Study of Machine Learning Techniques on Accident Data
| Autor: | Rubyat Tasnuva Hasan, Zakaria Shams Siam, Ankit Dev, Rashedur M. Rahman, Sumaia Islam Alita, Mustafizur Rahaman, Soumik Sarker Anik |
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| Rok vydání: | 2020 |
| Předmět: |
050210 logistics & transportation
Road accident Computer science business.industry 05 social sciences Agglomerative hierarchical clustering 02 engineering and technology Machine learning computer.software_genre Random forest Accident (fallacy) Road crash Homogeneous 0502 economics and business 0202 electrical engineering electronic engineering information engineering Damages 020201 artificial intelligence & image processing Artificial intelligence Severity level business computer |
| Zdroj: | Advances in Computational Collective Intelligence ISBN: 9783030631185 ICCCI (CCIS Volume) |
| DOI: | 10.1007/978-3-030-63119-2_3 |
| Popis: | Road crash is one of the major burning issues for Bangladesh. There are several factors that are responsible for occurring road crashes. If we can understand the causes and predict the severity level of a particular type of accident upfront, we can take necessary steps in the proper time to lessen the damages. In this study, we have built some predictive models of different homogeneous road crash groups of Bangladesh using machine learning methods that can predict that particular road crash severity level based on the environmental factors and road conditions. We have applied Agglomerative Hierarchical Clustering to find different clusters of road crashes and then applied Random Forest technique to extract the significant predictors of each cluster and then applied C5.0 to build predictive models of each cluster. Finally we have discussed the patterns of fatal and non-fatal accidents of Bangladesh through rule generation technique. |
| Databáze: | OpenAIRE |
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