Evaluating ITS interventions at railway level crossings using a driving simulator

This paper assesses Intelligent Transportation Systems (ITS) to identify safety systems that are most likely to reduce driver errors at railway crossings. ITS technologies have been integrated in order to develop improved evaluation tools to reduce crashes at railway crossings. Although emerging technologies, knowledge, innovative interventions have been introduced to change driver behaviour, there is a lack of research on the impact of integrating ITS technologies and transportation simulation on drivers. The outcomes of ITS technologies for complementing traditional signage were compared with those of current safety systems (passive and active) at railway crossings. Three ITS technologies are compared with current treatments, in terms of compliance rate and vehicle speed profiles. It is found that ITS technologies improve compliance rate by 17~30% and also encourage drivers to slow down earlier compared to current passive and active crossings when there is a train approaching the railway crossings.

Understanding pedestrian behaviour at railway level crossings : is there a need for more research?

Aim Collisions between trains and pedestrians are the most likely to result in severe injuries and fatalities when compared to other types of rail crossing accidents. Currently, there is a growing emphasis towards developing effective interventions designed to reduce the prevalence of train–pedestrian collisions. This paper reviews what is currently known regarding the personal and environmental factors that contribute to train–pedestrian collisions, particularly among high-risk groups. Method Studies that reported on the prevalence and characteristics of pedestrian accidents at railway crossings up until June 2012 were searched in electronic databases. Results Males, school children and older pedestrians (and those with disabilities) are disproportionately represented in fatality databases. However, a main theme to emerge is that little is known about the origins of train–pedestrian collisions (especially compared to train–vehicle collisions). In particular, whether collisions result from engaging in deliberate violations versus making decisional errors. This subsequently limits the corresponding development of effective and targeted interventions for high-risk groups as well as crossing locations. Finally, it remains unclear what combination of surveillance and deterrence-based and education-focused campaigns are required to produce lasting reductions in train–pedestrian fatality rates. This paper provides direction for future research into the personal and environmental origins of collisions as well as the development of interventions that aim to attract pedestrians’ attention and ensure crossing rules are respected.

Video analytics for the detection of near-miss incidents on approach to railway level crossings

Recent modelling of socio-economic costs by the Australian railway industry in 2010 has estimated the cost of level crossing accidents to exceed AU$116 million annually. To better understand causal factors that contribute to these accidents, the Cooperative Research Centre for Rail Innovation is running a project entitled Baseline Level Crossing Video. The project aims to improve the recording of level crossing safety data by developing an intelligent system capable of detecting near-miss incidents and capturing quantitative data around these incidents. To detect near-miss events at railway level crossings a video analytics module is being developed to analyse video footage obtained from forward-facing cameras installed on trains. This paper presents a vision base approach for the detection of these near-miss events. The video analytics module is comprised of object detectors and a rail detection algorithm, allowing the distance between a detected object and the rail to be determined. An existing publicly available Histograms of Oriented Gradients (HOG) based object detector algorithm is used to detect various types of vehicles in each video frame. As vehicles are usually seen from a sideway view from the cabin’s perspective, the results of the vehicle detector are verified using an algorithm that can detect the wheels of each detected vehicle. Rail detection is facilitated using a projective transformation of the video, such that the forward-facing view becomes a bird’s eye view. Line Segment Detector is employed as the feature extractor and a sliding window approach is developed to track a pair of rails. Localisation of the vehicles is done by projecting the results of the vehicle and rail detectors on the ground plane allowing the distance between the vehicle and rail to be calculated. The resultant vehicle positions and distance are logged to a database for further analysis. We present preliminary results regarding the performance of a prototype video analytics module on a data set of videos containing more than 30 different railway level crossings. The video data is captured from a journey of a train that has passed through these level crossings.

Low-cost railway level crossings - breaking down the barriers

Level crossing risk continues to be a significant safety concern for the security of rail operations around the world. Over the last decade or so, a third of railway related fatalities occurred as a direct result of collisions between road and rail vehicles in Australia. Importantly, nearly half of these collisions occurred at railway level crossings with no active protection, such as flashing lights or boom barriers. Current practice is to upgrade level crossings that have no active protection. However, the total number of level crossings found across Australia exceed 23,500, and targeting the proportion of these that are considered high risk (e.g. public crossings with passive controls) would cost in excess of AU$3.25 billion based on equipment, installation and commissioning costs of warning devices that are currently type approved. Level crossing warning devices that are low-cost provide a potentially effective control for reducing risk; however, over the last decade, there have been significant barriers and legal issues in both Australia and the US that have foreshadowed their adoption. These devices are designed to have significantly lower lifecycle costs compared with traditional warning devices. They often make use of use of alternative technologies for train detection, wireless connectivity and solar energy supply. This paper describes the barriers that have been encountered for the adoption of these devices in Australia, including the challenges associated with: (1) determining requisite safety levels for such devices; (2) legal issues relating to duty of care obligations of railway operators; and (3) issues of Tort liability around the use of less than fail-safe equipment. This paper provides an overview of a comprehensive safety justification that was developed as part of a project funded by a collaborative rail research initiative established by the Australian government, and describes the conceptual framework and processes being used to justify its adoption. The paper provides a summary of key points from peer review and discusses prospective barriers that may need to be overcome for future adoption. A successful outcome from this process would result in the development of a guideline for decision-making, providing a precedence for adopting low-cost level crossing warning devices in other parts of the world. The framework described in this paper also provides relevance to the review and adoption of analogous technologies in rail and other safety critical industries.

Near-miss event detection at railway level crossings

Recent modelling of socio-economic costs by the Australian railway industry in 2010 has estimated the cost of level crossing accidents to exceed AU$116 million annually. To better understand the causal factors of these accidents, a video analytics application is being developed to automatically detect near-miss incidents using forward facing videos from trains. As near-miss events occur more frequently than collisions, by detecting these occurrences there will be more safety data available for analysis. The application that is being developed will improve the objectivity of near-miss reporting by providing quantitative data about the position of vehicles at level crossings through the automatic analysis of video footage. In this paper we present a novel method for detecting near-miss occurrences at railway level crossings from video data of trains. Our system detects and localizes vehicles at railway level crossings. It also detects the position of railways to calculate the distance of the detected vehicles to the railway centerline. The system logs the information about the position of the vehicles and railway centerline into a database for further analysis by the safety data recording and analysis system, to determine whether or not the event is a near-miss. We present preliminary results of our system on a dataset of videos taken from a train that passed through 14 railway level crossings. We demonstrate the robustness of our system by showing the results of our system on day and night videos.

A simulator evaluation of effects of assistive technologies on driver cognitive load at 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. However, such systems could overwhelm drivers, generate different types of driver errors and have negative effects on safety at level crossing. The literature shows an increasing interest for new ITS for increasing driver situational awareness at level crossings, as well as evaluations of such new systems on compliance. To our knowledge, the potential negative effects of such technologies have not been comprehensively evaluated yet. This study aimed at assessing the effect of different ITS interventions, designed to enhance driver behaviour at railway crossings, on driver’s cognitive loads. Fifty eight participants 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 cognitive load was objectively and subjectively assessed for each ITS intervention. Objective data were collected from a heart rate monitor and an eye tracker, while subjective data was collected with the NASA-TLX questionnaire. Overall, results indicated that the three trialled technologies did not result in significant changes in cognitive load while approaching crossings.

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.

Improving Near-Miss Event Detection Rate at Railway Level Crossings

Even though crashes between trains and road users are rare events at railway level crossings, they are one of the major safety concerns for the Australian railway industry. Nearmiss events at level crossings occur more frequently, and can provide more information about factors leading to level crossing incidents. In this paper we introduce a video analytic approach for automatically detecting and localizing vehicles from cameras mounted on trains for detecting near-miss events. To detect and localize vehicles at level crossings we extract patches from an image and classify each patch for detecting vehicles. We developed a region proposals algorithm for generating patches, and we use a Convolutional Neural Network (CNN) for classifying each patch. To localize vehicles in images we combine the patches that are classified as vehicles according to their CNN scores and positions. We compared our system with the Deformable Part Models (DPM) and Regions with CNN features (R-CNN) object detectors. Experimental results on a railway dataset show that the recall rate of our proposed system is 29% higher than what can be achieved with DPM or R-CNN detectors.

Investigating the interactions of road users and pedestrians in a dynamic rail level crossing environment

There are 23,500 level crossings in Australia. In these types of environments it is important to understand what human factor issues are present and how road users and pedestrians engage with crossings. A series of on-site observations were performed over a 2-day period at a 3-track active crossing. This was followed by 52 interviews with local business owners and members of the public. Data were captured using a manual-coding scheme for recording and categorising violations. Over 700 separate road user and pedestrian violations were recorded, with representations in multiple categories. Time stamping revealed that the crossing was active for 59% of the time in some morning periods. Further, trains could take up to 4-min to arrive following its first activation. Many pedestrians jaywalked under side rails and around active boom gates. In numerous cases pedestrians put themselves at risk in order to beat or catch the approaching train, ignored signs to stop walking when the lights were flashing. Analysis of interview data identified themes associated with congestion, safety, and violations. This work offers insight into context specific issues associated with active level crossing protection.

Driver behaviour and decision making at railway level crossings : An exploratory on-road case study

Crashes at rail level crossings represent a significant problem, both in Australia and worldwide. Advances in driving assessment methods, such as the provision of on-road instrumented test vehicles, now provide researchers with the opportunity to further understand driver behaviour at rail level crossings in ways not previously possible. This paper gives an overview of a recent on-road pilot study of driver behaviour at rail level crossings in which 25 participants drove a pre-determined route, incorporating 4 rail level crossings, using MUARC's instrumented On-Road Test Vehicle (ORTeV). Drivers provided verbal commentary whilst driving the route, and a range of other data were collected, including eye fixations, forward, cockpit and driver video, and vehicle data (speed, braking, steering wheel angle, lane tracking etc). Participants also completed a post trial cognitive task analysis interview. Extracts from the wider analyses are used to examine in depth driver behaviour at one of the rail level crossings encountered during the study. The analysis presented, along with the overall analysis undertaken, gives insight into the driver and wider systems factors that shape behaviour at rail level crossings, and highlights the utility of using a multi-method, instrumented vehicle approach for gathering data regarding driver behaviour in different contexts.

Use of ITS to improve level crossings safety : open issues

Level crossing crashes have been shown to result in enormous human and financial cost to society. According to the Australian Transport Safety Bureau (ATSB) [5] a total of 632 Railway Level crossing (RLX) collisions, between trains and road vehicles, occurred in Australia between 2001 and June 2009. The cost of RLX collisions runs into the tens of millions of dollars each year in Australia [6]. In addition, loss of life and injury are commonplace in instances where collisions occur. Based on estimates that 40% of rail related fatalities occur at level crossings [12], it is estimated that 142 deaths between 2001 and June 2009 occurred at RLX. The aim of this paper is to (i) summarise crash patterns in Australia, (ii) review existing international ITS interventions to improve level crossing and (iii) highlights open human factors research related issues. Human factors (e.g., driver error, lapses or violations) have been evidenced as a significant contributing factor in RLX collisions, with drivers of road vehicles particularly responsible for many collisions. Unintentional errors have been found to contribute to 46% of RLX collisions [6] and appear to be far more commonplace than deliberate violations. Humans have been found to be inherently inadequate at using the sensory information available to them to facilitate safe decision-making at RLX and tend to underestimate the speed of approaching large objects due to the non-linear increases in perceived size [6]. Collisions resulting from misjudgements of train approach speed and distance are common [20]. Thus, a fundamental goal for improved RLX safety is the provision of sufficient contextual information to road vehicle drivers to facilitate safe decision-making regarding crossing behaviours.

Towards the adoption of low-cost rail level crossing warning devices in regional areas of Australia : a review of current technologies and reliability issues

This paper discusses major obstacles for the adoption of low cost level crossing warning devices (LCLCWDs) in Australia and reviews those trialed in Australia and internationally. The argument for the use of LCLCWDs is that for a given investment, more passive level crossings can be treated, therefore increasing safety benefits across the rail network. This approach, in theory, reduces risk across the network by utilizing a combination of low-cost and conventional level crossing interventions, similar to what is done in the road environment. This paper concludes that in order to determine if this approach can produce better safety outcomes than the current approach, involving the incremental upgrade of level crossings with conventional interventions, it is necessary to perform rigorous risk assessments and cost-benefit analyses of LCLCWDs. Further research is also needed to determine how best to differentiate less reliable LCCLWDs from conventional warning devices through the use of different warning signs and signals. This paper presents a strategy for progressing research and development of LCLCWDs and details how the Cooperative Research Centre (CRC) for Rail Innovation is fulfilling this strategy through the current and future affordable level crossing projects.

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.

Improving level crossing safety through enhanced data recording and reporting : The CRC for Rail Innovation’s Baseline Rail Level Crossing Video project

This paper describes the work being conducted in the baseline rail level crossing project, supported by the Australian rail industry and the Cooperative Research Centre for Rail Innovation. The paper discusses the limitations of near-miss data for analysis obtained using current level crossing occurrence reporting practices. The project is addressing these limitations through the development of a data collection and analysis system with an underlying level crossing accident causation model. An overview of the methodology and improved data recording process are described. The paper concludes with a brief discussion of benefits this project is expected to provide the Australian rail industry.

Detecting anomalous events at railway level crossings

Collisions between pedestrians and vehicles continue to be a major problem throughout the world. Pedestrians trying to cross roads and railway tracks without any caution are often highly susceptible to collisions with vehicles and trains. Continuous financial, human and other losses have prompted transport related organizations to come up with various solutions addressing this issue. However, the quest for new and significant improvements in this area is still ongoing. This work addresses this issue by building a general framework using computer vision techniques to automatically monitor pedestrian movements in such high-risk areas to enable better analysis of activity, and the creation of future alerting strategies. As a result of rapid development in the electronics and semi-conductor industry there is extensive deployment of CCTV cameras in public places to capture video footage. This footage can then be used to analyse crowd activities in those particular places. This work seeks to identify the abnormal behaviour of individuals in video footage. In this work we propose using a Semi-2D Hidden Markov Model (HMM), Full-2D HMM and Spatial HMM to model the normal activities of people. The outliers of the model (i.e. those observations with insufficient likelihood) are identified as abnormal activities. Location features, flow features and optical flow textures are used as the features for the model. The proposed approaches are evaluated using the publicly available UCSD datasets, and we demonstrate improved performance using a Semi-2D Hidden Markov Model compared to other state of the art methods. Further we illustrate how our proposed methods can be applied to detect anomalous events at rail level crossings.