Hamiltonian Monte Carlo with Random Effect for Analyzing Cyclist Crash Severity

Mahdi Rezapour,Khaled Ksaibati

doi:10.3390/signals2030032

Abstract

Vulnerable traffic users, such as bikers and pedestrians, account for a significant number of fatalities on the roadways. Extensive research has been conducted in the literature review to identify factors to those crashes. Studying factors to those crashes is especially important in the Western state in the US, due to one of the highest fatality rates in the nation and its unique geographic conditions. The first step in identifying factors to the severity of cyclist crashes is to find the underlying factors to that type of crash, while accounting for the heterogeneity in the dataset. Various techniques such as mixed parameter or mixed effect models have been employed in the literature to account for the heterogeneity of the dataset. In the mixed effect model, often the random effect parameter has been assigned subjectively, and based on some attributes and engineering intuitions. Those assignments are expected to account for the heterogeneity in the dataset and enhancement of the model fit. However, a question might arise whether those factors could account for an optimum amount of the heterogeneity in the dataset. A more reasonable way might be to let the algorithm such as the finite mixture model (FMM) to identify those clusters based on parameters of the Gaussian model, means and covariance matrices of the dataset, and allocate each observation to the related clusters. Thus, in this study, to capture optimum amount of heterogeneity, first we implemented the finite mixture model in the context of maximum likelihood, due the label switching issue of the method in the context of the Bayesian method. After assignment of the parameters to the observation, the main method of Hamiltonian Monte Carlo (HMC) with random effect was implemented. The results highlighted a significant improvement in the model fit, in terms of Widely Applicable Information Criterion (WAIC). The results of this study highlighted factors such as older biker age, increased number of lanes, nighttime travelling, increased posted speed limit and driving while under emotional conditions are some factors contributing to an increased severity of bikers’ crash severity. Extensive discussion has been made regarding the methodological algorithms and model parameters estimations.

Highlights

The benefits of cycling could be summarized as the wellbeing of the population through developing a healthy and sustainable environment, which could be achieved, in turn, by prevention of obesity and the consequent enhancement of cardiorespiratory fitness [1]
The method of the finite mixture model (FMM) assumes that the dataset arises from data components
Various quantiles of sampled θ would be used after discarding the first N number of burning samples, highlighting certainty or uncertainty associated with parameters’ estimates

Summary

Introduction

The benefits of cycling could be summarized as the wellbeing of the population through developing a healthy and sustainable environment, which could be achieved, in turn, by prevention of obesity and the consequent enhancement of cardiorespiratory fitness [1]. The benefits of shifting from car to bicycle have been seen in terms of health effects through a decrease in air pollution emission, and greenhouse emissions, and an increase in physical activity [2]. The situation would be direr as an increase in the rate of cycling would increase the risk of involvement in traffic crashes, as well as the severity of those crashes. While about 10% of trips in the U.S have been made by walking or bicycling [4], those types of commuting accounts for about 14% of all traffic fatalities [5]

Methods

Results

Conclusion