999 resultados para rail safety


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Scoping Project: Currently no national or structured learning framework available in Aus or NZ Current Project: Develop a national training program & capability framework for rail incident investigators - Establish the potential market demand - Define the curricula for a multi-level national training program - Explore training providers & delivery options

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Federal Railroad Administration, Office of Research and Development, Washington, D.C.

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Federal Railroad Administration, Office of Research and Development, Washington, D.C.

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Federal Railroad Administration, Office of Research and Development, Washington, D.C.

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Federal Railroad Administration, Office of Research and Development, Washington, D.C.

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A core component for the prevention of re-occurring incidents within the rail industry is rail safety investigations. Within the current Australasian rail industry, the nature of incident investigations varies considerably between organisations. As it stands, most of the investigations are conducted by the various State Rail Operators and Regulators, with the more major investigations in Australia being conducted or overseen by the Australian Transport Safety Bureau (ATSB). Because of the varying nature of these investigations, the current training methods for rail incident investigators also vary widely. While there are several commonly accepted training courses available to investigators in Australasia, none appear to offer the breadth of development needed for a comprehensive pathway. Furthermore, it appears that no single training course covers the entire breadth of competencies required by the industry. These courses range in duration between a few days to several years, and some were run in-house while others are run by external consultants or registered training organisations. Through consultations with rail operators and regulators in Australasia, this paper will identify capabilities required for rail incident investigation and explore the current training options available for rail incident investigators.

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Background: Historically rail organisations have been operating in silos and devising their own training agendas. However with the harmonisation of the Australian workplace health and safety legislation and the appointment of a national rail safety regulator in 2013, rail incident investigator experts are exploring the possibility of developing a unified approach to investigator training. Objectives: The Australian CRC for Rail Innovation commissioned a training needs analysis to identify if common training needs existed between organisations and to assess support for the development of a national competency framework for rail incident investigations. Method: Fifty-two industry experts were consulted to explore the possibility of the development of a standardised training framework. These experts were sourced from within 19 Australasian organisations, comprising Rail Operators and Regulators in Queensland, New South Wales, Victoria, Western Australia, South Australia and New Zealand. Results: Although some competency requirements appear to be organisation specific, the vast majority of reported training requirements were generic across the Australasian rail operators and regulators. Industry experts consistently reported strong support for the development of a national training framework. Significance: The identification of both generic training requirements across organisations and strong support for standardised training indicates that the rail industry is receptive to the development of a structured training framework. The development of an Australasian learning framework could: increase efficiency in course development and reduce costs; establish recognised career pathways; and facilitate consistency with regards to investigator training.

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A core component for the prevention of re-occurring incidents within the rail industry is rail safety investigations. Within the current Australasian rail industry, the nature of incident investigations varies considerably between organisations. As it stands, most of the investigations are conducted by the various State Rail Operators and Regulators, with the more major investigations in Australia being conducted or overseen by the Australian Transport Safety Bureau (ATSB). Because of the varying nature of these investigations, the current training methods for rail incident investigators also vary widely. While there are several commonly accepted training courses available to investigators in Australasia, none appear to offer the breadth of development needed for a comprehensive pathway. Furthermore, it appears that no single training course covers the entire breadth of competencies required by the industry. These courses range in duration between a few days to several years, and some were run in-house while others are run by external consultants or registered training organisations. Through consultations with rail operators and regulators in Australasia, this paper will identify capabilities required for rail incident investigation and explore the current training options available for rail incident investigators.

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Improving safety at rail level crossings is an important part of both road and rail safety strategies. While low in number, crashes between vehicles and trains at level crossings are catastrophic events typically involving multiple fatalities and serious injuries. Advances in driving assessment methods, such as the provision of on-road instrumented test vehicles with eye and head tracking, provide researchers with the opportunity to further understand driver behaviour at such crossings in ways not previously possible. This paper describes a study conducted to further understand the factors that shape driver behaviour at rail level crossings using instrumented vehicles. Twenty-two participants drove an On-Road Test Vehicle (ORTeV) on a predefined route in regional Victoria with a mix of both active (flashing lights with/without boom barriers) and passively controlled (stop, give way) crossings. Data collected included driving performance data, head checks, and interview data to capture driver strategies. The data from an integrated suite of methods demonstrated clearly how behaviour differs at active and passive level crossings, particularly for inexperienced drivers. For example, the head check data clearly show the reliance and expectancies of inexperienced drivers for active warnings even when approaching passively controlled crossings. These studies provide very novel and unique insights into how level crossing design and warnings shape driver behaviour.

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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.

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The Cooperative Research Centre (CRC) for Rail Innovation is conducting a tranche of industry-led research projects looking into safer rail level crossings. This paper will provide an overview of the Affordable Level Crossings project, a project that is performing research in both engineering and human factors aspects of low-cost level crossing warning devices (LCLCWDs), and is facilitating a comparative trial of these devices over a period of 12 months in several jurisdictions. Low-cost level crossing warning devices (LCLCWDs) are characterised by the use of alternative technologies for high cost components including train detection and connectivity (e.g. radar, acoustic, magnetic induction train detection systems and wireless connectivity replacing traditional track circuits and wiring). These devices often make use of solar power where mains power is not available, and aim to make substantial savings in lifecycle costs. The project involves trialling low-cost level crossing warning devices in shadow-mode, where devices are installed without the road-user interface at a number of existing level crossing sites that are already equipped with conventional active warning systems. It may be possible that the deployment of lower-cost devices can provide a significantly larger safety benefit over the network than a deployment of expensive conventional devices, as the lower cost would allow more passive level crossing sites to be upgraded with the same capital investment. The project will investigate reliability and safety integrity issues of the low-cost devices, as well as evaluate lifecycle costs and investigate human factors issues related to warning reliability. This paper will focus on the requirements and safety issues of LCLCWDs, and will provide an overview of the Rail CRC projects.

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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.

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Passively protected railway crossings are a major rail safety issue in Australia. Such crossings cannot be upgraded as such crossings are too numerous and the cost involved is prohibitive. Advanced Driver Assistance Systems (ADAS) have been shown to improve road safety and are widely used. These systems could be a solution to improve safety of passively protected crossings at a lower cost. Such complementary ADAS could result in driver’s over-trust due to the absence of Humane Machine Interface reflecting the quality of the information or the state of the ADAS (failure status). This paper demonstrates that driver’s exposure to crossing exhibiting fail-safe and non-fail safe properties could result in improperly allocating trust between technologies. We conducted a driving simulator study where participants (N=58) were exposed to three types of level crossing warning system on passive and active crossings. The results show that a significant proportion of participants over-trust the ADAS. Such drivers exhibit the same driving performance with the ADAS as when exposed to infrastructure based active crossing protection. They do not take the necessary safety precautions as they have a faster speed approach, reduced number of gaze toward the rail tracks and fail to stop at the crossing.

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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.