Abstract
This paper mainly deals with analyzing the road accident properly on each parameter/factor that causes accidents, i.e., speed of the vehicle, road’s surface conditions, weather, age of the driver, type of road junctions, etc., at the city, district, state/UT’s level and try to identify black spot areas and various flaws in road infrastructures. Road accidents are emerging as one of the major life-threatening causes in various countries. It was also claimed that roads are becoming deadlier than AIDS due to emerging technologies that are making vehicle average speed faster than ever before and also roads are one of the major causes of death of youngsters and thus affect the economic development of the country very badly. Our analysis showed us that Friday is the day in which most of the accidents occurred, accidents in which motorcycle is involved are more deadly than that of a light motor vehicle, and also, various results show us that men are mostly the victims of accidents than women. We did this analysis because there is a need to wake government agencies in order to remove these flaws also because saving lives is one of the most important duties of mankind, and we can also prevent million-dollar loss not only per individual but also at the national level. We can also give our next generation safer roads as it was predicted that by 2025 0.2 million deaths will be occurring yearly. Hence, this paper mainly brings light on the main factors causing accidents and areas of improvement by identifying accident-prone areas.
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Acknowledgements
We are very thankful to www.Kaggle.com because without it we do not get such a valuable dataset of the UK, i.e., vehicle and road separately that helped us analyze them separately.
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Tiwari, M., Nagar, P., Arya, G., Chauhan, S.S. (2022). Road Accident Analysis Using ML Classification Algorithms and Plotting Black Spot Areas on Map. In: Sharma, D.K., Peng, SL., Sharma, R., Zaitsev, D.A. (eds) Micro-Electronics and Telecommunication Engineering . ICMETE 2021. Lecture Notes in Networks and Systems, vol 373. Springer, Singapore. https://doi.org/10.1007/978-981-16-8721-1_64
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