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.
Get full access to this article
View all access options for this article.
References
1.
AbugessaisaI. (2008). Knowledge Discovery in Road Accidents Database Integration of Visual and Automatic Data Mining Methods. International Journal of Public Information Systems, 1, 59-85.
2.
BeshahT.HillS. (2010). Mining Road Traffic Accident Data to Improve Safety: Role of Road-Related Factors on Accident Severity in Ethiopia. AAAI Spring Symposium: Artificial intelligence, 14-19.
3.
D'AndreaE.DucangeP.LazzeriniB.MarcelloniF. (2015). Real-Time Detection of Traffic From Twitter Stream Analysis. IEEE Transactions on Intelligent Transportation Systems IEEE Trans. Intell. Transport. Syst. Intelligent Transportation Systems, 16(4), 2269-2283.
4.
FogueM.GarridoP.MartinezF. J.CanoJ.-C.CalafateC. T.ManzoniP. (2012). Using Data Mining and Vehicular Networks to Estimate the Severity of Traffic Accidents. Management Intelligent Systems, AISC, 171, 37-46.
5.
GostinL. O.JacobsonP. D. (2010). Reducing Distracted Driving: Regulation and Education to Avert Traffic Injuries and Fatalities. Georgetown University Faculty Publications, 303(14), 1419-1420.
6.
GuY.QianZ.ChenF. (2016). From Twitter to Detector: Real-time Traffic Incident Detection Using Social Media Data. Transportation Research Part C, 67, 321–342.
7.
HeW.ZhaS.LiL. (2013). Social Media Competitive Analysis and Text Mining: A Case Study in the Pizza Industry. International Journal of Information Management, 33(3), 464-472.
8.
KumarS.ToshniwalD. (2016). A Data Mining Approach to Characterize Road Accident Locations. J. Mod. Transport., 24(1), 62-72.
9.
QiG.DuY.WuJ.XuM. (2015). Leveraging Longitudinal Driving Behaviour Data with data Mining Techniques for Driving Style Analysis. IET Intelligent Transport System, 9(8), 792-801.
10.
SarkarS.VinayS.MaitiJ. (2016). Text Mining based Safety Risk Assessment and Prediction of Occupational Accidents in a Steel Plant. International Conference on Computational Techniques in Information and Communication Technologies (ICCTICT). New Delhi, India: IEEE.
11.
ShanthiS.RamaniG. R. (2012). Feature Relevance Analysis and Classification of Road Traffic Accident Data through Data Mining Techniques. Proceedings of the World Congress on Engineering and Computer Science, 1.
TsengW.-S.NguyenH.LiebowitzJ.AgrestiW. (2005). Distraction and Motor Vehicle Accidents: Data Mining Application on Fatality Analysis Report System (FARS) data files. Industrial management and Data Systems, 105(9), 1188-1205.
ZhangZ.HeQ.GaoJ.NiM. (2018). A Deep Learning Approach for Detecting Traffic Accidents from Social Media Data. Transportation Research Part C: Emerging Technologies, 86, 580
