Public safety, especially the daily traffic accidentis concerned by the public. Previous studies have alreadydiscussed accident reasons associated with accidents statistically. There is amethod called Innovators Marketplace on Data Jackets created by ProfessorOhsawa. This method is used to externalize the value of data via stakeholders’requirement communication. This paper applied the solution from an IMDJworkshop to research this topic creatively.
This novel solution suggested to doanalysis on the combination of urban data and traffic accident rate to find theimpact factors to the traffic accident rate in the urban system. This paperused factor analysis, structure equation modeling and data mining to constructa theoretical frame for traffic accident rate analysis for urban data. Differentaccident indexes, such as total number of accident, fatality rate, injury rate,and casualty rate are combined to construct a traffic accident risk evaluationmodel. This paper chosen the urban data as the solution from IMDJ workshop,such as population structure information, vehicle information, road characters,public traffic system information, and the other kinds of data to explorefactor meaning, and to identify relationships between different factors. Itsegmented these urban data based on their categories, and determined accidentrisk for each section. By doing analysis on not only the original data but alsothe changing rate of these data each year, the result analytical results showedthat traffic accident rate on urban data could be described by the combinationof population structure, road characters, public traffic system and publicfacilities.
These four sections affects traffic accident rate significantlyduring the development of urban; however, the vehicle factor does not haveinfluence on traffic accident rate. And it proves the solution from IMDJworkshop is not only novel but also practical strongly. Making some solutionfrom IMDJ into reality, we will find another new way to affect the world.
