The influence of zonal configurations on macro-level crash modeling

ABSTRACT

This study investigated the impacts of zonal configurations on macro-level traffic safety analysis for crashes of different severity levels. Bayesian multivariate Poisson-lognormal models with multivariate conditional auto-regressive priors were developed to account for the spatial autocorrelation between adjacent geographical units and correlations among crash types of four ordinal severity levels, i.e. fatality, severe injury, slight injury and no injury. For the purpose of evaluating the effects of zonal configurations on macro-level traffic safety analysis, the proposed model was calibrated using crash data of four types of geographical units, i.e. block group, traffic analysis zone, census tract and zip code tabulation area, in Hillsborough County of Florida. The study empirically revealed the extensive presence and the significance of MAUP in macro-level safety analysis based on the existing zonal configurations. It gave out a warning and encouraged more research efforts on rational application of macroscopic safety analysis with different zonal configurations.

KEYWORDS:

• Modifiable areal unit problem
• macro-level traffic safety analysis
• geographical configuration
• crash severity

Acknowledgements

We would like to thank Dr. Mohamed Abdel-Aty at the University of Central Florida and the Florida Department of Transportation for providing the data.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was jointly supported by the Joint Research Scheme of National Natural Science Foundation of China/Research Grants Council of Hong Kong [grant number: 71561167001 and N_HKU707/15], the Natural Science Foundation of China [grant number: 71371192], and the Research Committee of the Hong Kong Polytechnic University [grant number: 1-ZE5V].