Citation

Atumo EA, Zhang B, Li H, Jiang X, Yu Q, Ambo TB, Hu Z. Transp. Res. Rec. 2023; 2677(7): 434-448.

Copyright

(Copyright © 2023, Transportation Research Board, National Research Council, National Academy of Sciences USA, Publisher SAGE Publishing)

DOI

10.1177/03611981231152471

PMID

unavailable

Abstract

Studies of spatial effects on road traffic safety are highly skewed in relation to aggregation units. As a result, the spatial effect of road traffic crashes at segment-level aggregation is rarely investigated. In light of that, this study investigates spatial spillover effects to determine the effects of independent variable changes initiated in one segment on the dependent variable of its neighboring segments. Additionally, the performance of the implemented models is evaluated under five different spatial weighting approaches. Spatial spillover effect of fatal and injury crashes aggregated at the segment level is estimated with the spatial lag of explanatory variables model under the general framework of Poisson and negative binomial models. The results indicate (i) spatial spillover effects are better modeled with spatial relation conceptualized by inverse distance spatial weighting schemes; (ii) spatial lag of explanatory variables under negative binomial and inverse distance spatial weighting provides the best model fitting; (iii) exogenous interactions among influence factors of fatal and injury crashes have a noteworthy effect in determining the crash frequency of neighboring segments. Convincingly, spatial spillover effect seems to significantly affect the results of conventional count modeling at the segment levels. To that effect, this study expects to provide empirical evidence and complement the existing literature on the impact of changes in explanatory variables initiated in a given segment on the safety of segments in proximity.

Language: en