Bayesian multivariate spatial modeling for crash frequencies by injury severity at daytime and nighttime in traffic analysis zones

ABSTRACT

This paper develops a multivariate spatial model for the joint analysis of daytime and nighttime crash frequencies by injury severity in traffic analysis zones (TAZs). The model specification allows for spatial correlation across TAZs, heterogeneous effects specific to crash time and severity, and correlations across response types under a Bayesian multivariate conditional autoregressive framework. One hundred and thirty one TAZs in Hong Kong, China, with traffic crash, traffic flow, roadway network, and land use data for a one-year period are selected to calibrate the advocated model. Considerable spatial and heterogeneous effects are found for each type crash frequency. Significant correlations exist in the heterogeneous effects for various severity levels and those for daytime and nighttime. The Bayesian estimates of the regression coefficients reveal that there are significant inconsistencies in the set of factors contributing to zonal daytime and nighttime crashes at various severity levels.

KEYWORDS:

• Transportation safety planning
• zonal transportation safety
• spatial correlation
• multivariate spatial model
• conditional autoregressive prior
• bayesian inference

Acknowledgments

This work was supported by the International Science & Technology Cooperation Program of China under Grant [No. 2017YFE0134500], the National Natural Science Foundation of China under Grant [Nos. 71801095, 71671100, and 52072214], the Science and Technology Program of Guangzhou, China under Grant [No. 202102020781], and the Fundamental Research Funds for the Central Universities under Grant [No. 2020ZYGXZR007].

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the Fundamental Research Funds for the Central Universities [2020ZYGXZR007]; Guangzhou Municipal Science and Technology Project [202102020781]; International Science and Technology Cooperation Programme [2017YFE0134500]; National Natural Science Foundation of China [52072214]; National Natural Science Foundation of China [71801095]; National Natural Science Foundation of China [71671100].