Abstract
Spatial autocorrelation and skewed distribution are the most frequent issues in crash rate modelling analysis. Previous studies commonly focus on the spatial autocorrelation between adjacent regions or the relationships between crash rate and potentially risky factors across different quantiles of crash rate distribution, but rarely both. To overcome the research gap, this study utilizes the spatial autoregressive quantile (SARQ) model to estimate how contributing factors influence the total and fatal-plus-injury crash rates and how modelling relationships change across the distribution of crash rates considering the effects of spatial autocorrelation. Three types of explanatory variables, i.e., demographic, traffic networks and volumes, and land-use patterns, were considered. Using data collected in New York City from 2017 to 2019, the results show that: (1) the SARQ model outperforms the traditional quantile regression model in prediction and fitting performance; (2) the effects of variables vary with the quantiles, mainly classifying three types: increasing, unchanged, and U-shaped; (3) at the high tail of crash rate distribution, the effects commonly have sudden increases/decrease. The findings are expected to provide strategies for reducing the crash rate and improving road traffic safety.
Highlights
Road traffic accident causes serious property damage and casualties around the world.In the past decades, many studies have carried out considerable efforts in all kinds of aspects of traffic safety to reduce road crashes [1,2]
This study investigated how the regional factors influenced the crash rates changed cally,the when the quantile value changes the lowautoregressive tail to the high tail, the figure decreases with number of crash rates usingfrom a spatial quantile (SARQ)
Using available data collected in New York City from 2017–2019, the relationships signs of these parameters do not change across the quantiles
Summary
Many studies have carried out considerable efforts in all kinds of aspects of traffic safety to reduce road crashes [1,2]. Among these studies, one of the most attractive areas is to explore how various influential factors affect crash rates. To analyze the impacts of these contributing factors on crash rates, many micro-level analytic models, such as the random parametric Tobit and random-effects Tobit models, have been developed at the segment and intersection level of transportation networks [5,9]. The cross-sectional analyses are becoming increasingly attractive and have considerable potential to have an in-depth understanding of crash rates
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