A Spatial Autoregressive Quantile Regression to Examine Quantile Effects of Regional Factors on Crash Rates

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.

Spatial Autoregressive Quantile Regression with Application on Open Unemployment Data

“…. , p. If the VIF value is less than 5, multicollinearity does not occur in the regression model (Yu et al, 2021) .…”

“…The development of SAR modelling on the quantile 𝜏th is specifically defined as follows (Lum and Gelfand, 2012) : The value of the spatial autoregressive coefficient in the SARQR model shows the magnitude of the spatial dependence between adjacent areas. The IVQR (Instrumental Variable Quantile Regression) method is used to estimate parameters in the SARQR model (Yu et al, 2021) . The assumptions used in estimating the parameters in SARQR model are as follows (Zhang et al, 2021a) :…”

The Construction of Unemployment Rate Model Using SAR, Quantile Regression, and SARQR Model

“…If the VIF value is less than 5, multicollinearity does not occur in the regression model (Yu et al, 2022).…”

“…Modeling for data containing spatial effects and outliers can be modeled using a quantile regression approach. The quantile regression method also has powerful robustness and flexibility for handling data with outliers and skewed distribution (Yanuar et al, 2019(Yanuar et al, , 2022Yanuar & Zetra, 2021; Pakistan Journal of Statistics and Operation Research Yu et al, 2022). A combination of SAR and quantile regression then resulting in the Spatial Autoregressive Quantile Regression (SARQR) method, can also be used to model the data which contain spatial effect and skewed distribution (Dai et al, 2020;Dai & Jin, 2021;Jin et al, 2016).…”

Division and Analysis of Accident-Prone Areas near Highway Ramps Based on Spatial Autocorrelation

“…For example, Khanh et al [38] proposed a method for determining the location of traffic accident black-spots by combining the kernel density estimation (KDE) algorithm and spatial autocorrelation analysis. Fan [39] used a spatial autoregressive quantile model to estimate how risk factors affect overall and fatal traffic accident rates. The results were expected to provide strategies for reducing accident rates and improving road safety.…”