Analytic Methods in Accident Research 
This is an RSS file. You can use it to subscribe to this data in your favourite RSS reader or to display this data on your own website or blog.
This is an RSS file. You can use it to subscribe to this data in your favourite RSS reader or to display this data on your own website or blog.Statistical and machine-learning methods for clearance time prediction of road incidents: A methodology review
Publication date: Available online 24 February 2020Source: Analytic Methods in Accident ResearchAuthor(s): Jinjun Tang, Lanlan Zheng, Chunyang Han, Weiqi Yin, Yue Zhang, Yajie Zou, Helai Huang (Source: Analytic Methods in Accident Research)
Source: Analytic Methods in Accident Research - February 25, 2020 Category: Accident Prevention Source Type: research
A hierarchical Bayesian multivariate ordered model of distracted drivers’ decision to initiate risk-compensating behaviour
This study aims to fill this gap by developing a comprehensive multivariate ordered model in Bayesian framework for risk-compensating behaviour of distracted drivers. The multivariate setting captures the common unobserved factors between multiple types of risk-compensating behaviour. In addition, an instrumental variable is employed to account for the endogeneity between crash risk and driving behaviour. To capture the varying effects of exogenous factors as well as varying propensity of initiating risk-compensating behaviour, the model is specified with grouped random parameters and random thresholds. This model is then ...
Source: Analytic Methods in Accident Research - February 8, 2020 Category: Accident Prevention Source Type: research
Temporal stability of driver injury severities in animal-vehicle collisions: A random parameters with heterogeneity in means (and variances) approach
This study investigates the determinants of driver injury severity in animal-vehicle collisions while systematically accounting for unobserved heterogeneity in the data by using three methodological approaches: mixed logit model, mixed logit model with heterogeneity in means, and mixed logit model with heterogeneity in means and variances. Using the data from Washington state from January 1, 2012 to December 31, 2016, a wide range of factors that could potentially affect the injury severity of drivers were examined. Moreover, the temporal stability and transferability of the models were investigated through a series of lik...
Source: Analytic Methods in Accident Research - February 5, 2020 Category: Accident Prevention Source Type: research
On the causal effect of proximity to school on pedestrian safety at signalized intersections: A heterogeneous endogenous econometric model
Publication date: Available online 28 January 2020Source: Analytic Methods in Accident ResearchAuthor(s): Shahram Heydari, Luis Miranda-Moreno, Adrian J. HickfordAbstractPedestrian safety in proximity to schools is a major concern of transportation authorities, local governments, and residents. In fact, several countermeasures (e.g., school-zone speed limits) are usually in place around schools to provide a safer environment, especially for school-age children. Two questions arise here: (i) are transportation facilities in proximity to schools truly safer than other facilities given a variety of implemented road safety int...
Source: Analytic Methods in Accident Research - January 29, 2020 Category: Accident Prevention Source Type: research
An Exploratory analysis of traffic accidents and vehicle ownership decisions using a random parameters logit model with heterogeneity in means
In this study, we present a methodology that addresses a driver’s decisions pertaining to a damaged car following a traffic accident. The choice set includes three alternatives: to replace the damaged car with a new one, replace the damaged car with a used one, and repair the damaged car. A random parameter (mixed) logit model with heterogeneity in the means is specified and estimated to gain more insight into the driver’s decision-making process following a traffic accident.The estimation results of the random parameter (mixed) logit model with heterogeneity in the means indicate that a wide range of explanatory varia...
Source: Analytic Methods in Accident Research - January 29, 2020 Category: Accident Prevention Source Type: research
Big Data, Traditional Data and the Tradeoffs between Prediction and Causality in Highway-Safety Analysis
Publication date: Available online 25 January 2020Source: Analytic Methods in Accident ResearchAuthor(s): Fred Mannering, Chandra R. Bhat, Venky Shankar, Mohamed Abdel-AtyAbstractThe analysis of highway accident data is largely dominated by traditional statistical methods (standard regression-based approaches), advanced statistical methods (such as models that account for unobserved heterogeneity), and data-driven methods (artificial intelligence, neural networks, machine learning, and so on). These methods have been applied mostly using data from observed crashes, but this can create a problem in uncovering causality sinc...
Source: Analytic Methods in Accident Research - January 25, 2020 Category: Accident Prevention Source Type: research
Incorporating Safety Reliability into Route Choice Model: Heterogeneous Crash Risk Aversions
In this study, a route choice model which accounts for both travelers’ safety concern—route safety reliability—and travel time concern is proposed. Route safety reliability (variability) is defined by the distribution of the travel crash risk cost (CRC) to represent the safety condition of travel routes. We further associate the travel safety variability due to stochastic crash occurrence with travelers’ crash risk aversion route choice behaviors, and postulate that travelers acquire the variability of route travel safety based on the past experience and factor it into their route choice in the form of an effective...
Source: Analytic Methods in Accident Research - January 25, 2020 Category: Accident Prevention Source Type: research
Driver Drowsiness Detection Using Mixed-effect Ordered Logit Model Considering Time Cumulative Effect
Publication date: Available online 25 January 2020Source: Analytic Methods in Accident ResearchAuthor(s): Xuxin Zhang, Xuesong Wang, Xiaohan Yang, Chuan Xu, Xiaohui Zhu, Jiaohua WeiAbstractDrowsy driving is one of the main causes of traffic crashes, a serious threat to road traffic safety. The effective early detection of a drowsiness state can help provide a timely warning for drivers, but previous studies have seldom considered the cumulative effect of drowsiness over time. The purpose of this study is therefore to establish a model to detect a driver's drowsiness level by considering individual differences combined with...
Source: Analytic Methods in Accident Research - January 25, 2020 Category: Accident Prevention Source Type: research
A Bivariate Bayesian Hierarchical Extreme Value Model for Traffic Conflict-based Crash Estimation
Publication date: Available online 22 January 2020Source: Analytic Methods in Accident ResearchAuthor(s): Lai Zheng, Tarek SayedAbstractThere are two main issues associated with traffic conflict-based crash estimation. First, there are several conflict indicators which were shown to inherently represent partial severity aspects of traffic events. Therefore, combining more than one conflict indicator can result in more comprehensive understanding on the underlying level of safety. Second, the conflict extremes characterized by the indicators, which are most related to crashes, are rare and heterogeneous in nature. These iss...
Source: Analytic Methods in Accident Research - January 22, 2020 Category: Accident Prevention Source Type: research
Hourly associations between weather factors and traffic crashes: non-linear and lag effects
In this study, we propose a novel distributed lag non-linear model (DLNM), integrated with case-crossover design, to evaluate the lag effect of weather on crash incidence. The proposed modelling framework could describe the non-linear relationship between weather and crash and the lag effects. Also, the possible over-dispersion and autocorrelation of the time-series weather and crash data can be controlled for. The model was estimated using an integrated meteorological, traffic and crash dataset in Hong Kong. For instances, high resolution data on temperature, humidity, rain intensity and wind speed in 1-hour interval was ...
Source: Analytic Methods in Accident Research - November 9, 2019 Category: Accident Prevention Source Type: research
A latent class approach for driver injury severity analysis in highway single vehicle crash considering unobserved heterogeneity and temporal influence
This study provided an insightful understanding of the time-varying effects of the significant factors on driver injury severity using marginal effect analysis, and the temporal indicators in the proposed model were found to enhance the model capability of temporal variation identification. (Source: Analytic Methods in Accident Research)
Source: Analytic Methods in Accident Research - November 9, 2019 Category: Accident Prevention Source Type: research
Do We Need Multivariate Modeling Approaches to Model Crash Frequency by Crash Types? A Panel Mixed Approach to Modeling Crash Frequency by Crash Types
Publication date: Available online 31 October 2019Source: Analytic Methods in Accident ResearchAuthor(s): Tanmoy Bhowmik, Shamsunnahar Yasmin, Naveen EluruAbstractIn safety literature, simulation-based multivariate framework is the most commonly employed approach for analyzing multiple crash frequency dependent variables. The current research effort contributes to literature on crash frequency analysis by suggesting an alternative and mathematically simpler approach for analyzing multiple crash frequency variables for the same study unit. The proposed recasts a multivariate distributional problem as a repeated measure univ...
Source: Analytic Methods in Accident Research - October 31, 2019 Category: Accident Prevention Source Type: research
Bayesian hierarchical modeling of traffic conflict extremes for crash estimation: A non-stationary peak over threshold approach
This study presents a Bayesian hierarchical model to estimate crashes from traffic conflict extremes in a non-stationary context. The model combines a peak over threshold approach with non-stationary thresholds in terms of regression quantiles and covariate-dependent parameters of the generalized Pareto distribution. The developed model was applied to estimate rear-end crashes from traffic conflicts of the same type collected from four signalized intersections. The conflicts were measured by the modified time to collision (MTTC) and traffic volume, shock wave area, average shock wave speed, and platoon ratio of each signal...
Source: Analytic Methods in Accident Research - October 18, 2019 Category: Accident Prevention Source Type: research
Bayesian hierarchical modeling traffic conflict extremes for crash estimation: A non-stationary peak over threshold approach
This study presents a Bayesian hierarchical model to estimate crashes from traffic conflict extremes in a non-stationary context. The model combines a peak over threshold approach with non-stationary thresholds in terms of regression quantiles and covariate-dependent parameters of the generalized Pareto distribution. The developed model was applied to estimate rear-end crashes from traffic conflicts of the same type collected from four signalized intersections. The conflicts were measured by the modified time to collision (MTTC) and traffic volume, shock wave area, average shock wave speed, and platoon ratio of each signal...
Source: Analytic Methods in Accident Research - October 2, 2019 Category: Accident Prevention Source Type: research
Modeling unobserved heterogeneity for zonal crash frequencies: a Bayesian multivariate random-parameters model with mixture components for spatially correlated data
This study applies mixture components in a multivariate random parameters spatial model for zonal crash counts. Three different modeling formulations are employed to demonstrate the effects of mixture components and spatial heterogeneity in the goodness-of-fit in a multivariate random parameter model. The models are built for injury (i.e., possible, non-incapacitating, incapacitating, and fatal injury) and non-injury crashes using the data from 738 traffic analysis zones (TAZs) in Hillsborough County of Florida during a three-year period. The Deviance Information Criteria (DIC) is used to evaluate the performances of these...
Source: Analytic Methods in Accident Research - September 12, 2019 Category: Accident Prevention Source Type: research
