| 研究実績の概要 |
The main finding of this study is the influential factors in traffic accidents. Those independent variables are found to be influential; that is, number of shops, number of elementary schools, number of sport facilities, number of intersections, number of people commuting to work by train, automobile, bicycle, and on foot, number of rainy days, number of snowy days, urban area, and total length of roads are statistically significant in the traffic accidents models on workday; and number of shops, number of department stores, number of intersections, number of residents, number of rainy days, number of snowy days, urban area, and total length of roads are significant in the traffic accidents models on weekend and holiday.After the identifying factors among land use, climate, road and demographic variables, it is necessary to develop models that consider temporalchanges. Land use factors, which rarely change in short period of time, and demographic factors, which change also in a longer time period should be considered. In addition, it is necessary to include factors which change daily or in a shorter time period using real time climate data and road related data such as traffic volume and speed to alert users when they enter high risk zones. By developing this approach, it could improve the road safety and reduce traffic accidents; it is anticipated that heavy or fatal accidents will decrease and that awareness of road users will be raised at all time and all places.
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