Examining the Use of Regression Models for Developing Crash Modification Factors

Crash modification factors (CMFs) can be used to capture the safety effects of countermeasures and play significant roles in traffic safety management. The before-after study has been one of the most popular methods for developing CMFs. However, several drawbacks have limited its use for estimating high-quality CMFs. As an alternative, cross-sectional studies, specifically regression models, have been proposed and widely used for developing CMFs. However, the use of regression models for estimating CMFs has never been fully investigated. This study consequently sought to examine the conditions in which regression models could be used for such purpose. CMFs for several variables and their dependence were assumed and used for generating random crash counts. CMFs were derived from regression models using the simulated data for various scenarios. The CMFs were then compared with the assumed true values. The findings of this study are summarized as follows: (1) The CMFs derived from regression models should be unbiased when the premise of cross-sectional studies were met (i.e., all segments were similar, proper functional forms, variables were independent, enough sample size, etc.). (2) Functional forms played important roles in developing reliable CMFs. When improper forms for some variables were used, the CMFs for these variables were biased, and the quality of CMFs for other variables could also be affected. Meanwhile, this might produce biased estimates for other parameters. In addition, variable correlation and distribution might potentially influence the CMFs and parameter estimates when improper functional forms were used. (3) Regression models did suffer from the omitted-variable bias. If some factors having minor safety effects were omitted, the accuracy of estimated CMFs might still be acceptable. However, if some factors already known to have significant effects on crash risk were omitted, the estimated CMFs were generally unreliable. (4) When the influence on safety of considered variables were not independent, the CMFs produced from the commonly used regression models were biased. The bias was significantly correlated with the degree of their dependence

Detecting phone-related pedestrian distracted behaviours via a two-branch convolutional neural network

The distracted phone-use behaviours among pedestrians, like Texting, Game Playing and Phone Calls, have caused increasing fatalities and injuries. However, the research of phonerelated distracted behaviour by pedestrians has not been systemically studied. It is desired to improve both the driving and pedestrian safety by automatically discovering the phonerelated pedestrian distracted behaviours. Herein, a new computer vision-based method is proposed to detect the phone-related pedestrian distracted behaviours from a view of intelligent and autonomous driving. Specifically, the first end-to-end deep learning based Two-Branch Convolutional Neural Network (CNN) is designed for this task. Taking one synchronised image pair by two front on-car GoPro cameras as the inputs, the proposed two-branch CNN will extract features for each camera, fuse the extracted features and perform a robust classification. This method can also be easily extended to video-based classification by confidence accumulation and voting. A new benchmark dataset of 448 synchronised video pairs of 53,760 images collected on a vehicle is proposed for this research. The experimental results show that using two synchronised cameras obtained better performance than using one single camera. Finally, the proposed method achieved an overall best classification accuracy of 84.3% on the new benchmark when compared to other methods

Factors influencing the patterns of wrong-way driving crashes on freeway exit ramps and median crossovers: Exploration using ‘Eclat’ association rules to promote safety

Wrong-way driving (WWD) has been a constant traffic safety problem in certain types of roads. These crashes are mostly associated with fatal or severe injuries. This study aims to determine associations between various factors in the WWD crashes. Past studies on WWD crashes used either descriptive statistics or logistic regression to identify the impact of key contributing factors on frequency and/or severity of crashes. Machine learning and data mining approaches are resourceful in determining interesting and non-trivial patterns from complex datasets. This study employed association rules ‘Eclat’ algorithm to determine the interactions between different factors that result in WWD crashes. This study analyzed five years (2010–2014) of Louisiana WWD crash data to perform the analysis. A broad definition of WWD crashes (both freeway exit ramp WWD crashes and median crossover WWD crashes on low speed roadways) was used in this study. The results of this study confirmed that WWD fatalities are more likely to be associated with head-on collisions. Additionally, fatal WWD crashes tend to be involved with male drivers and off-peak hours. Driver impairment was found as a critical factor among the top twenty rules. Despite several other studies identifying only the WWD contributing factors, this study determined several influencing patterns in WWD crashes. The findings can provide an excellent opportunity for state departments of transportation (DOTs) and local agencies to develop safety strategies and engineering solutions to tackle the issues associated with WWD crashes

Fatal crashes at highway rail grade crossings: A U.S. based study

Crashes at highway rail grade crossings (HRGCs) are often involved with fatalities due to the momentum of a train. This study collected nine years (2010–2018) of fatal HRGC crashes from the Fatality Analysis Reporting System (FARS) to perform the analysis. The Taxicab Correspondence Analysis (TCA) was applied to this dataset. This method identified several patterns that trigger HRGC-related fatal crashes. The findings indicate that fatal crashes involving multiple fatalities are often highly associated with alcohol-influenced drivers, poor lighting conditions, and inclement weather. The fatal crash that occurs during the daylight with the uninfluenced driver is less likely to involve more than one fatality. The results also recognized the combinations of vehicle type and speed are associated with fatal crashes at rail grade crossings. The relatively low-speed limit crossings and larger utility vehicles are more likely to be associated with fatal crashes because large vehicles require a longer time to cross railroads at a low speed. The relatively high-speed limit crossing and smaller or lighter vehicles, especially the motorcycle, are highly associated with fatal crashes

Structural and functional abnormities of amygdala and prefrontal cortex in major depressive disorder with suicide attempts

Finding neural features of suicide attempts (SA) in major depressive disorder (MDD) may be helpful in preventing suicidal behavior. The ventral and medial prefrontal cortex (PFC), as well as the amygdala form a circuit implicated in emotion regulation and the pathogenesis of MDD. The aim of this study was to identify whether patients with MDD who had a history of SA show structural and functional connectivity abnormalities in the amygdala and PFC relative to MDD patients without a history of SA. We measured gray matter volume in the amygdala and PFC and amygdala-PFC functional connectivity using structural and functional magnetic resonance imaging (MRI) in 158 participants [38 MDD patients with a history of SA, 60 MDD patients without a history of SA, and 60 healthy control (HC)]. MDD patients with a history of SA had decreased gray matter volume in the right and left amygdala (F = 30.270, P = 0.000), ventral/medial/dorsal PFC (F = 15.349, P = 0.000), and diminished functional connectivity between the bilateral amygdala and ventral and medial PFC regions (F = 22.467, P = 0.000), compared with individuals who had MDD without a history of SA, and the HC group. These findings provide evidence that the amygdala and PFC may be closely related to the pathogenesis of suicidal behavior in MDD and implicate the amygdala-ventral/medial PFC circuit as a potential target for suicide intervention

Structural and functional abnormities of amygdala and prefrontal cortex in major depressive disorder with suicide attempts

Finding neural features of suicide attempts (SA) in major depressive disorder (MDD) may be helpful in preventing suicidal behavior. The ventral and medial prefrontal cortex (PFC), as well as the amygdala form a circuit implicated in emotion regulation and the pathogenesis of MDD. The aim of this study was to identify whether patients with MDD who had a history of SA show structural and functional connectivity abnormalities in the amygdala and PFC relative to MDD patients without a history of SA. We measured gray matter volume in the amygdala and PFC and amygdala-PFC functional connectivity using structural and functional magnetic resonance imaging (MRI) in 158 participants [38 MDD patients with a history of SA, 60 MDD patients without a history of SA, and 60 healthy control (HC)]. MDD patients with a history of SA had decreased gray matter volume in the right and left amygdala (F = 30.270, P = 0.000), ventral/medial/dorsal PFC (F = 15.349, P = 0.000), and diminished functional connectivity between the bilateral amygdala and ventral and medial PFC regions (F = 22.467, P = 0.000), compared with individuals who had MDD without a history of SA, and the HC group. These findings provide evidence that the amygdala and PFC may be closely related to the pathogenesis of suicidal behavior in MDD and implicate the amygdala-ventral/medial PFC circuit as a potential target for suicide intervention

Road Assessment Model and Pilot Application in China

Risk assessment of roads is an effective approach for road agencies to determine safety improvement investments. It can increases the cost-effective returns in crash and injury reductions. To get a powerful Chinese risk assessment model, Research Institute of Highway (RIOH) is developing China Road Assessment Programme (ChinaRAP) model to show the traffic crashes in China in partnership with International Road Assessment Programme (iRAP). The ChinaRAP model is based upon RIOH’s achievements and iRAP models. This paper documents part of ChinaRAP’s research work, mainly including the RIOH model and its pilot application in a province in China

Application of the Empirical Bayes Method with the Finite Mixture Model for Identifying Accident-Prone Spots

Hotspot identification (HSID) is an important component of the highway safety management process. A number of methods have been proposed to identify hotspots. Among these methods, previous studies have indicated that the empirical Bayes (EB) method can outperform other methods for identifying hotspots, since the EB method combines the historical crash records of the site and expected number of crashes obtained from a safety performance function (SPF) for similar sites. However, the SPFs are usually developed based on a large number of sites, which may contain heterogeneity in traffic characteristic. As a result, the hotspot identification accuracy of EB methods can possibly be affected by SPFs, when heterogeneity is present in crash data. Thus, it is necessary to consider the heterogeneity and homogeneity of roadway segments when using EB methods. To address this problem, this paper proposed three different classification-based EB methods to identify hotspots. Rural highway crash data collected in Texas were analyzed and classified into different groups using the proposed methods. Based on the modeling results for Texas crash dataset, it is found that one proposed classification-based EB method performs better than the standard EB method as well as other HSID methods