Efficient traffic management systems are especially necessary for large cities. Chicago is the third largest city in the United States. The city’s recent collision data is not optimistic, and citizens’ lives are at risk. Current research on the topic has some shortcomings, such as, only a few traffic vehicle collision models exist, the difficulty in applying related methods to vehicle collision data, and the complexity of the relationships between the myriad of variables. This paper analyzes Chicago traffic crash data from 2015 to 2019. Geographical location, multi-component, and exploratory data analyses are used to analyze the relationships of variables, i.e., collision date, road surface and weather conditions, and vehicle condition. The results showed that good driving conditions and fine weather rarely cause vehicle collisions; while, high alcohol content and improper vehicle speed has the opposite effect. This study offers meaningful contribution to the advancement of traffic vehicle collision data models.


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    Titel :

    Chicago Traffic Collision Data Analysis Based on Multi-Component Analysis and Exploratory Data Analysis


    Beteiligte:
    Zhang, Wenzhao (Autor:in) / Zhang, Shen (Autor:in)

    Kongress:

    20th COTA International Conference of Transportation Professionals ; 2020 ; Xi’an, China (Conference Cancelled)


    Erschienen in:

    CICTP 2020 ; 4684-4696


    Erscheinungsdatum :

    2020-12-09




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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