A semi-nonparametric Poisson regression model for analyzing motor vehicle crash data

Abstract

This paper develops a semi-nonparametric Poisson regression model to analyze motor vehicle crash frequency data collected from rural multilane highway segments in California, US. Motor vehicle crash frequency on rural highway is a topic of interest in the area of transportation safety due to higher driving speeds and the resultant severity level. Unlike the traditional Negative Binomial (NB) model, the semi-nonparametric Poisson regression model can accommodate an unobserved heterogeneity following a highly flexible semi-nonparametric (SNP) distribution. Simulation experiments are conducted to demonstrate that the SNP distribution can well mimic a large family of distributions, including normal distributions, log-gamma distributions, bimodal and trimodal distributions. Empirical estimation results show that such flexibility offered by the SNP distribution can greatly improve model precision and the overall goodness-of-fit. The semi-nonparametric distribution can provide a better understanding of crash data structure through its ability to capture potential multimodality in the distribution of unobserved heterogeneity. When estimated coefficients in empirical models are compared, SNP and NB models are found to have a substantially different coefficient for the dummy variable indicating the lane width. The SNP model with better statistical performance suggests that the NB model overestimates the effect of lane width on crash frequency reduction by 83.1%.

The Effects of Traffic Composition on Freeway Crash Frequency by Injury Severity: A Bayesian Multivariate Spatial Modeling Approach

“…, (4) , and (5) are the traffic volumes for vehicle categories 1-5, respectively, on freeway segment in 2014. As in the previous studies [1,2], the daily vehicle kilometers traveled (DVKT), measured as the product of AADT and segment length, are used as the crash exposure variable.…”

“…The percentage of the 5th vehicle category, ℎ (5) , is set as the reference case. Table 2 illustrates the definitions and descriptive statistics of the variables used in the model development.…”

“…To reveal crash occurrence mechanism, rank hotspots for safety improvement, and evaluate the safety benefits of countermeasures, crash prediction models (also called "safety performance functions") are usually developed to link traffic volume, roadway attributes (e.g., length, speed limit, number of lanes, median and shoulder width, vertical grade, and horizontal curvature), and weather conditions (e.g., visibility, temperature, and precipitation) to the crash frequencies on freeway segments [2,4,5].…”

Analysis of Nonsevere Crashes on Two- and Four-Lane Urban and Rural Highways: Effects of Wet Pavement Surface Condition

“…The study found that the lognormal hurdle model with flexible scale parameter is superior to the other models. More recently, Ye et al [20] proposed seminonparametric (SNP) Poisson model which accounts for unobserved heterogeneity in crash data. Using ten years of crash data collected from California rural roads, the authors suggested that the overall performance of SNPPoisson model is better than the traditional negative binomial model.…”

A Multivariate Modeling Analysis of Commuters’ Non-Work Activity Allocations in Xiaoshan District of Hangzhou, China

“…On the contrary, public transit, with uncertain waiting time and fixed routes, has a limitation to undertake multiple activities in a tour [21,22]. Therefore, the complex trip chains may increase the dependence of travelers on automobiles, which leads to the problems related to auto route choice and optimization, as well as transportation safety [23][24][25][26][27][28][29][30][31]. In order to verify this conclusion, this paper also takes the mode choice of commuters as one of the independent variables.…”