TY - GEN
T1 - Chicago Traffic Collision Data Analysis Based on Multi-Component Analysis and Exploratory Data Analysis
AU - Zhang, Wenzhao
AU - Zhang, Shen
N1 - Publisher Copyright:
© 2020 ASCE.
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
KW - Exploratory data analysis
KW - Multivariate analysis.
KW - Traffic vehicle collision data
UR - https://www.scopus.com/pages/publications/85098267629
U2 - 10.1061/9780784483053.389
DO - 10.1061/9780784483053.389
M3 - 会议稿件
AN - SCOPUS:85098267629
T3 - CICTP 2020: Transportation Evolution Impacting Future Mobility - Selected Papers from the 20th COTA International Conference of Transportation Professionals
SP - 4684
EP - 4696
BT - CICTP 2020
A2 - Wei, Heng
A2 - Wang, Haizhong
A2 - Zhang, Lei
A2 - An, Yisheng
A2 - Zhao, Xiangmo
PB - American Society of Civil Engineers (ASCE)
T2 - 20th COTA International Conference of Transportation Professionals: Transportation Evolution Impacting Future Mobility, CICTP 2020
Y2 - 14 August 2020 through 16 August 2020
ER -