Road Traffic Injury Prevention Using DBSCAN Algorithm - Chantamit-o-pas, Pattanapong; Pongpum, Weerakorn; Kongsaksri, Krisakorn (My University, 2020)
Machine learning has been used in innovation research for the last two decades. It is widely applied in decision making such as clustering, analysis, predicting, evaluating prognosis, and recommendation. The car accident often causes death or disability in most countries. Road accident victims usually have poor quality of life because of serious illness, long-term disability, which is a huge burden to their families and some eventually died. The behavior of driving on road is a major risk factor to road traffic. This research develops a mobile application that can notify the driver when there is a risk nearby. It focuses on Thailand and it is applied only to local cases. The dataset comes from Thai Road Safety Collaboration (ThaiRSC), which is a non-governmental organization that records a lot of daily accident cases. It uses the DBSCAN algorithm, a clustering technique, for road traffic injury prevention applied on the ThaiRSC’s dataset that focused on 3 districts of Bangkok, namely Ladkrbang, Pravet, Suan Lung, as well as all province in eastern Thailand. The outcomes of this research are beneficial in warning drivers if they are likely to encounter a road accident.
Keywords: Clustering; DBSCAN; Machine learning; Road accidents