Risk‐Based Decision Support Tools: Protecting Rail‐Centered Transit Corridors from Cascading Effects

Greenberg, Michael; Lowrie, Karen; Mayer, Henry; Altıok, Tayfur

doi:10.1111/j.1539-6924.2011.01627.x

Cited by 12 publications

(11 citation statements)

References 41 publications

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“…To reduce technological disasters, their impact calls for studies of technological risk, which may include risk assessment, estimate, perception, acceptability, mitigation, and policy (Kates and Kasperson 1983). Various types of technological disasters also call for integrated approaches to multi-hazard risk assessment (Greiving, Fleischhauer, and Lückenkötter 2006), decision support tools (Greenberg et al 2011), and effective risk analysis ideas and methods (Chen, Fan, and Chen 2009;Greenberg et al 2012).…”

Section: Technological Disaster Risk Analyses and Modelsmentioning

confidence: 99%

Revealing global hot spots of technological disasters: 1900–2013

Shen

Hwang

2016

Journal of Risk Research

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Technological disasters can happen in any country in the world and cause human fatalities, injuries, and economic damages, among other physical and social consequences. As the world adopts more technologies, becomes further industrialized, continues faster urbanization, and has larger and more concentrated population, the occurrences and impacts of technological disasters are likely to be more frequent and severe and call for more scholarly research. However, there is a lack of good models for reliable technological risk analysis, which is the foundation for effective preparation for, sound mitigation of, and quick recovery from technological disasters. This research develops an expected risk analysis model, including a base sub-model and a location quotient sub-model, for nearly 200 countries of the world, using the technological disasters recorded in the EM-DAT database for the period 1900-2013. The sub-models are based on country-level risk impacts in terms of expected fatalities, injuries, people affected, and economic losses, their standard deviations, ranges, and corresponding country percentages and ranks. The sub-models are validated using correlations and scatter plots for the observed and expected risk impacts. The results show that the sub-models perform well by yielding consistent expected risks and related measures, indicating that the expected risk analysis model is a reasonably good alternative to existing risk analysis models.

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Section: Technological Disaster Risk Analyses and Modelsmentioning

confidence: 99%

Revealing global hot spots of technological disasters: 1900–2013

Shen

Hwang

2016

Journal of Risk Research

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“…Also, the responsible parties wanted us to concentrate on those parts of the system that were sufficiently complicated that decision support tools were needed to replace existing spreadsheets and collective experience. These discussions with users substantially influenced our model designs …”

Section: Three Simulation Modelsmentioning

confidence: 99%

Passenger Rail Security, Planning, and Resilience: Application of Network, Plume, and Economic Simulation Models as Decision Support Tools

et al. 2013

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We built three simulation models that can assist rail transit planners and operators to evaluate high and low probability rail-centered hazard events that could lead to serious consequences for rail-centered networks and their surrounding regions. Our key objective is to provide these models to users who, through planning with these models, can prevent events or more effectively react to them. The first of the three models is an industrial systems simulation tool that closely replicates rail passenger traffic flows between New York Penn Station and Trenton, New Jersey. Second, we built and used a line source plume model to trace chemical plumes released by a slow-moving freight train that could impact rail passengers, as well as people in surrounding areas. Third, we crafted an economic simulation model that estimates the regional economic consequences of a variety of rail-related hazard events through the year 2020. Each model can work independently of the others. However, used together they help provide a coherent story about what could happen and set the stage for planning that should make rail-centered transport systems more resistant and resilient to hazard events. We highlight the limitations and opportunities presented by using these models individually or in sequence.

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“…A Bayesian network (BN) has been used in maritime transportation problems because it is a powerful tool for risk analysis and decision making . After obtaining the basic feasible options by using CBR, this BN method is used for final decision making.…”

Section: Introductionmentioning

confidence: 99%

Three‐Stage Decision‐Making Model under Restricted Conditions for Emergency Response to Ships Not under Control

Yan

Wang

et al. 2017

Risk Analysis

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A ship that is not under control (NUC) is a typical incident that poses serious problems when in confined waters close to shore. The emergency response to NUC ships is to select the best risk control options, which is a challenge in restricted conditions (e.g., time limitation, resource constraint, and information asymmetry), particularly in inland waterway transportation. To enable a quick and effective response, this article develops a three-stage decision-making framework for NUC ship handling. The core of this method is (1) to propose feasible options for each involved entity (e.g., maritime safety administration, NUC ship, and ships passing by) under resource constraint in the first stage, (2) to select the most feasible options by comparing the similarity of the new case and existing cases in the second stage, and (3) to make decisions considering the cooperation between the involved organizations by using a developed Bayesian network in the third stage. Consequently, this work provides a useful tool to achieve well-organized management of NUC ships.

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Risk‐Based Decision Support Tools: Protecting Rail‐Centered Transit Corridors from Cascading Effects

Cited by 12 publications

References 41 publications

Revealing global hot spots of technological disasters: 1900–2013

Revealing global hot spots of technological disasters: 1900–2013

Passenger Rail Security, Planning, and Resilience: Application of Network, Plume, and Economic Simulation Models as Decision Support Tools

Three‐Stage Decision‐Making Model under Restricted Conditions for Emergency Response to Ships Not under Control

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