What are the differences in driver injury outcomes at highway-rail grade crossings? Untangling the role of pre-crash behaviors

Crashes at highway-rail grade crossings can result in severe injuries and fatalities to vehicle occupants. Using a crash database from the Federal Railroad Administration (N = 15,639 for 2004–2013), this study explores differences in safety outcomes from crashes between passive controls (Crossbucks and STOP signs) and active controls (flashing lights, gates, audible warnings and highway signals). To address missing data, an imputation model is developed, creating a complete dataset for estimation. Path analysis is used to quantify the direct and indirect associations of passive and active controls with pre-crash behaviors and crash outcomes in terms of injury severity. The framework untangles direct and indirect associations of controls by estimating two models, one for pre-crash driving behaviors (e.g., driving around active controls), and another model for injury severity. The results show that while the presence of gates is not directly associated with injury severity, the indirect effect through stopping behavior is statistically significant (95% confidence level) and substantial. Drivers are more likely to stop at gates that also have flashing lights and audible warnings, and stopping at gates is associated with lower injury severity. This indirect association lowers the chances of injury by 16%, compared with crashes at crossings without gates. Similar relationships between other controls and injury severity are explored. Generally, crashes occurring at active controls are less severe than crashes at passive controls. The results of study can be used to modify Crash Modification Factors (CMFs) to account for crash injury severity. The study contributes to enhancing the understanding of safety by incorporating pre-crash behaviors in a broader framework that quantifies correlates of crash injury severity at active and passive crossings.     

Accident prediction model for railway-highway interfaces

Considerable past research has explored relationships between vehicle accidents and geometric design and operation of road sections, but relatively little research has examined factors that contribute to accidents at railway-highway crossings. Between 1998 and 2002 in Korea, about 95% of railway accidents occurred at highway-rail grade crossings, resulting in 402 accidents, of which about 20% resulted in fatalities. These statistics suggest that efforts to reduce crashes at these locations may significantly reduce crash costs. The objective of this paper is to examine factors associated with railroad crossing crashes. Various statistical models are used to examine the relationships between crossing accidents and features of crossings. The paper also compares accident models developed in the United States and the safety effects of crossing elements obtained using Korea data. Crashes were observed to increase with total traffic volume and average daily train volumes. The proximity of crossings to commercial areas and the distance of the train detector from crossings are associated with larger numbers of accidents, as is the time duration between the activation of warning signals and gates. The unique contributions of the paper are the application of the gamma probability model to deal with underdispersion and the insights obtained regarding railroad crossing related vehicle crashes.     

Motor vehicle drivers’ injuries in train–motor vehicle crashes

The objectives of this research were to: (1) identify a more suitable model for modeling injury severity of motor vehicle drivers involved in train–motor vehicle crashes at highway–rail grade crossings from among three commonly used injury severity models and (2) to investigate factors associated with injury severity levels of motor vehicle drivers involved in train–motor vehicle crashes at such crossings. The 2009–2013 highway–rail grade crossing crash data and the national highway–rail crossing inventory data were combined to produce the analysis dataset. Four-year (2009–2012) data were used for model estimation while 2013 data were used for model validation. The three injury severity levels—fatal, injury and no injury—were based on the reported intensity of motor-vehicle drivers’ injuries at highway–rail grade crossings.     

A joint-probability approach to crash prediction models

Many road safety researchers have used crash prediction models, such as Poisson and negative binomial regression models, to investigate the associations between crash occurrence and explanatory factors. Typically, they have attempted to separately model the crash frequencies of different severity levels. However, this method may suffer from serious correlations between the model estimates among different levels of crash severity. Despite efforts to improve the statistical fit of crash prediction models by modifying the data structure and model estimation method, little work has addressed the appropriate interpretation of the effects of explanatory factors on crash occurrence among different levels of crash severity. In this paper, a joint probability model is developed to integrate the predictions of both crash occurrence and crash severity into a single framework. For instance, the Markov chain Monte Carlo (MCMC) approach full Bayesian method is applied to estimate the effects of explanatory factors. As an illustration of the appropriateness of the proposed joint probability model, a case study is conducted on crash risk at signalized intersections in Hong Kong. The results of the case study indicate that the proposed model demonstrates a good statistical fit and provides an appropriate analysis of the influences of explanatory factors.     

Investigating driving behaviour of older drivers with mild cognitive impairment using a portable driving simulator

While there is a large body of research indicating that individuals with moderate to severe dementia are unfit to drive, relatively little is known about the driving performance of older drivers with mild cognitive impairment (MCI). The aim of the current study was to examine the driving performance of older drivers with MCI on approach to intersections, and to investigate how their healthy counterparts perform on the same driving tasks using a portable driving simulator. Fourteen drivers with MCI and 14 age-matched healthy older drivers (aged 65–87 years) completed a 10-min simulator drive in an urban environment. The simulator drive consisted of stop-sign controlled and signal-controlled intersections. Drivers were required to stop at the stop-sign controlled intersections and to decide whether or not to proceed through a critical light change at the signal-controlled intersections. The specific performance measures included; approach speed, number of brake applications on approach to the intersection (either excessive or minimal), failure to comply with stop signs, and slower braking response times on approach to a critical light change. MCI patients in our sample performed more poorly than controls across a number of variables. However, because the trends failed to reach statistical significance it will be important to replicate the study using a larger sample to qualify whether the results can be generalised to the broader population.     

A latent class modeling approach for identifying vehicle driver injury severity factors at highway-railway crossings

In this paper, we aim to identify the different factors that influence injury severity of highway vehicle occupants, in particular drivers, involved in a vehicle-train collision at highway-railway grade crossings. The commonly used approach to modeling vehicle occupant injury severity is the traditional ordered response model that assumes the effect of various exogenous factors on injury severity to be constant across all accidents. The current research effort attempts to address this issue by applying an innovative latent segmentation based ordered logit model to evaluate the effects of various factors on the injury severity of vehicle drivers. In this model, the highway-railway crossings are assigned probabilistically to different segments based on their attributes with a separate injury severity component for each segment. The validity and strength of the formulated collision consequence model is tested using the US Federal Railroad Administration database which includes inventory data of all the railroad crossings in the US and collision data at these highway railway crossings from 1997 to 2006. The model estimation results clearly highlight the existence of risk segmentation within the affected grade crossing population by the presence of active warning devices, presence of permanent structure near the crossing and roadway type. The key factors influencing injury severity include driver age, time of the accident, presence of snow and/or rain, vehicle role in the crash and motorist action prior to the crash.     

Effectiveness of traffic light vs. boom barrier controls at road–rail level crossings: A simulator study

Although collisions at level crossings are relatively uncommon occurrences, the potential severity of their consequences make them a top priority among safety authorities. Twenty-five fully-licensed drivers aged between 20 and 50 years participated in a driving simulator study that compared the efficacy, and drivers’ subjective perception, of two active level crossing traffic control devices: flashing lights with boom barriers and standard traffic lights. Because of its common usage in most states in Australia, a stop sign-controlled level crossing served as the passive referent. Although crossing violations were less likely at the level crossings controlled by active devices than at those controlled by stop signs, both kinds of active control were associated with a similar number of violations. Further, the majority (72%) of drivers reported preferring flashing lights to traffic lights. Collectively, results indicate that the installation of traffic lights at real-world level crossings would not be likely to offer safety benefits over and above those provided already by flashing lights with boom barriers. Furthermore, the high rate of violations at passively controlled crossings strongly supports the continued practice of upgrading level crossings with active traffic control devices.     

Poisson, Poisson-gamma and zero-inflated regression models of motor vehicle crashes: balancing statistical fit and theory

There has been considerable research conducted over the last 20 years focused on predicting motor vehicle crashes on transportation facilities. The range of statistical models commonly applied includes binomial, Poisson, Poisson-gamma (or negative binomial), zero-inflated Poisson and negative binomial models (ZIP and ZINB), and multinomial probability models. Given the range of possible modeling approaches and the host of assumptions with each modeling approach, making an intelligent choice for modeling motor vehicle crash data is difficult. There is little discussion in the literature comparing different statistical modeling approaches, identifying which statistical models are most appropriate for modeling crash data, and providing a strong justification from basic crash principles. In the recent literature, it has been suggested that the motor vehicle crash process can successfully be modeled by assuming a dual-state data-generating process, which implies that entities (e.g., intersections, road segments, pedestrian crossings, etc.) exist in one of two states-perfectly safe and unsafe. As a result, the ZIP and ZINB are two models that have been applied to account for the preponderance of "excess" zeros frequently observed in crash count data. The objective of this study is to provide defensible guidance on how to appropriate model crash data. We first examine the motor vehicle crash process using theoretical principles and a basic understanding of the crash process. It is shown that the fundamental crash process follows a Bernoulli trial with unequal probability of independent events, also known as Poisson trials. We examine the evolution of statistical models as they apply to the motor vehicle crash process, and indicate how well they statistically approximate the crash process. We also present the theory behind dual-state process count models, and note why they have become popular for modeling crash data. A simulation experiment is then conducted to demonstrate how crash data give rise to "excess" zeros frequently observed in crash data. It is shown that the Poisson and other mixed probabilistic structures are approximations assumed for modeling the motor vehicle crash process. Furthermore, it is demonstrated that under certain (fairly common) circumstances excess zeros are observed-and that these circumstances arise from low exposure and/or inappropriate selection of time/space scales and not an underlying dual state process. In conclusion, carefully selecting the time/space scales for analysis, including an improved set of explanatory variables and/or unobserved heterogeneity effects in count regression models, or applying small-area statistical methods (observations with low exposure) represent the most defensible modeling approaches for datasets with a preponderance of zeros.     

Urban and rural differences in older drivers’ failure to stop at stop signs

Our purpose was to determine visual and cognitive predictors for older drivers’ failure to stop at stop signs. 1425 drivers aged between ages 67 and 87 residing in Salisbury Maryland were enrolled in a longitudinal study of driving. At baseline, the participants were administered a battery of vision and cognition tests, and demographic and health questionnaires. Five days of driving data were collected with a Driving Monitoring System (DMS), which obtained data on stop signs encountered and failure to stop at stop signs. Driving data were also collected 1 year later (round two). The outcome, number of times a participant failed to stop at a stop sign at round two, was modeled using vision and cognitive variables as predictors. A negative binomial regression model was used to model the failure rate. Of the 1241 who returned for round two, 1167 drivers had adequate driving data for analyses and 52 did not encounter a stop sign. In the remaining 1115, 15.8% failed at least once to stop at stop signs, and 7.1% failed to stop more than once. Rural drivers had 1.7 times the likelihood of not stopping compared to urban drivers. Amongst the urban participants, the number of points missing in the bilateral visual field was significantly associated with a lower failure rate. In this cohort, older drivers residing in rural areas were less likely to stop at stop-sign intersections than those in urban areas. It is possible that rural drivers frequent areas with less traffic and better visibility, and may be more likely to take the calculated risk of not stopping. In this cohort failure to stop at stop signs was not explained by poor vision or cognition. Conversely in urban areas, those who have visual field loss appear to be more cautious at stop signs.