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Discovering potential traffic risks in Japan using a supervised learning approach | IEEE Conference Publication | IEEE Xplore

Discovering potential traffic risks in Japan using a supervised learning approach


Abstract:

Traffic accidents are caused by many different factors, such as the status of drivers, climate, and road conditions. Among them, we focus on risk factors derived from roa...Show More

Abstract:

Traffic accidents are caused by many different factors, such as the status of drivers, climate, and road conditions. Among them, we focus on risk factors derived from road facilities and connectivity relations. We collected statistics regarding these factors for risky and safe roads in Tokyo and employed supervised learning to train linear classifiers with two models: a L2-logistic regression model, and a combinatorial Boolean model. We found different aspects of traffic risk and concluded that combinatorial Boolean models are more effective to discover useful knowledge pertaining to risk.
Date of Conference: 11-14 December 2017
Date Added to IEEE Xplore: 15 January 2018
ISBN Information:
Conference Location: Boston, MA, USA

References

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