A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Interactions between the built and socio-economic environment and driver demographics: spatial econometric models of car crashes in the Columbus Metropolitan Area
This research analyzes car crashes resulting from the interactions between (1) the characteristics of the built and socio-economic environment where the crashes take place and (2) the gender and age of the driver at fault. Crashes are classified in terms of seriousness (fatalities/injuries, property damages only) and driver demographics. Data are drawn for the Central Ohio Region over 2006–2011 from the multiple files of the crash database of the Ohio Department of Public Safety. These data are aggregated over Traffic Analysis Zones (TAZ). Additional data include socio-economic, land-use, public transit, road network, and other locational/physical factors, also specified at the TAZ level. Regression analysis is used to explain the numbers of crashes in each of 12 groups. Three age groups are considered: young (15–24), adult (25–64), and older (65+). Spatial autocorrelation effects are tested and corrected by estimating spatial econometric models. The implications of the results for transportation safety policy are discussed.
Interactions between the built and socio-economic environment and driver demographics: spatial econometric models of car crashes in the Columbus Metropolitan Area
This research analyzes car crashes resulting from the interactions between (1) the characteristics of the built and socio-economic environment where the crashes take place and (2) the gender and age of the driver at fault. Crashes are classified in terms of seriousness (fatalities/injuries, property damages only) and driver demographics. Data are drawn for the Central Ohio Region over 2006–2011 from the multiple files of the crash database of the Ohio Department of Public Safety. These data are aggregated over Traffic Analysis Zones (TAZ). Additional data include socio-economic, land-use, public transit, road network, and other locational/physical factors, also specified at the TAZ level. Regression analysis is used to explain the numbers of crashes in each of 12 groups. Three age groups are considered: young (15–24), adult (25–64), and older (65+). Spatial autocorrelation effects are tested and corrected by estimating spatial econometric models. The implications of the results for transportation safety policy are discussed.
Interactions between the built and socio-economic environment and driver demographics: spatial econometric models of car crashes in the Columbus Metropolitan Area
Lee, Dongkwan (author) / Guldmann, Jean-Michel (author) / von Rabenau, Burkhard (author)
International Journal of Urban Sciences ; 22 ; 17-37
2018-01-02
21 pages
Article (Journal)
Electronic Resource
English
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