Factors associated with self-reported inattentive driving at highway-rail grade crossings

Factors reducing the detectability of train horns by road users: A laboratory study

Level crossing safety is a well-researched safety issue worldwide, but little attention has been placed on the safety benefits of using train horns when a train approaches a level crossing. Given train horns' adverse effects on the health and well-being of residents living near rail tracks, the use of train horns must be beneficial to safety. The current study sought to determine in a laboratory environment whether road users (N = 31) can detect the range of train horns observed in Australia in terms of loudness and duration, using high-definition audio recordings from railway crossings. A repeated measures design was used to evaluate the effects of key factors likely to influence the detectability of train horns, including, visual and auditory distractive tasks, hearing loss and environmental noise (crossing bells). Train horn detectability was assessed based on participants' accuracy and reaction times. Results indicated the duration of the train horn had the most influential effect on the detectability of train horns, with short-duration train horns less likely to be detected. The presence of bells at a crossing was the second most important factor that limited train horn detection. Train horn loudness also affected detectability: faint blasts were less likely to be noticed, while loudest blasts were more likely to be noticed. However, loud horns reduced the ability to detect the side from which the train was approaching and may result in longer times to detect the train, in the field. The auditory distractive task reduced the train horn detection accuracy and increased reaction time. However, the visual distractive task and medium to severe hearing loss were not found to affect train horn detection. This laboratory study is the first to provide a broad understanding of the factors that affect the detectability of Australian train horns by road users. The findings from this study provide important insights into ways to reduce the use and modify the practice to mitigate the negative effects of train horns while maintaining the safety of road users.

Exploring pre-crash gate violations behaviors of drivers at highway-rail grade crossings using a mixed multinomial logit model

The highway-rail grade crossings (HRGCs) across the United States have been experiencing about 2500 crashes each year. Previous studies analyzed crash frequencies and fatalities; however, factors pertaining to drivers' gate violation behaviors are little known. Also, applied methodologies for gate violation behaviors analysis did not consider their heterogeneity across regions. This study uses 20-year of crash data (1999-2018) to evaluate pre-crash drivers' behaviors at HRGCs. A mixed multinomial logit model was developed to associate such behaviors with demographic factors, vehicle characteristics, temporal and environmental factors, as well as crossing-related factors. The study results indicated a high intra-class correlation coefficient which signifies the importance of including the random-effect parameter in the model. Further, the study found that male drivers are more likely to drive around the gate, while older drivers are more likely to stop and proceed before a train has passed. Furthermore, compared to trucks, all other vehicle types are more likely to drive around the gate. The influence of train speed, vehicle occupancy, visibility, among others, on drivers' pre-crash behaviors, is also presented. Understanding the impact of these factors on pre-crash behaviors may assist in improving the motorist's safety at the highway-rail grade crossings across the United States.

Influence of built environment and roadway characteristics on the frequency of vehicle crashes caused by driver inattention: A comparison between rural roads and urban roads

INTRODUCTION

With prevalent and increased attention to driver inattention (DI) behavior, this research provides a comprehensive investigation of the influence of built environment and roadway characteristics on the DI-related vehicle crash frequency per year. Specifically, a comparative analysis between DI-related crash frequency in rural road segments and urban road segments is conducted.

METHOD

Utilizing DI-related crash data collected from North Carolina for the period 2013-2017, three types of models: (1) Poisson/negative binomial (NB) model, (2) Poisson hurdle (HP) model/negative binomial hurdle (HNB) model, and (3) random intercepts Poisson hurdle (RIHP) model/random intercepts negative binomial hurdle (RIHNB) model, are applied to handle excessive zeros and unobserved heterogeneity in the dataset.

RESULTS

The results show that RIHP and RIHNB models distinctly outperform other models in terms of goodness-of-fit. The presence of commercial areas is found to increase the probability and frequency of DI-related crashes in both rural and urban regions. Roadway characteristics (such as non-freeways, segments with multiple lanes, and traffic signals) are positively associated with increased DI-related crash counts, whereas state-secondary routes and speed limits (higher than 35 mph) are associated with decreased DI-related crash counts in rural and urban regions. Besides, horizontal curved and longitudinal bottomed segments and segments with double yellow lines/no passing zones are likely to have fewer DI-related crashes in urban areas. Medians in rural road segments are found to be effective to reduce DI-related crashes. Practical Applications: These findings provide a valuable understanding of the DI-related crash frequency for transportation agencies to propose effective countermeasures and safety treatments (e.g., dispatching more police enforcement or surveillance cameras in commercial areas, and setting more medians in rural roads) to mitigate the negative consequences of DI behavior.

A Holistic Analysis of Train-Vehicle Accidents at Highway-Rail Grade Crossings in Florida

Highway-rail grade crossing (HRGC) accidents pose a serious risk of safety to highway users, including pedestrians trying to cross HRGCs. A significant increase in the number of HRGC accidents globally calls for greater research efforts, which are not limited to the analysis of accidents at HRGCs but also understanding user perception, driver behavior, potential conflicting areas at crossings, effectiveness of countermeasures and user perception towards them. HRGC safety is one of the priority areas in the State of Florida, since the state HRGCs experienced a total of 429 injuries and 146 fatalities between 2010 and 2019 with a significant increase in HRGC accidents over the last years. The present study aims to conduct a comprehensive analysis of the HRGCs that experienced accidents in Florida over the last years. The databases maintained by the Federal Rail Administration (FRA) are used to gather the relevant information for a total of 578 crossings that experienced at least one accident from 2010 to 2019. In contrast with many of the previous efforts, this study investigates a wide range of various factors, including physical and operational characteristics of crossings, vehicle and train characteristics, spatial characteristics, temporal and environmental characteristics, driver actions and related characteristics, and other relevant information. The outcomes of this research will help better understanding the major causes behind accidents at the HRGCs in the State of Florida in a holistic way by considering a variety of relevant factors, which will assist the appropriate stakeholders with implementation of safety improvement projects across the state.

Multivariate copula temporal modeling of intersection crash consequence metrics: A joint estimation of injury severity, crash type, vehicle damage and driver error

This study employs a copula-based multivariate temporal ordered probit model to simultaneously estimate the four common intersection crash consequence metrics - driver error, crash type, vehicle damage and injury severity - by accounting for potential correlations due to common observed and unobserved factors, while also accommodating the temporal instability of model estimates over time. To this end, a comprehensive literature review of relevant studies was conducted; four different copula model specifications including Frank, Clayton, Joe and Gumbel were estimated to identify the dominant factors contributing to each crash consequence indicator; the temporal effects on model estimates were investigated; the elasticity effects of the independent variables with regard to all four crash consequence indicators were measured to express the magnitude of the effects of an independent variable on the probability change for each level of four indicators; and specific countermeasures were recommended for each of the contributing factors to improve the intersection safety. The model goodness-of-fit illustrates that the Joe copula model with the parameterized copula parameters outperforms the other models, which verifies that the injury severity, crash type, vehicle damage and driver error are significantly correlated due to common observed and unobserved factors and, accounting for their correlations, can lead to more accurate model estimation results. The parameterization of the copula function indicates that their correlation varies among different crashes, including crashes that occurred at stop-controlled intersections, four-leg intersections and crashes which involved drivers younger than 25. The model coefficient estimates indicate that the driver's age, driving under the influence of drugs and alcohol, intersection geometry and control types, and adverse weather and light conditions are the most critical factors contributing to severe crash consequences. The coefficient estimates of four-leg intersections, yield and stop-controlled intersections and adverse weather conditions varied over time, which indicates that the model estimation of crash data may not be stable over time and should be accommodated in crash prediction analysis. In the end, relevant countermeasures corresponding to law enforcement and intersection infrastructure design are recommended to all of the contributing factors identified by the model. It is anticipated that this study can shed light on selecting valid statistical models for crash data analysis, identifying intersection safety issues, and helping develop effective countermeasures to improve intersection safety.