zero fatalities, usefulness of traffic law enforcement, and six types of high-risk behaviors including impairment driving, speeding, distracted driving, unrestrained vehicle occupants, teenage drivers, and older drivers. The present study that capitalizes on social media mining overcomes the limitations of the traditional survey method that is time-consuming and expensive. The generated information from this study is expected to facilitate uncovering of and development of solutions to overcome the barriers to prevention of fatal traffic accidents in Washington.
Understanding Traffic Safety Culture in Washington Using Twitter Mining
Sujon, Mohhammad (author) / Dai, Fei (author)
Construction Research Congress 2020 ; 2020 ; Tempe, Arizona
Construction Research Congress 2020 ; 201-209
2020-11-09
Conference paper
Electronic Resource
English
MINING TWITTER FOR MARINE SPATIAL PLANNING - Soapbox
British Library Online Contents | 2013
| Engineering Index Backfile | 1938