Abstract
In this study, we present both data mining and information visualization techniques to identify accident-prone areas, most accident-prone time, day, and month. Also, we surveyed among volunteers to understand which visualization techniques help non-expert users to understand the findings better. Findings of this study suggest that most accidents occur in the dusk (i.e., between 6 and 7 pm), and on Fridays. Results also suggest that most accidents occurred in October, which is a popular month for tourism. These findings are consistent with social information and can help policymakers, residents, tourists, and other law enforcement agencies. This study can be extended to draw broader implications.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Amiruzzaman M (2018) Prediction of traffic-violation using data mining techniques. In: Proceedings of the future technologies conference. Springer, Berlin, pp 283–297
Bandyopadhyay S, Maulik U (2002) Genetic clustering for automatic evolution of clusters and application to image classification. Pattern Recogn 35(6):1197–1208
Boyles RP, Raman S (2003) Analysis of climate trends in North Carolina (1949–1998). Environ Int 29(2–3):263–275
Depaire B, Wets G, Vanhoof K (2008) Traffic accident segmentation by means of latent class clustering. Accident Anal Prevent 40(4):1257–1266
Ester M, Kriegel HP, Sander J, Xu X et al (1996) A density-based algorithm for discovering clusters in large spatial databases with noise. Kdd 96:226–231
Gårder PE (2004) The impact of speed and other variables on pedestrian safety in maine. Accident Anal Prevent 36(4):533–542
Kumar S, Toshniwal D (2015) A data mining framework to analyze road accident data. J Big Data 2(1):26
McComas J, MacKay M, Pivik J (2002) Effectiveness of virtual reality for teaching pedestrian safety. Cyber Psychol Behav 5(3):185–190
Mohamed MG, Saunier N, Miranda-Moreno LF, Ukkusuri SV (2013) A clustering regression approach: a comprehensive injury severity analysis of pedestrian-vehicle crashes in New York, US and Montreal, Canada. Safety Sci 54:27–37
Organization WH et al (2018) Global status report on road safety 2018: Summary. World Health Organization, Technical Report
Runfola CD, Von Holle A, Trace SE, Brownley KA, Hofmeier SM, Gagne DA, Bulik CM (2013) Body dissatisfaction in women across the lifespan: results of the unc-self and gender and body image (gabi) studies. Europ Eating Disorders Rev 21(1):52–59
Sauber-Schatz EK, Parker EM, Sleet DA, Ballesteros MF (2019) Road & traffic safety. In: 2020 yellow book (Jun 2019). https://wwwnc.cdc.gov/travel/yellowbook/2020/travel-by-air-land-sea/road-and-traffic-safety
Savolainen PT, Mannering FL, Lord D, Quddus MA (2011) The statistical analysis of highway crash-injury severities: a review and assessment of methodological alternatives. Accident Anal Prevent 43(5):1666–1676
Smith DF (2004) Traffic accidents and friday the 13th. Am J Psych 161(11):2140
Tian R, Yang Z, Zhang M (2010) Method of road traffic accidents causes analysis based on data mining. In: 2010 international conference on computational intelligence and software engineering, pp 1–4. IEEE
USGovernment: (Apr 2020), https://www.data.gov/
Xu R, Xu J, Wunsch DC (2012) A comparison study of validity indices on swarm-intelligence-based clustering. IEEE Trans Syst Man Cybern Part B (Cybern) 42(4):1243–1256
Yu X, Fernando B, Ghanem B, Porikli F, Hartley R (2018) Face super-resolution guided by facial component heatmaps. In: Proceedings of the European conference on computer vision (ECCV), pp 217–233
Acknowledgements
We would like to thank the Institute of Energy, Environment, Research, and Development (IEERD, UAP) and the University of Asia Pacific for financial support.
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Mashfiq Rizvee, M., Md Amiruzzaman, Islam, M.R. (2021). Data Mining and Visualization to Understand Accident-Prone Areas. In: Uddin, M.S., Bansal, J.C. (eds) Proceedings of International Joint Conference on Advances in Computational Intelligence. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-16-0586-4_12
Download citation
DOI: https://doi.org/10.1007/978-981-16-0586-4_12
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-0585-7
Online ISBN: 978-981-16-0586-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)