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.     

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.     

The experiences and perceptions of heavy vehicle drivers and train drivers of dangers at railway level crossings

Heavy vehicle–train collisions have the potential to be catastrophic in terms of fatalities, environmental disaster, delays in the rail network, and extensive damage to property. Heavy vehicles, such as ‘Road Trains’ and ‘B-Doubles’, are vulnerable road users due to their size and mass and require specific risk management solutions. The present study aimed to capture the experiences of heavy vehicle drivers and train drivers at road–rail level crossings, with a view to exploring the contributing factors toward such accidents. A series of semi-structured focus groups was conducted, with a total of 17 train drivers and 26 heavy vehicle drivers taking part. Though there were some differences between the groups in perceptions of the causes of heavy vehicle-level crossing incidents, discussion in both groups centred on design issues and behavioural issues. With regard to design, the configuration of level crossings was found to affect heavy vehicle driver visibility and effective vehicle clearance. With regard to behaviour, discussion centred around wilful violation of crossing protocols, often as a time-saving measure, as well as driver complacency due to high levels of familiarity. The implications of these factors for future level crossing safety initiatives are discussed.     

Fatal accidents at railway level crossings in Great Britain 1946-2009

This paper investigates fatal accidents and fatalities at level crossings in Great Britain over the 64-year period 1946–2009. The numbers of fatal accidents and fatalities per year fell by about 65% in the first half of that period, but since then have remained more or less constant at about 11 fatal accidents and 12 fatalities per year. At the same time other types of railway fatalities have fallen, so level crossings represent a growing proportion of the total. Nevertheless, Britain's level crossing safety performance remains good by international standards.The paper classifies level crossings into three types: railway-controlled, automatic, and passive. The safety performance of the three types of crossings has been very different. Railway-controlled crossings are the best-performing crossing type, with falling fatal accident rates. Automatic crossings have higher accident rates per crossing than railway controlled or passive crossings, and the accident rates have not decreased. Passive crossings are by far the most numerous, but many have low usage by road users. Their fatal accident rate has remained remarkably constant over the whole period at about 0.9 fatal accidents per 1000 crossings per year.A principal reason why fatal accidents and fatalities have not fallen in the second half of the period as they did in the first half is the increase in the number of automatic crossings, replacing the safer railway controlled crossings on some public roads. However, it does not follow that this replacement was a mistake, because automatic crossings have advantages over controlled crossings in reducing delays to road users and in not needing staff.Based on the trends for each type of crossing and for pedestrian and non-pedestrian accidents separately, in 2009 a mean of about 5% of fatal accidents were at railway controlled crossings, 52% were at automatic crossings, and 43% were at passive crossings. Fatalities had similar proportions. About 60% of fatalities were to pedestrians.A simple comparison of automatic railway level crossings and signalised road intersections found that in 2005 the numbers of fatalities per 1000 crossings or intersections were similar.     

Car driver behavior at flashing light railroad grade crossings

The behavior of car drivers at two Dutch railroad grade crossings with automatic flashing warning lights was analyzed. Car drivers were videotaped while approaching either the red flashing lights or the white flashing "safe"-signal. Approach speeds, positions, and time intervals were semiautomatically measured from videos of more than 900 drivers: 660 while confronted with the red lights and 272 while passing the white light. Of the latter group, head movements during the approach to the crossing were also registered. Red light compliance was relatively good, as no driver was observed to cross later than 6 seconds after the onset of the red lights, despite train-arrival times of well over 60 seconds. The level of red light compliance was further quantified in terms of both the deceleration and time-to-stopping-line as accepted by drivers. From a comparison with earlier research on red light compliance at signalized road intersections it appeared that red light compliance was better at railroad crossings than at road crossings. It is concluded that faulty red light compliance is not a major cause for car-train accidents and that emphasis should be placed on the ability of the present device to attract attention and to signal unambiguously. The high degree of compliance also causes unexpected driver actions, such as emergency braking and hesitations. A yellow phase may reduce these problems. Some drivers tended to proceed immediately after a train had cleared the road instead of waiting for the end of the red signal (typically some 3 to 5 seconds after the train had passed). This tendency might reveal a major cause of dramatic errors when a second train is approaching. Immediate extinction of the red signal is suggested, or even better, a separate signal to announce the arrival of the second train. Behavior during the white signal phase also showed indications of uncertainty. In some 10% of cases drivers tended to decelerate more strongly than necessary and to make extra head movements. It is recommended that the present white flashing signal be reconsidered.     

A mathematical modeling approach to resource allocation for railroad-highway crossing safety upgrades

State Departments of Transportation (S-DOT's) periodically allocate budget for safety upgrades at railroad-highway crossings. Efficient resource allocation is crucial for reducing accidents at railroad-highway crossings and increasing railroad as well as highway transportation safety. While a specific method is not restricted to S-DOT's, sorting type of procedures are recommended by the Federal Railroad Administration (FRA), United States Department of Transportation for the resource allocation problem. In this study, a generic mathematical model is proposed for the resource allocation problem for railroad-highway crossing safety upgrades. The proposed approach is compared to sorting based methods for safety upgrades of public at-grade railroad-highway crossings in Tennessee. The comparison shows that the proposed mathematical modeling approach is more efficient than sorting methods in reducing accidents and severity.     

Model crash frequency at highway–railroad grade crossings using negative binomial regression

Despite the fact that traffic collisions at highway–railroad grade crossings (HRGXs) are rare events, the impact of HRGX crashes is nevertheless more severe than highway crashes. Empirical studies show that traffic collisions at HRGXs are mainly attributed to railway-related and/or highway-related characteristics, particularly drivers’ abnormal behavior, driving around, or through an HRGX. These factors have different effects on crash likelihood (i.e., the number of traffic collisions or crash frequency) at an HRGX. To explore the causal relationship between crash frequency and the factors related to railroad and highway systems, we used a negative binomial regression model to identify the factors that are statistically significantly associated with traffic collisions at HRGXs, and conducted relevant sensitivity analyses to investigate the marginal effect of daily highway traffic on changes in crash frequency. The empirical study shows that the number of daily trains, the number of tracks, highway separation, annual averaged daily traffic (AADT), and crossing length had statistically significant effects on the mean number of traffic collisions (all p-values?≤?0.0487). Further, the marginal effect of the AADT on the change of crash frequency indicates that crash likelihood monotonically increases with the increase of AADT.     

Injury severity in delivery-motorcycle to vehicle crashes in the Seoul metropolitan area

More than 56% of motorcycles in Korea are used for the purpose of delivering parcels and food. Since such delivery requires quick service, most motorcyclists commit traffic violations while delivering, such as crossing the centerline, speeding, running a red light, and driving in the opposite direction down one-way streets. In addition, the fatality rate for motorcycle crashes is about 12% of the fatality rate for road traffic crashes, which is considered to be high, although motorcycle crashes account for only 5% of road traffic crashes in South Korea. Therefore, the objective of this study is to analyze the injury severity of vehicle-to-motorcycle crashes that have occurred during delivery. To examine the risk of different injury levels sustained under all crash types of vehicle-to-motorcycle, this study applied an ordered probit model. Based on the results, this study proposes policy implications to reduce the injury severity of vehicle-to-motorcycle crashes during delivery.     

Assessing factors causing severe injuries in crashes of high-deck buses in long-distance driving on freeways

High-deck buses that have a higher center of gravity traveling at an excessive speed have a higher likelihood of causing serious and fatal accidents when drivers lose control of the vehicle. In addition, drivers who suffer from fatigue in long-distance driving increase the likelihood of serious accident. This paper examines the effects of risk factors contributing to severe crashes associated with high-deck buses used for long-distance driving on freeways. An ordered logit and latent class models are used to examine significant factors on the severity of injuries in crashes related to high-deck buses. Driver fatigue, drivers or passengers not wearing a seat belt, reckless driving, drunk driving, crashes occurred between midnight and dawn, and crashes occurred at interchange ramps were found to significantly affect the severity of injuries in crashes involving high-deck buses. Safety policies to prevent severe injuries in crashes involving high deck buses used for long-distance runs on freeways include: (1) restricting drivers from exceeding the limit of daily driving hours and mandating sufficient rest breaks; (2) installing an automatic sleep-warning device in the vehicle; (3) drivers with obstructive sleep apnea syndrome or sleep disorders should be tested and treated before they are allowed to perform long hours of driving tasks; (4) educating the public or even amending the seatbelt legislation to require all passengers to wear a seat belt and thus reduce the chance of ejection from a high-deck bus and prevent serious injuries in a crash while traveling at a higher speed on freeways.     

Factors influencing safety in a sample of marked pedestrian crossings selected for safety inspections in the city of Oslo

This paper reports an analysis of factors influencing safety in a sample of marked pedestrian crossings in the city of Oslo, Norway. The sample consists of 159 marked pedestrian crossings where a total of 316 accidents were recorded during a period of five years. The crossings were selected for inspection because of they were, for various reasons, regarded as sub-standard. The sample of crossings is therefore not representative of all pedestrian crossings in Oslo. Factors influencing the number of accidents were studied by means of negative binomial regression. Factors that were studied included the volume of pedestrians and vehicles, the number of traffic lanes at the crossing, the location of the crossing (midblock or junction), the type of traffic control, the share of pedestrians using the crossing and the speed of approaching vehicles. The analysis confirmed the presence of a “safety-in-numbers” effect, meaning that an increase in the number of pedestrians is associated with a lower risk of accident for each pedestrian. Crossings located in four-leg junctions or roundabouts had more accidents than crossings located in three-leg junctions or on sections between junctions. A high share of pedestrians crossing the road outside the marked crossing was associated with a high number of accidents. Increased speed was associated with an increased number of accidents.     

Modeling crash outcome probabilities at rural intersections: application of hierarchical binomial logistic models

It is important to examine the nature of the relationships between roadway, environmental, and traffic factors and motor vehicle crashes, with the aim to improve the collective understanding of causal mechanisms involved in crashes and to better predict their occurrence. Statistical models of motor vehicle crashes are one path of inquiry often used to gain these initial insights. Recent efforts have focused on the estimation of negative binomial and Poisson regression models (and related deviants) due to their relatively good fit to crash data. Of course analysts constantly seek methods that offer greater consistency with the data generating mechanism (motor vehicle crashes in this case), provide better statistical fit, and provide insight into data structure that was previously unavailable. One such opportunity exists with some types of crash data, in particular crash-level data that are collected across roadway segments, intersections, etc. It is argued in this paper that some crash data possess hierarchical structure that has not routinely been exploited. This paper describes the application of binomial multilevel models of crash types using 548 motor vehicle crashes collected from 91 two-lane rural intersections in the state of Georgia. Crash prediction models are estimated for angle, rear-end, and sideswipe (both same direction and opposite direction) crashes. The contributions of the paper are the realization of hierarchical data structure and the application of a theoretically appealing and suitable analysis approach for multilevel data, yielding insights into intersection-related crashes by crash type.     

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.     

Distance between speed humps and pedestrian crossings: does it matter?

Speed humps are a common physical measure installed at pedestrian crossings to reduce vehicle speeds therefore improve the safety and mobility of pedestrians at the crossing.The aim of this study was to determine whether variations in distance between speed humps and pedestrian crossings contribute differently to the safety and mobility of pedestrians and cyclists, especially children and the elderly, and if so, how. Three sites in Sweden were studied, where vehicle speed measurements and video filming at the site resulted in manually coded, road user behaviour of 1972 pedestrians and cyclists.Road user behaviour at three test sites and two comparison sites equipped with speed cushion at distances of about 5 m and 10 m from the pedestrian crossing, i.e. about one or two car lengths, were studied. As vehicle speeds were somewhat lower at the pedestrian crossing when the distance between the speed cushion to the pedestrian crossing was greater, and there were positive aspects regarding the mobility of the pedestrians and cyclists, a greater distance of about 10 m or two car lengths between the hump and the pedestrian crossing is suggested. The present study only covers speed cushions, but the same distance is also regarded as important when installing other types of physical measures to reduce vehicle speed.     

Safety effects of blue cycle crossings: a before-after study

This paper presents a before-after accident study of marking blue cycle crossings in 65 signalised junctions. Corrections factors for changes in traffic volumes and accident/injury trends are included using a general comparison group in this non-experimental observational study. Analysis of long-term accident trends point towards no overall abnormal accident counts in the before period. The safety effect depends on the number of blue cycle crossings at the junction. One blue cycle crossing reduces the number of junction accidents by 10%, whereas marking of two and four blue cycle crossings increases the number of accidents by 23% and 60%, respectively. Larger reduction and increases are found for injuries. Safety gains at junctions with one blue cycle crossing arise because the number of accidents with cyclists and moped riders that may have used the blue cycle crossing in the after period and pedestrians in the pedestrian crossing parallel and just next to the blue marking was statistically significant reduced. Two or four blue cycle crossings especially increase the number of rear-end collisions only with motor vehicles involved and right-angle collisions with passenger cars driving on red traffic lights.     

Safety effects of exclusive and concurrent signal phasing for pedestrian crossing

This paper describes the estimation of pedestrian crash count and vehicle interaction severity prediction models for a sample of signalized intersections in Connecticut with either concurrent or exclusive pedestrian phasing. With concurrent phasing, pedestrians cross at the same time as motor vehicle traffic in the same direction receives a green phase, while with exclusive phasing, pedestrians cross during their own phase when all motor vehicle traffic on all approaches is stopped. Pedestrians crossing at each intersection were observed and classified according to the severity of interactions with motor vehicles. Observation intersections were selected to represent both types of signal phasing while controlling for other physical characteristics. In the nonlinear mixed models for interaction severity, pedestrians crossing on the walk signal at an exclusive signal experienced lower interaction severity compared to those crossing on the green light with concurrent phasing; however, pedestrians crossing on a green light where an exclusive phase was available experienced higher interaction severity. Intersections with concurrent phasing have fewer total pedestrian crashes than those with exclusive phasing but more crashes at higher severity levels. It is recommended that exclusive pedestrian phasing only be used at locations where pedestrians are more likely to comply.     

Mistakes or deliberate violations? A study into the origins of rule breaking at pedestrian train crossings

Train pedestrian collisions are the most likely to result in severe injuries and fatalities when compared to other types of rail crossing accidents. However, there is currently scant research that has examined the origins of pedestrians’ rule breaking at level crossings. As a result, this study examined the origins of pedestrians’ rule breaking behaviour at crossings, with particular emphasis directed towards examining the factors associated with making errors versus deliberation violations. A total of 636 individuals volunteered to participate in the study and completed either an online or paper version of the questionnaire. Quantitative analysis of the data revealed that knowledge regarding crossing rules was high, although up to 18% of level crossing users were either unsure or did not know (in some circumstances) when it was legal to cross at a level crossing. Furthermore, 156 participants (24.52%) reported having intentionally violated the rules at level crossings and 3.46% (n = 22) of the sample had previously made a mistake at a crossing. In regards to rule violators, males (particularly minors) were more likely to report breaking rules, and the most frequent occurrence was after the train had passed rather than before it arrives. Regression analysis revealed that males who frequently use pedestrian crossings and report higher sensation seeking traits are most likely to break the rules. This research provides evidence that pedestrians are more likely to deliberately violate rules (rather than make errors) at crossings and it illuminates high risk groups. This paper will further outline the study findings in regards to the development of countermeasures as well as provide direction for future research efforts in this area.     

Fatal train accidents on Europe's railways: 1980-2009

This paper presents an analysis of fatal train accident rates and trends on Europe's main line railways from 1980 to 2009. The paper uses a new set of data for the European Union together with Norway and Switzerland, assembled partly under the auspices of the European Railway Agency and partly on the author's own account. The estimated overall trend in the number of fatal train collisions and derailments per train-kilometre is −6.3% per year from 1990 to 2009, with a 95% confidence interval of −8.7% to −3.9%. The estimated accident rate in 2009 is 1.35 fatal collisions or derailments per billion train-kilometres, giving an estimated mean number of fatal accidents in 2009 of 6.0. The overall number of fatalities per fatal accident in 1990–2009 is 4.10, with no apparent long term change over time, giving an estimated mean of 24.6 fatalities per year in train collisions and derailments in 2009. There are statistically significant differences in the fatal train accident rates and trends between the different European countries, although the estimates of the rates and trends for many individual countries have wide confidence limits. The distribution of broad causes of accidents appears to have remained unchanged over the long term, so that safety improvements appear to have been across the board, and not focused on any specific cause. The most frequent cause of fatal train collisions and derailments is signals passed at danger. In contrast to fatal train collisions and derailments, the rate per train-kilometre of serious accidents at level crossings remained unchanged in 1990–2009. The immediate causes of most of the serious level crossing accidents are errors or violations by road users.     

The effects of age,gender, and crash types on drivers’ injury-related health care costs

There are many studies that evaluate the effects of age, gender, and crash types on crash related injury severity. However, few studies investigate the effects of those crash factors on the crash related health care costs for drivers that are transported to hospital. The purpose of this study is to examine the relationships between drivers’ age, gender, and the crash types, as well as other crash characteristics (e.g., not wearing a seatbelt, weather condition, and fatigued driving), on the crash related health care costs. The South Carolina Crash Outcome Data Evaluation System (SC CODES) from 2005 to 2007 was used to construct six separate hierarchical linear regression models based on drivers’ age and gender. The results suggest that older drivers have higher health care costs than younger drivers and male drivers tend to have higher health care costs than female drivers in the same age group. Overall, single vehicle crashes had the highest health care costs for all drivers. For males older than 64-years old sideswipe crashes are as costly as single vehicle crashes. In general, not wearing a seatbelt, airbag deployment, and speeding were found to be associated with higher health care costs. Distraction-related crashes are more likely to be associated with lower health care costs in most cases. Furthermore this study highlights the value of considering drivers in subgroups, as some factors have different effects on health care costs in different driver groups. Developing an understanding of longer term outcomes of crashes and their characteristics can lead to improvements in vehicle technology, educational materials, and interventions to reduce crash-related health care costs.     

Microsimulation modelling of driver behaviour towards alternative warning devices at railway level crossings

Level crossings are amongst the most complex of road safety issues, due to the addition of rail infrastructure, trains and train operations. The differences in the operational characteristics of different warning devices together with varying crossing, traffic or/and train characteristics, cause different driver behaviour at crossings. This paper compares driver behaviour towards two novel warning devices (rumble strips and in-vehicle audio warning) with two conventional warning devices (flashing light and stop sign) at railway level crossings using microsimulation modelling. Two safety performance indicators directly related to collision risks, violation and time-to-collision, were adopted. Results indicated the active systems were more effective at reducing likely collisions compared to passive devices. With the combined application of driving simulation and traffic microsimulation modelling, traffic safety performance indicators for a level crossing can be estimated. From these, relative safety comparisons for the different traffic devices are derived, or even for absolute safety evaluation with proper calibration from field investigations.