What factors influence risk at rail level crossings? A systematic review and synthesis of findings using systems thinking

Read, Gemma J. M.; Cox, Jolene A.; Hulme, Adam; Naweed, Anjum; Salmon, Paul M.

doi:10.1016/j.ssci.2021.105207

Cited by 43 publications

(13 citation statements)

References 83 publications

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“…Like Naumann et al (2020), we advocate for development of a systems archetype map (Senge, 1990) as systems thinking approaches are shown to be effective in areas of social change and safety improvement (e.g. Malfabon et al, 2015;Stroh, 2015;Newnam and Muir 2021;Read et al, 2021). Figure 3 shows an example of a simplified partial systems archetype map that might be developed.…”

Section: Shared Understandingmentioning

confidence: 99%

A process for change: The safe system approach and Vision Zero

Schell

Ward

2022

Front. Future Transp.

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While vision zero has been widely accepted as a core goal of many transportation agencies, to date efforts to achieve this target provide mixed results. One path to improved performance is gaining more interest in the United States-the safe system approach. While a safe system approach has proven successful in other countries and has great potential in the United States, successfully using this method requires a significant paradigm shift. Specifically, for many stakeholder organizations, establishing the safe system approach to reach Vision Zero represents a fundamental change in how they 1) perceive the road transport system, 2) interpret their role in that system, 3) operate with other system elements (stakeholder organizations), and 4) define a vision of success for the system. This level of transformative change requires structured preparation and deliberate management of the change process to be successful. In this paper we present a perspective on a process to initiate and manage this change that can increase the odds of success.

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Section: Shared Understandingmentioning

confidence: 99%

A process for change: The safe system approach and Vision Zero

Schell

Ward

2022

Front. Future Transp.

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“…The conventional driving mode enforces safety procedures that may involve vehicles reaching a complete stop before entering an intersection crossing. Read et al ( 39 ) presented relationships between the identified factors like risk and magnitude, unsafe and unsatisfactory road user characteristics, and road user risk perception, attitudes, and beliefs. It indicates that the main consideration has been given to the intersection of vital factors (i.e., how crossing design influences driving near the roadside).…”

Section: Related Workmentioning

confidence: 99%

Optimization-Based Traffic Safety Improvement Strategy for Autonomous Vehicle Driving at Level Crossings

Adane

Hou

Peng

et al. 2022

Transportation Research Record: Journal of the Transportation R

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Railway level crossings pose a serious threat to the safety and mobility of drivers traversing them. Globally, a significant number of traffic violations occur annually at level crossings. This study proposes an optimization-based autonomous vehicle (AV) driving strategy to improve the traffic safety of AVs at level crossings. First, an optimization model considering the operational constraints is developed. The cost function of the proposed optimization-based driving strategy is the time difference between the time required by the train to reach the level crossing and the time taken by the AV to complete the whole crossing maneuver. Second, a MATLAB simulation model is developed to validate the proposed driving strategy. Simulation results show that the proposed driving strategy delivers optimized AV driving without stoppage. The comparative study also shows that the proposed method improves driving performance. For instance, when a train is 1 m away from the light signal while the AV’s initial speed is 36 km/h, the time required for the AV to reach the speed limit sign is about 0.44 s, while the time required by the train to reach the car block area is 0.13 s. Thus, the AV is forced to stop and then go after the train leaves the level crossing. Contrary to this, the proposed method allows a safe passage of the vehicle within 1.66 s without stoppage. Most importantly, the driving strategy is a collision avoidance strategy without unnecessary enforcement of stop-and-go when AVs have the chance of crossing safely. Consequently, the optimization-based driving strategy both improves traffic safety and reduces traveling time.

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“…The safety of rail level crossings is the focus point of many activities of railway agencies [ 1 ]. Reports show that nearly a third of fatalities that are recorded in the course of operation of railways occur at RLCs [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

Detection of Safe Passage for Trains at Rail Level Crossings Using Deep Learning

Pamuła

2021

Sensors

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The detection of obstacles at rail level crossings (RLC) is an important task for ensuring the safety of train traffic. Traffic control systems require reliable sensors for determining the state of anRLC. Fusion of information from a number of sensors located at the site increases the capability for reacting to dangerous situations. One such source is video from monitoring cameras. This paper presents a method for processing video data, using deep learning, for the determination of the state of the area (region of interest—ROI) vital for a safe passage of the train. The proposed approach is validated using video surveillance material from a number of RLC sites in Poland. The films include 24/7 observations in all weather conditions and in all seasons of the year. Results show that the recall values reach 0.98 using significantly reduced processing resources. The solution can be used as an auxiliary source of signals for train control systems, together with other sensor data, and the fused dataset can meet railway safety standards.

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What factors influence risk at rail level crossings? A systematic review and synthesis of findings using systems thinking

Cited by 43 publications

References 83 publications

A process for change: The safe system approach and Vision Zero

A process for change: The safe system approach and Vision Zero

Optimization-Based Traffic Safety Improvement Strategy for Autonomous Vehicle Driving at Level Crossings

Detection of Safe Passage for Trains at Rail Level Crossings Using Deep Learning

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