Citation

Dai Z, Wang X. J. Traffic Transp. Eng. Engl. Ed. 2022; 9(6): 978-990.

Copyright

(Copyright © 2022, Periodical Offices of Chang'an University, Publisher Elsevier Publishing)

DOI

10.1016/j.jtte.2022.11.002

PMID

unavailable

Abstract

The high risk of injury resulting from non-motorized vehicle (NMV) crashes has created the goal of using the 3E strategy to comprehensively improve NMV safety. Traditional safety improvement methods identify hot zones generally by crash frequency or density, which is effective for roadway engineering improvements but neglects characteristics related to other improvements such as safety education. As safety education would be more effective if targeted at the residences of crash-involved road users, the traditional approach to hot zones may therefore provide biased results for such alternative countermeasures. After confirming that 77.17% of NMV crashes occurred outside the involved riders' areas of residence, this study compared the differences between the locations of crashes and the residences of NMV crash-involved riders in safety influencing factors and hot zone identification. A Poisson lognormal bivariate conditional autoregressive (PLN-BCAR) model was developed to account for potential correlations between crashes and involved riders. The model was compared with the univariate Poisson lognormal conditional autoregressive (UPLN-CAR) model and the bivariate Poisson lognormal conditional autoregressive (BPLN-CAR) model; the PLN-BCAR model outperformed the other two models in its better interpretation of the influencing factors and its more efficient parameter estimation. Model results indicated that crashes were mainly associated with roadway and land use characteristics, while involved road users were mainly associated with socioeconomic and land use characteristics. The potential for safety improvement method was adopted to identify hot zones for countermeasure implementation.

RESULTS showed over 60% of the identified hot zones were inconsistent: they needed improvement in either engineering or education but not both. These findings can help target the type of improvement to better utilize resources for NMV safety.

Language: en

Keywords

Bivariate spatial model; Hot zone identification; Macro safety analysis; Non-motorized vehicle safety; Potential for safety improvement