Abstract
Motor vehicle is the backbone of the modern transportation system worldwide. However, the excessive number of motor vehicles tend to cause traffic accidents leading to numerous casualties. Analyzing existing works on this area, this study has identified prime reasons behind traffic accidents and casualties. They include driving over the speed limit, Age of drivers and pedestrians, environmental condition, location and road types. It has also reviewed and identified several data visualization methods and visualization techniques that have been proposed by many researchers. The objective of this research endeavour is to identify the factors behind traffic accidents, determine the techniques that are used to visualize data, develop a dashboard using data visualization tools to visualize traffic accident trend and to evaluate the functionality of the dashboard which is developed on United Kingdom’s (UK) traffic accident dataset from 2014 to 2016. Upon performing data cleaning, pre-processing and filtering, the raw data has been converted into cleaned, filtered and processed data to create a coherent and properly linked data model. Then, using the Power BI visualization tool, various interactive visualizations have been produced that illustrated several significant trends in accident and casualties. The visualization trend revealed that between 2014 to 2016, majority of the accidents in the UK occurred in the urban area, in the single carriageway, on the dry road surface, under the daylight with fine weather, and when the speed limit was below 30 mph. This research may assist UK’s traffic management authority to identify the underlying factors behind the traffic accident and to detect the traffic accident and casualty trend in order to take necessary steps to reduce casualties in traffic accidents.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Triguero, I., Figueredo, G.P., Mesgarpour, M., Garibaldi, J.M., John, R.I.: Vehicle incident hot spots identification: an approach for big data. In: 2017 IEEE Trustcom/BigDataSE/ICESS, pp. 901–908 (2017)
Tulu, G.S., Washington, S., Haque, M.M., King, M.J.: Injury severity of pedestrians involved in road traffic crashes in Addis Ababa, Ethiopia. J. Transp. Saf. Secur. 9(sup1), 47–66 (2017)
Ram, T., Chand, K.: Effect of drivers’ risk perception and perception of driving tasks on road safety attitude. Transp. Res. Part F Traffic Psychol. Behav. 42, 162–176 (2016)
Masuri, M.G., Isa, K.A.M., Tahir, M.P.M.: Children, youth and road environment: road traffic accident. Asian J. Environ. Behav. Stud. 2(4), 13–20 (2017)
Guo, M., Wei, W., Liao, G., Chu, F.: The impact of personality on driving safety among Chinese high-speed railway drivers. Accid. Anal. Prev. 92, 9–14 (2016)
Liao, Y., Li, S.E., Wang, W., Wang, Y., Li, G., Cheng, B.: Detection of driver cognitive distraction: a comparison study of stop-controlled intersection and speed-limited highway. IEEE Trans. Intell. Transp. Syst. 17(6), 1628–1637 (2016)
Goniewicz, K., Goniewicz, M., Pawłowski, W., Fiedor, P.: Road accident rates: strategies and programmes for improving road traffic safety. Eur. J. Trauma Emerg. Surg. 42(4), 433–438 (2016)
Omranian, E., Sharif, H., Dessouky, S., Weissmann, J.: Exploring rainfall impacts on the crash risk on Texas roadways: a crash-based matched-pairs analysis approach. Accid. Anal. Prev. 117, 10–20 (2018)
Lin, L., Wang, Q., Sadek, A.W.: A combined M5p tree and hazard-based duration model for predicting urban freeway traffic accident durations. Accid. Anal. Prev. 91, 114–126 (2016)
Xi, J., Zhao, Z., Li, W., Wang, Q.: A traffic accident causation analysis method based on AHP-Apriori. Procedia Eng. 137, 680–687 (2016)
Bergel-Hayat, R., Debbarh, M., Antoniou, C., Yannis, G.: Explaining the road accident risk: weather effects. Accid. Anal. Prev. 60, 456–465 (2013)
Mohamed, M.G., Saunier, N., Miranda-Moreno, L.F., Ukkusuri, S.V.: A clustering regression approach: a comprehensive injury severity analysis of pedestrian–vehicle crashes in New York, US and Montreal, Canada. Saf. Sci. 54, 27–37 (2013)
Ferenchak, N.N.: Pedestrian age and gender in relation to crossing behavior at midblock crossings in India. J. Traffic Transp. Eng. Engl. Ed. 3(4), 345–351 (2016)
Zhang, W., Wang, K., Wang, L., Feng, Z., Du, Y.: Exploring factors affecting pedestrians’ red-light running behaviors at intersections in China. Accid. Anal. Prev. 96, 71–78 (2016)
Clarke, D.D., Ward, P., Bartle, C., Truman, W.: Killer crashes: fatal road traffic accidents in the UK. Accid. Anal. Prev. 42(2), 764–770 (2010)
Vedagiri, P., Kadali, B.R.: Evaluation of pedestrian–vehicle conflict severity at unprotected midblock crosswalks in India. Transp. Res. Rec. J. Transp. Res. Board 2581, 48–56 (2016)
Shirazi, M.S., Morris, B.T.: Looking at intersections: a survey of intersection monitoring, behavior and safety analysis of recent studies. IEEE Trans. Intell. Transp. Syst. 18(1), 4–24 (2017)
Huang, H., Zhou, H., Wang, J., Chang, F., Ma, M.: A multivariate spatial model of crash frequency by transportation modes for urban intersections. Anal. Methods Accid. Res. 14, 10–21 (2017)
Zhang, G., Yau, K.K.W., Zhang, X., Li, Y.: Traffic accidents involving fatigue driving and their extent of casualties. Accid. Anal. Prev. 87, 34–42 (2016)
Green, C.P., Heywood, J.S., Navarro, M.: Traffic accidents and the London congestion charge. J. Public Econ. 133, 11–22 (2016)
Smith, A.C.: Spring forward at your own risk: daylight saving time and fatal vehicle crashes. Am. Econ. J. Appl. Econ. 8(2), 65–91 (2016)
Abela, A.: Choosing a good chart. In: The Extreme Presentation(tm) Method, 06 Sept 2006
Gulbis, J.: Data visualization – how to pick the right chart type? In: eaziBI (2016)
Oetting, J.: Data visualization 101: how to choose the right chart or graph for your data. In: HubSpot, 31 Aug 2016
Yu, H., Liu, P., Chen, J., Wang, H.: Comparative analysis of the spatial analysis methods for hotspot identification. Accid. Anal. Prev. 66, 80–88 (2014)
Denham, B., Eguakun, G., Quaye, K.: GeoTAIS: an application of spatial analysis for traffic safety improvements on provincial highways in Saskatchewan (2011)
Dales, J.: M25 road traffic accidents, 02 Feb 2016. http://community.powerbi.com/t5/Best-Report-Contest/M25-Road-Traffic-Accidents/m-p/17210#M14. Accessed 20 Dec 2017
O’Donnel, M.: Exploring NYC Vehicle Crash Data in Tableau. InterWorks, Inc., 26 Aug 2015
Acknowledgments
The authors would like to thanks Advanced Informatic School (AIS), Universiti Teknologi Malaysia (UTM) for the support in publishing the findings. This work was financially supported through research grant of RUG UTM under vote number 14H76
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Sakib, A., Ismail, S.A., Sarkan, H., Azmi, A., Mohd Yusop, O. (2019). Analyzing Traffic Accident and Casualty Trend Using Data Visualization. In: Saeed, F., Gazem, N., Mohammed, F., Busalim, A. (eds) Recent Trends in Data Science and Soft Computing. IRICT 2018. Advances in Intelligent Systems and Computing, vol 843. Springer, Cham. https://doi.org/10.1007/978-3-319-99007-1_9
Download citation
DOI: https://doi.org/10.1007/978-3-319-99007-1_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-99006-4
Online ISBN: 978-3-319-99007-1
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)