Integrating driving and traffic simulators for the study of railway level crossing safety interventions: a methodology

Safety at Railway Level Crossings (RLXs) is an important issue within the Australian transport system. Crashes at RLXs involving road vehicles in Australia are estimated to cost $10 million each year. Such crashes are mainly due to human factors; unintentional errors contribute to 46% of all fatal collisions and are far more common than deliberate violations. This suggests that innovative intervention targeting drivers are particularly promising to improve RLX safety. In recent years there has been a rapid development of a variety of affordable technologies which can be used to increase driver’s risk awareness around crossings. To date, no research has evaluated the potential effects of such technologies at RLXs in terms of safety, traffic and acceptance of the technology. Integrating driving and traffic simulations is a safe and affordable approach for evaluating these effects. This methodology will be implemented in a driving simulator, where we recreated realistic driving scenario with typical road environments and realistic traffic. This paper presents a methodology for evaluating comprehensively potential benefits and negative effects of such interventions: this methodology evaluates driver awareness at RLXs , driver distraction and workload when using the technology . Subjective assessment on perceived usefulness and ease of use of the technology is obtained from standard questionnaires. Driving simulation will provide a model of driving behaviour at RLXs which will be used to estimate the effects of such new technology on a road network featuring RLX for different market penetrations using a traffic simulation. This methodology can assist in evaluating future safety interventions at RLXs

Methodology to assess safety effects of future Intelligent Transport Systems on railway level crossings

There is consistent evidence showing that driver behaviour contributes to crashes and near miss incidents at railway level crossings (RLXs). The development of emerging Vehicle-to-Vehicle and Vehicle-to-Infrastructure technologies is a highly promising approach to improve RLX safety. To date, research has not evaluated comprehensively the potential effects of such technologies on driving behaviour at RLXs. This paper presents an on-going research programme assessing the impacts of such new technologies on human factors and drivers’ situational awareness at RLX. Additionally, requirements for the design of such promising technologies and ways to display safety information to drivers were systematically reviewed. Finally, a methodology which comprehensively assesses the effects of in-vehicle and road-based interventions warning the driver of incoming trains at RLXs is discussed, with a focus on both benefits and potential negative behavioural adaptations. The methodology is designed for implementation in a driving simulator and covers compliance, control of the vehicle, distraction, mental workload and drivers’ acceptance. This study has the potential to provide a broad understanding of the effects of deploying new in-vehicle and road-based technologies at RLXs and hence inform policy makers on safety improvements planning for RLX

Is it safe to cross? Identification of trains and their approach speed at level crossings

© 2017 Elsevier Ltd Improving the safety at passive rail crossings is an ongoing issue worldwide. These crossings have no active warning systems to assist drivers’ decision-making and are completely reliant on the road user perceiving the approach of a train to decide whether to enter a crossing or not. This study aimed to better understand drivers’ judgements regarding approaching trains and their perceptions of safe crossing. Thirty-six participants completed a field-based protocol that involved detecting and judging the speeds of fast moving trains. They were asked to report when they first detected an approaching train, when they could first perceive it as moving, as well as providing speed estimates and a decision regarding when it would not be safe to cross. Participants detected the trains ∼2 km away and were able to perceive the trains as moving when they were 1.6 km away. Large differences were observed between participants but all could detect trains within the range of the longest sighting distances required at passive level crossings. Most participants greatly underestimated travelling speed by at least 30%, despite reporting high levels of confidence in their estimates. Further, most participants would have entered the crossing at a time when the lights would have been activated if the level crossing had been protected by flashing lights. These results suggest that the underestimation of high-speed trains could have significant safety implications for road users’ crossing behaviour, particularly as it reduces the amount of time and the safety margins that the driver has to cross the rail crossing

Big Data Risk Assessment the 21st Century approach to safety science

Safety Science has been developed over time with notable models in the early 20th Century such as Heinrich’s iceberg model and the Swiss cheese model. Common techniques such fault tree and event tree analyses, HAZOP analysis and bow-ties construction are widely used within industry. These techniques are based on the concept that failures of a system can be caused by deviations or individual faults within a system, combinations of latent failures, or even where each part of a complex system is operating within normal bounds but a combined effect creates a hazardous situation. In this era of Big Data, systems are becoming increasingly complex, producing such a large quantity of data related to safety that cannot be meaningfully analysed by humans to make decisions or uncover complex trends that may indicate the presence of hazards. More subtle and automated techniques for mining these data are required to provide a better understanding of our systems and the environment within which they operate, and insights to hazards that may not otherwise be identified. Big Data Risk Analysis (BDRA) is a suite of techniques being researched to identify the use of non-traditional techniques from big data sources to predict safety risk. This paper describes early trials of BDRA that have been conducted on railway signal information and text-based reports of railway safety near misses and the ongoing research that is looking at combining various data sources to uncover obscured trends that cannot be identified by considering each source individually. The paper also discusses how visual analytics may be a key tool in analysing Big Data to support knowledge elicitation and decision-making, as well as providing information in a form that can be readily interpreted by a variety of audiences

The development of an automatic method of safety monitoring at Pelican Crossings

This paper reports on the development of a method for automatic monitoring of safety at Pelican crossings. Historically, safety monitoring has typically been carried out using accident data, though given the rarity of such events it is difficult to quickly detect change in accident risk at a particular site. An alternative indicator sometimes used is traffic conflicts, though this data can be time consuming and expensive to collect. The method developed in this paper uses vehicle speeds and decelerations collected using standard in-situ loops and tubes, to determine conflicts using vehicle decelerations and to assess the possibility of automatic safety monitoring at Pelican crossings. Information on signal settings, driver crossing behaviour, pedestrian crossing behaviour and delays, and pedestrian-vehicle conflicts was collected synchronously through a combination of direct observation, video analysis, and analysis of output from tube and loop detectors. Models were developed to predict safety, i.e. pedestrian-vehicle conflicts using vehicle speeds and decelerations

Video analytics on the MLK Smart Corridor testbed

With the predicted boom of urban environment populations in the next 30 years, many new challenges in urban transportation will surface. In an effort to mitigate these, the Center for Urban Informatics and Progress (CUIP) has been introduced along with its testbed. One opportunity this testbed provides is the ability to utilize computer vision and video analytics to anonymously gather data on how citizens traverse the city. This thesis shall discuss an approach to real-time object tracking that serves as a basis for further analytics such as traffic flow data collection and near-miss detection. The proposed video analytics platform will aid citizens with their day-to-day commute through the corridor by deriving real-time data based on actual behavior seen in the citizens\u27 commute. Furthermore, since the testbed is ever-expanding in both hardware and size the algorithms and software proposed in this thesis are designed to prioritize scalability

EFFECT OF SHORT-STORAGE HRGCs ON DRIVER DECISION BEHAVIOR AND SAFETY CONCERNS: REAL-WORLD ANALYSIS AND EXPERIMENTAL EVIDENCE

Vehicle-train collisions at highway-rail grade crossings (HRGCs) continue to be a safety concern, and despite improvements in warnings, many of these incidents are attributed to human error. In some cases, distractions other than railroad traffic, such as HRGCs with limited space between the railroad tracks and the highway intersection, may create additional cognitive burdens for drivers. We investigated the effect of HRGC type (short-storage vs. non-short storage) on driver attention and decision-making in two studies. In Study 1, we systematically analyzed 996 incidents from 2017-2019 from the Federal Railroad Administration’s Safety database. Driver decision making and outcomes were different depending on HRGC type, with more train strikes in short storage incidents, as opposed to vehicle strikes. Study 2 was a controlled lab experiment in which drivers identified safety concerns in driving images. Drivers reported more safety concerns, and rated them more important in images of short-storage HRGCs than non-short storage HRGCs. This pattern did not depend on their rural or urban driving experience. Eye-tracking analysis found some differences in search behavior depending on the type of HRGC. This research contributes to a new area of research in rail safety, as studies comparing the two types of HRGCs have previously not been done. Interventions for non-short-storage HRGCs may not apply to short-storage HRGCs if it is found that drivers approach them differently

Application of Cognitive Systems Engineering Approach to Railway Systems (System for Investigation of Railway Interfaces)

This chapter presents the results of a cognitive systems engineering approach applied to railway systems. This application is through the methodology of ’System for Investigation of Railway Interfaces – SIRI’. The utility of the chapter lies in highlighting errors in the current approaches to safety risk management