Passive Crossings Research Articles

Case study: Compounded problems at a passive railroad grade crossing

Passive railroad grade crossings fail to provide critical information to motorists about the presence of an approaching train, and pose a greater risk to motorists than do active grade crossings that can include the addition of flashing signal lights and gates. This case study describes the conditions that resulted in a train colliding with a truck on the tracks. In addition to the typical hazards at passive crossings, the subject collision resulted from additional hazards at the site that most likely prevented the truck driver from noticing the sight and sound of the approaching train. These factors are discussed, along with some current efforts to improve safety at railroad crossings.

DSRC-Enabled Train Safety Communication System at Unmanned Crossings

Although wireless technology is available for safety-critical applications, few applications have been used to improve train crossing safety. To prevent potential collisions between trains and vehicles, we present a Dedicated Short-Range Communication (DSRC)-enabled train safety communication system targeting passive crossings. Since our application’s purpose is preventing collisions between trains and vehicles, we present a method to calculate the minimum required warning time for head-to-head collision. We therefore define the best and worst-case scenarios and provide empirical data collected at six operating crossings in the U.S. with numerous system configurations, including modulation scheme, transmission power, antenna type, train speed, and vehicle braking distances. From our measurements, we find that the warning application coverage range is independent of the train speed, that the omnidirectional antenna with high transmission power is the best configuration for our system, and that the communications latency is less than 1 ms on average and around 5 m worst case. We use the radio communication coverage and introduce the safeness level metric to evaluate the suitability of DSRC for collision avoidance. From the measured data, we observe that the DSRC-enabled train safety communication system is feasible for up to 35 mph train speeds which is providing more than 25–30 s to avoid a collision for 25–65 mph vehicle speeds. Higher train speeds are expected to be safe, but additional data over extended distances are needed for a definite conclusion.

Model for Risk Calculation and Reliability Comparison of Level Crossings.

There is not a single country in the world that is so rich that it can remove all level crossings or provide their denivelation in order to absolutely avoid the possibility of accidents at the intersections of railways and road traffic. In the Republic of Serbia alone, the largest number of accidents occur at passive crossings, which make up three-quarters of the total number of crossings. Therefore, it is necessary to constantly find solutions to the problem of priorities when choosing level crossings where it is necessary to raise the level of security, primarily by analyzing the risk and reliability at all level crossings. This paper presents a model that enables this. The calculation of the maximal risk of a level crossing is achieved under the conditions of generating the maximum entropy in the virtual operating mode. The basis of the model is a heterogeneous queuing system. Maximum entropy is based on the mandatory application of an exponential distribution. The system is Markovian and is solved by a standard analytical concept. The basic input parameters for the calculation of the maximal risk are the geometric characteristics of the level crossing and the intensities and structure of the flows of road and railway vehicles. The real risk is based on statistical records of accidents and flow intensities. The exact reliability of the level crossing is calculated from the ratio of real and maximal risk, which enables their further comparison in order to raise the level of safety, and that is the basic idea of this paper.

Traverses, delays and fatalities at railway level crossings in Great Britain

This paper investigates relationships between traverses, delays and fatalities to road users at railway level crossings in Great Britain. A ‘traverse’ means a passage across a level crossing by a road user, who may be a pedestrian, cyclist, or occupant of a road vehicle. The paper finds that the road users with the highest fatality rate per traverse are pedestrians at passive crossings. Their rate is about three orders of magnitude higher than that of users with the lowest risk, who are road vehicle occupants at railway-controlled crossings. The paper considers the choice between automatic and railway-controlled crossings on public roads. Railway-controlled crossings are widely used in Britain. They are about one order of magnitude safer than automatic crossings, but they impose greater delays on users. A formula is developed to give the overall delay to road users at either type of crossing in terms of the numbers of road users and trains per day, and in terms of the length of time that the crossing must be closed to the road to allow the passage of one train. It is found that automatic level crossings cause substantially less delay than railway-controlled level crossings. The official monetary values of road user delay and of preventing a fatality were used to estimate the valuations of delays and fatalities at hypothetical but representative automatic and railway-controlled crossings. These valuations were then used to explore the effect of replacing representative railway-controlled with automatic crossings or vice-versa. It is found that the valuation of the reduced delays from adopting automatic crossings typically outweighs the valuation of the losses from the increased casualties. However, in practice Britain has chosen to retain a large number of railway-controlled crossings, which implies accepting the delays in return for a good level crossing safety record. Finally, an analysis is carried out to determine the additional risk of typical car and walk journeys that involve traversing a level crossing compared with similar journeys that do not. It is found that the additional risk is small for motor vehicle journeys, but substantial for walk journeys.

Getting the Attention of Drivers Back on Passive Railway Level Crossings: Evaluation of Advanced Flashing Lights

Improving safety at railway level crossings remains a priority for the rail industry internationally, as they remain a significant hazard. A high proportion of collisions occur at passive level crossings, because of their high prevalence and their lower effectiveness at mitigating the risks that road users encounter at such crossings. The unreasonable cost required to upgrade them to incorporate active warnings implies that such crossings will remain on the road, and that alternative approaches are required. Drivers tend to make errors at such crossings, and this can be related to approaching such level crossings at speeds that are too high, exhibiting reduced scanning behaviors to look for trains, and not complying with stop signs at the crossing. An alternative approach is to upgrade the advanced signage with active flashing lights activated by road vehicles, aimed at reducing looked-but-failed-to-see errors and reinforcing the behavior expected from road users at such crossings. A field trial was conducted in New Zealand that evaluated how approach speeds and the visual scanning behavior of 27 drivers, recorded with an eye tracker, changed with such treatments. It was found that the presence of road vehicle-activated advanced signage provided a range of benefits for drivers unaware of the presence of a passive crossings, such as increasing drivers’ attention to road signs through drivers fixating on signage for longer durations and reduced (slower) approach speeds. Further research is needed to evaluate whether these benefits are sustained over time, and whether this can minimize complacency resulting from familiarity.

The road user, the pedestrian, and me: Investigating the interactions, errors and escalating risks of users of fully protected level crossings

Most railway level crossings in high-traffic road environments are actively protected and safest when exposure is taken into account, supporting the rail industry’s strategy to focus on upgrading passive crossings. However, collisions at these crossings account for two thirds of the collision costs at level crossings. These collisions may occur for a variety of reasons with anecdotal evidence assigning deliberate violations a key concern though there is little data to illustrate the size of the problem. The paper quantifies this to provide a more comprehensive and empirical understanding of road user interactions at active level crossings in a busy urban environment and shed light on a broader range of accident precursors. Two days of observation were conducted with large numbers of violations observed for both road vehicles and pedestrians. High levels of safety-related events were observed per activation of the crossing, and these events were often followed by safety-related events from following road users. The specific design of the level crossing and local road environment resulted in high levels of congestion for a number of reasons including large train traffic volumes, short stacking issues, limited options for pedestrians to cross, and activation vs. time-to-arrival synchronisation issues. These conditions attracted decision errors, pressures, and other ostensible issues which led to deliberate and unintended violations. This paper shows the need for development of interventions at urban level crossings targeting violations for all types of level crossing users to ensure level crossings can respond to the rapid changes in the transport environment.

A new method for evaluating driver behavior and interventions for passive railway level crossings with pneumatic tubes

ABSTRACTThe recent development of new technologies has attracted the interest of the rail industry to increase driver awareness at railway-level crossings. Such interventions have been predominantly tested in driving simulators, but driver behavior at level crossings is still poorly understood due to the limited number, focus, and period of time of studies conducted. This highlights the lack of manageable methods for studying driver behavior on a long-term basis in naturalistic conditions, particularly for passive-level crossings. The authors propose a new, practical, and affordable application of pneumatic tubes to evaluate over the long term the compliance of drivers at level crossings and their speed during the approach of level crossing with low road-traffic volume. The authors tested this methodology at two railway-level crossings in the Brisbane region and recorded more than 10,000 vehicle movements for each site over a 6-month data collection. The authors showed that this methodology detected differences in driver compliance at the two crossings that had different sighting characteristics. The authors also showed by sensitivity analysis that compliance estimates from this approach were within a ±6% range, which would be sufficient for detecting significant changes in driver behavior with new interventions for level crossings.

Safety Ranking of Railway Crossings in Hungary

Regarding traffic safety, railway crossings are crucial points in the rail and road network. In Hungary the safety ranking of railway crossings has been performed since 1993 and the results have been used for selecting unsafe locations and for prioritizing safety measures. Prioritization is based on a complex point system taking into account accident data, traffic volumes and various traffic engineering aspects. Weights of various indicators were based on engineering judgement. Although this method has been renewed a few times, there is a need to update this method involving more thorough statistical methods. The objective of this paper is to calibrate a new model and to compare it with previous models.A sample of about 1700 railway crossings satisfying a few requirements (railway with public service, traffic volume data available) was set up. The first part of the paper describes data collection. Five years (2010–2014) of accident data, AADT (Annual Average Daily Traffic) for rail and road, speed limits and further data (road pavement width, control devices, crossing angle, alignment, number of tracks, sight distances) were gathered. The modeling was conducted in two steps. First, each variable was entered into the model alone to see which ones significantly affect accident frequency. As a next step, two-variable models were built where expected injury accident frequency is predicted by annual daily road traffic and annual daily rail traffic. The sample was also split up into subgroups based on the type of control devices in order to analyze their effect on safety. Models are proposed using the Generalized Linear Modeling approach (GLM) assuming a negative binomial error structure.The results give estimation of the impacts of explanatory factors on the safety of railway crossings. Annual daily road traffic and annual daily train traffic are significant predictors. A number of predictors such as crossing angle, track alignment, number of tracks and sight distances turned out to be not significant. As far as the type of control devices are concerned, as expected coefficients are decreasing, hence safety is increasing in the following order: passive crossings, flashing lights, flashing lights + half barriers. Based on the above results, the complex point system used by practitioners for safety ranking was simplified considerably leaving out variables which turned out to be not significant.

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.

Driving Simulator Evaluation of the Failure of an Audio In-vehicle Warning for Railway Level Crossings

It is impracticable to upgrade the 18,900 Australian passive crossings as such crossings are often located in remote areas, where power is lacking and with low road and rail traffic. The rail industry is interested in developing innovative in-vehicle technology interventions to warn motorists of approaching trains directly in their vehicles. The objective of this study was therefore to evaluate the benefits of the introduction of such technology. We evaluated the changes in driver performance once the technology is enabled and functioning correctly, as well as the effects of an unsafe failure of the technology? We conducted a driving simulator study where participants (N = 15) were familiarised with an in-vehicle audio warning for an extended period. After being familiarised with the system, the technology started failing, and we tested the reaction of drivers with a train approaching. This study has shown that with the traditional passive crossings with RX2 signage, the majority of drivers complied (70 %) and looked for trains on both sides of the rail track. With the introduction of the in-vehicle audio message, drivers did not approach crossings faster, did not reduce their safety margins and did not reduce their gaze towards the rail tracks. However, participants’ compliance at the stop sign decreased by 16.5 % with the technology installed in the vehicle. The effect of the failure of the in-vehicle audio warning technology showed that most participants did not experience difficulties in detecting the approaching train even though they did not receive any warning message. This showed that participants were still actively looking for trains with the system in their vehicle. However, two participants did not stop and one decided to beat the train when they did not receive the audio message, suggesting potential human factors issues to be considered with such technology.

Driver’s behavioural changes with new intelligent transport system interventions at railway level crossings—A driving simulator study

Improving safety at railway level crossings is an important issue for the Australian transport system. Governments, the rail industry and road organisations have tried a variety of countermeasures for many years to improve railway level crossing safety. New types of intelligent transport system (ITS) interventions are now emerging due to the availability and the affordability of technology. These interventions target both actively and passively protected railway level crossings and attempt to address drivers’ errors at railway crossings, which are mainly a failure to detect the crossing or the train and misjudgement of the train approach speed and distance. This study aims to assess the effectiveness of three emerging ITS that the rail industry considers implementing in Australia: a visual in-vehicle ITS, an audio in-vehicle ITS, as well as an on-road flashing beacons intervention. The evaluation was conducted on an advanced driving simulator with 20 participants per trialled technology, each participant driving once without any technology and once with one of the ITS interventions. Every participant drove through a range of active and passive crossings with and without trains approaching. Their speed approach of the crossing, head movements and stopping compliance were measured. Results showed that driver behaviour was changed with the three ITS interventions at passive crossings, while limited effects were found at active crossings, even with reduced visibility. The on-road intervention trialled was unsuccessful in improving driver behaviour; the audio and visual ITS improved driver behaviour when a train was approaching. A trend toward worsening driver behaviour with the visual ITS was observed when no trains were approaching. This trend was not observed for the audio ITS intervention, which appears to be the ITS intervention with the highest potential for improving safety at passive crossings.

Assessing driver acceptance of Intelligent Transport Systems in the context of railway level crossings

Intelligent Transport Systems (ITS) have the potential to substantially reduce the number of crashes caused by human errors at railway levels crossings. Such systems, however, will only exert an influence on driving behaviour if they are accepted by the driver. This study aimed at assessing driver acceptance of different ITS interventions designed to enhance driver behaviour at railway crossings. Fifty-eight participants, divided into three groups, took part in a driving simulator study in which three ITS devices were tested: an in-vehicle visual ITS, an in-vehicle audio ITS, and an on-road valet system. Driver acceptance of each ITS intervention was assessed in a questionnaire guided by the Technology Acceptance Model and the Theory of Planned Behaviour. Overall, results indicated that the strongest intentions to use the ITS devices belonged to participants exposed to the road-based valet system at passive crossings. The utility of both models in explaining drivers’ intention to use the systems is discussed, with results showing greater support for the Theory of Planned Behaviour. Directions for future studies, along with strategies that target attitudes and subjective norms to increase drivers’ behavioural intentions, are also discussed.

Human Factors Evaluation of a Novel Australian Approach for Activating Railway Level Crossings

There are currently 23,500 level crossings in Australia, broadly divided active level crossings with flashing lights; and passive level crossings controlled by stop and give way signs. The current strategy is to annually upgrade passive level crossings with active controls within a given budget, but the 5,900 public passive crossings are too numerous to be upgraded all. The rail industry is considering alternative options to treat more crossings. One of them is to use lower cost equipment with reduced safety integrity level, but with a design that would fail to a safe state: in case of the impossibility for the system to know whether a train is approaching, the crossing changes to a passive crossing. This is implemented by having a STOP sign coming in front of the flashing lights. While such design is considered safe in terms of engineering design, questions remain on human factors. In order to evaluate whether such approach is safe, we conducted a driving simulator study where participants were familiarized with the new active crossing, before changing the signage to a passive crossing. Our results show that drivers treated the new crossing as an active crossing after the novelty effect had passed. While most participants did not experience difficulties with the crossing being turned back to a passive crossing, a number of participants experienced difficulties stopping in time at the first encounter of such passive crossing. Worse, a number of drivers never realized the signage had changed, highlighting the link between the decision to brake and stop at an active crossing to the lights flashing. Such results show the potential human factor issues of changing an active crossing to a passive crossing in case of failure of the detection of the train.

Traffic Safety at Road–Rail Level Crossings Using a Driving Simulator and Traffic Simulation

Several intelligent transportation systems (ITS) were used with an advanced driving simulator to assess its influence on driving behavior. Three types of ITS interventions were tested: video in vehicle, audio in vehicle, and on-road flashing marker. The results from the driving simulator were inputs for a developed model that used traffic micro-simulation (VISSIM 5.4) to assess the safety interventions. Using a driving simulator, 58 participants were required to drive through active and passive crossings with and without an ITS device and in the presence or absence of an approaching train. The effect of changes in driver speed and compliance rate was greater at passive crossings than at active crossings. The slight difference in speed of drivers approaching ITS devices indicated that ITS helped drivers encounter crossings in a safer way. Since the traffic simulation was not able to replicate a dynamic speed change or a probability of stopping that varied depending on ITS safety devices, some modifications were made to the traffic simulation. The results showed that exposure to ITS devices at active crossings did not influence drivers' behavior significantly according to the traffic performance indicator, such as delay time, number of stops, speed, and stopped delay. However, the results of traffic simulation for passive crossings, where low traffic volumes and low train headway normally occur, showed that ITS devices improved overall traffic performance.

Modelling risk at low-exposure railway level crossings: supporting an argument for low-cost level crossing warning devices with lower levels of safety integrity

This paper describes a risk model for estimating the likelihood of collisions at low-exposure railway level crossings, demonstrating the effect that differences in safety integrity can have on the likelihood of a collision. The model facilitates the comparison of safety benefits between level crossings with passive controls (stop or give-way signs) and level crossings that have been hypothetically upgraded with conventional or low-cost warning devices. The scenario presented illustrates how treatment of a cross-section of level crossings with low-cost devices can provide a greater safety benefit compared with treatment with conventional warning devices for the same budget.

Integration of driving simulator and traffic simulation to analyse behaviour at railway crossings

The use of state-of-the-art technology to collect and analyse data has significantly improved the effectiveness of safety studies. Currently, despite the fact that there are many safety systems deployed at railway crossings, only limited research has been conducted to evaluate which of these systems is the most effective in terms of costs and safety. This paper demonstrates a way to evaluate safety at railway crossings using a twin-pronged approach: a driving simulator and traffic simulation software. A number of outputs have been observed from a driving simulator, such as driver compliance rate, vehicle speed profile, acceleration profile, initial braking position and final braking position. The compliance percentage at passive crossings (67 and 72% for a stop sign and rumble strips, respectively) has lower compliance rates compared with active crossings (97 and 93% for flashing red light and in-vehicle audible warning, respectively) at an 80 km/h approach speed. Using a statistical analysis it is shown that speed and acceleration profiles can be used to differentiate the effectiveness of active and passive crossings. These indicators are interpreted and used as input to a traffic simulation, which assists in determining which safety device is more efficient. By integrating driving simulator and traffic simulation models, this approach can be applied to evaluate and compare safety performance without the need to install costly test beds at real railway crossings.

Decision support model for prioritizing railway level crossings for safety improvements: Application of the adaptive neuro-fuzzy system

Every year, more than 400 people are killed in over 1.200 accidents at road-rail level crossings in the European Union (European Railway Agency, 2011). Together with tunnels and specific road black spots, level crossings have been identified as being a particular weak point in road infrastructure, seriously jeopardizing road safety. In the case of railway transport, level crossings can represent as much as 29% of all fatalities caused by railway operations. In Serbia there are approximately 2.350 public railway level crossings (RLC) across the country, protected either passively (64%) or by active systems (25%). Passive crossings provide only a stationary sign warning of the possibility of trains crossing. Active systems, by contrast, activate automatic warning devices (i.e., flashing lights, bells, barriers, etc.) as a train approaches. Securing a level crossing (whether it has an active or passive system of protection) is a material expenditure, and having in mind that Serbian Railways is a public company directly financed from the budget of the Republic of Serbia, it cannot be expected that all unsecured level crossings be part of a programme of securing them. The most common choice of which level crossings to secure is based on media and society pressure, and on the possible consequences of a rise in the number of traffic accidents at the level crossings. The process of selecting a level crossing where safety equipment will be installed is accompanied by a greater or lesser degree of uncertainty of the essential criteria for making a relevant decision. In order to exploit these uncertainties and ambiguities, fuzzy logic is used in this paper. Here also, modeling of the Adaptive Neuro Fuzzy Inference System (ANFIS) is presented, which supports the process of selecting which level crossings should receive an investment of safety equipment. The ANFIS model is a trained set of data which is obtained using a method of fuzzy multi-criteria decision making and fuzzy clustering techniques. 20 experts in road and rail traffic safety at railway level crossings took part in the study. The ANFIS model was trained with the experiential knowledge of these experts and tested on a selection of rail crossings in the Belgrade area regarding an investment of safety equipment. The ANFIS model was tested on 88 level crossings and a comparison was made between the data set it produced and the data set obtained on the basis of predictions made by experts.

Clinical Saccharomyces cerevisiae isolates cannot cross the epithelial barrier in vitro

Saccharomyces cerevisiae is generally considered to be a safe organism and is essential to produce many different kinds of foods as well as being widely used as a dietary supplement. However, several isolates, which are genetically related to brewing and baking yeasts, have shown virulent traits, being able to produce human infections in immunodeficient patients. Previously it has been shown that the administration of S. cerevisiae clinical isolates can lead to systemic infections, reaching several organs in murine systems. In this work, we studied S. cerevisiae clinical isolates in an in vitro intestinal epithelial barrier model, comparing their behaviour with that of several strains of the related pathogens Candida glabrata and Candida albicans. The results showed that, in contrast to C. glabrata and C. albicans, S. cerevisiae was not able to cross the intestinal barrier. We concluded that S. cerevisiae can only perform opportunistic or passive crossings when epithelial barrier integrity is previously compromised.

How Drivers Understand Safe Behaviour and Perceive Risks at Passive Railway-Road Level Crossings

This study was designed to investigate how drivers assess safety issues at passive railway-road level crossings. The study was limited to traditional and relatively inexpensive safety measures. Fifty-six car and van drivers were interviewed after passive level crossings with low traffic volume. Both key requirements of safe behaviour (i.e. decrease of speed and looking for trains) were indicated by 36-71% of drivers depending on the classification of responses. This result suggests that a substantial percentage of drivers have no proper concept of safe behaviour at level crossings. Another important result was that drivers found the crossing of main roads to be more difficult than crossing passive railway-road level crossings — despite the fact that they considered the latter to be more dangerous. This suggests that the drivers estimated the crash risk at railway-road level crossings to be relatively low, although they know that it is dangerous in general. Furthermore, the drivers suggested that the conspicuity of level crossings could be improved by increasing lateral visibility early enough and with advance warning signs. The drivers also suggested that caution could be increased with the use of STOP signs, improving the visibility of road signs and increasing the lateral visibility of tracks. The results suggest that there are several potential safety measures that could support drivers.

Determination of Sight Distance Requirements for Finnish Level Crossings

Safety at level crossings is a major concern for Finnish railways. Less than a quarter of all 4,061 level crossings are equipped with active warning devices, mainly barriers but also warning lights and bells. Especially at passive level crossings it is entirely up to the road user to know whether it is safe to cross the railway. However, the environment should support road users and infrastructure managers should provide conditions where safe behaviour is possible. Perhaps the most important criterion then concerns sight distance conditions at the level crossing. A minimum requirement is that a road user stopped before the level crossing must be able to traverse the railway in less time than it takes for an approaching train to arrive at the level crossing from the point where the road user can first see it. Examples of current sight distance guidelines and their reasoning in selected countries are described. A proposal for new Finnish sight distance guidelines is made. The proposal for required sight distances consists of a minimum sight distance from the road of 5-8 metres from the nearest rail to the railway, and a minimum distance at which a road user approaching the level crossing must be able to see it.