| 研究実績の概要 |
Summary of research achievements
- We successfully setup a collaborative research with our counterparts.- We successfully setup our cloud server and develop a script to process the data use for this research.- We concluded the data structure, sampling scheme, and identify possible risk factors use for this research.- We have studied and explored several analysis models and get our first results to determine the factors that highly related to traffic accidents. Our findings include:
Results of GLMs (Poisson regression, NB regression), and zero-inflated models (ZIP, ZINB) of traffic accident in weekday/weekend and holiday in Tokyo using Land use data such as Densely inhabited area map, urban area map, land use map, elementary school counts POI data including number of banks,convenience store and shops, department stores, entertainment businesses, hospitals, high schools and universities, sport facilities, railway stations, tourist attractions Road data such as traffic volume, traffic speed, number of intersections, length of roads Note: the parameters have been selected individually for each model to get the best result Comparison between nested and non-nested models found the significance of zero-inflated models, and NB regression to have superiority over Poisson regression.The same data analysis for 32 other prefectures namely Hokkaido, Aomori, Miyagi, Fukushima, Ibaraki, Gunma, Saitama, Chiba, Kanagawa, Niigata, Ishikawa, Fukui, Nagano, Shizuoka, Aichi, Mie, Shiga, Kyoto, Osaka, Hyogo, Nara, Wakayama, Tottori, Shimane, Okayama, Tokushima, Kagawa, Kochi.
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