Risk Reduction in Transport System in Emergency Conditions: A Framework for Network Design Problems

Vitetta, Antonino

doi:10.2495/safe210221

Cited by 6 publications

(4 citation statements)

References 22 publications

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“…Resources allocation is supported by ICT tools for monitoring [21] and specific methodologies for transport network design in evacuation conditions that became traffic control and management strategies [31][32][33][34][35]. Collected data, methods and models represent the content of a dynamic DSS that performs two different functions:…”

Section: Urban Planning Activitiesmentioning

confidence: 99%

Planning for Risk Reduction in the Transport System at Urban Level

Rindone¹,

Panuccio²

2023

IJTDI

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Globally, communities are committing to reduce the risks arising from different types of events. According to the classical formulation, risk comprehends occurrence and magnitude related to the potential damages produced by the event. Magnitude comprehends two components: exposure and vulnerability. It is possible to define specific actions for pursuing risk reduction according to the emergency management cycle and its phases (prevention, preparedness, response and recovery). This paper focuses on the preparedness phase and the transport planning process at the urban level. The process produces decisions about the configuration of the transport system and its components (supply, demand and interactions). Generally, planning decisions concern ordinary conditions but they have to be defined considering also implications for extraordinary situations. The objective of this paper is to present different issues of urban transport planning, one for each risk component. A single emergency event and the relative urban planning activities have been selected. The selected event represents a category of disasters with effects to be reduced with specific planned activities. The issues represent different problems and relative solutions to increase preparedness for emergency events at the urban level with a correct approach to risk assessment for supporting urban transport planning. The paper contributes to clarifying the characteristics of the risk's components and relative urban transport planning activities aimed at reducing risk.

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Section: Urban Planning Activitiesmentioning

confidence: 99%

Planning for Risk Reduction in the Transport System at Urban Level

Rindone¹,

Panuccio²

2023

IJTDI

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“…To effectively market their products and services, large corporations are constantly trying to predict and comprehend their clients behavior. However, determining users' behavioral patterns, their states, and the crucial actions they must take [39] can be challenging. According to our research, the HMM model is the most suitable method for addressing this issue.…”

Section: Problematic Issuementioning

confidence: 99%

Predicting user behavior using data profiling and hidden Markov model

Elbaghazaoui

Amnai

Fakhri

2023

IJECE

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<span lang="EN-US">Mental health disorders affect many aspects of patient’s lives, including emotions, cognition, and especially behaviors. E-health technology helps to collect information wealth in a non-invasive manner, which represents a promising opportunity to construct health behavior markers. Combining such user behavior data can provide a more comprehensive and contextual view than questionnaire data. Due to behavioral data, we can train machine learning models to understand the data pattern and also use prediction algorithms to know the next state of a person’s behavior. The remaining challenges for this issue are how to apply mathematical formulations to textual datasets and find metadata that aids to identify the person’s life pattern and also <span>predict the next state of his comportment. The main idea of this work is to use a hidden Markov model (HMM) to predict user behavior from social media applications by analyzing and detecting states and symbols from the user</span> behavior dataset. To achieve this goal, we need to analyze and detect the states and symbols from the user behavior dataset, then convert the textual data to mathematical and numerical matrices. Finally, apply the HMM model to predict the hidden user behavior states. We tested our program and identified that the log-likelihood was higher and better when the model fits the data. In any case, the results of the study indicated that the program was suitable for the purpose and yielded valuable data.</span>

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“…Moreover, authors as highlighted the possibility of limiting exposure through evacuation operating on the: transport system demand [6]; evacuation plans in emergency conditions [7], as well as on logistics operations (placing inventory management, rescue vehicles and teams) increasing both response and recovery phases. Furthermore, the transport system analysis in emergency conditions requires the identification of the process (emergency strategy) to be followed to reduce the effects that limit network operating conditions [8,9]. Thus, analysts who deal with emergencies draw up an appropriate evacuation plan share between natural (i.e., seismic, hydro-geological, volcanic, …) or anthropic disasters (i.e., accident with dangerous goods, nuclear event, …), their spatial and temporal effects: which could be punctual or diffuse; immediate or delayed (each risk component may be associated to a reference period, which ranges from some years to few seconds); and the effect on the transport system, which can be on the demand, network and demand -network interaction components.…”

Section: Introductionmentioning

confidence: 99%

An Overview on Decision Support Systems for Risk Management in Emergency Conditions: Present, Past and Future Trends

Gangi¹,

Belcore²,

Polimeni³

2023

IJTDI

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The transport system has a crucial role in economic and social processes. In emergency conditions, a resilient infrastructure has to keep supply chains active through mobilising people and goods. Accordingly, administrations are increasingly using tools such as decision support systems to assist decisionmakers through the evolution of crisis phenomena. The most modern decision support systems will have a modular structure, where acquisition and analysis layers must be recursive. Moreover, innovative solutions let to employ a wide range of data acquired through information and communication technologies and sources of information provided by volunteers. This trend makes realtime information and monitoring a cornerstone to allow decision-makers to implement plans considering the transport system's current conditions and the emergency phases. Thus, the present paper aims to provide a brief critical analysis of the approaches and models developed, highlighting the progress made and their limitations. Finally, the proposal for a general and flexible architecture is outlined; it allows the public administration to approach emergencies by extending the decision-making phases to the various professionals involved in the resolution for a specific instance, thus evaluating the system's optimum solutions in managing: the evacuation process; resources allocation and displacement.

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Risk Reduction in Transport System in Emergency Conditions: A Framework for Network Design Problems

Cited by 6 publications

References 22 publications

Planning for Risk Reduction in the Transport System at Urban Level

Planning for Risk Reduction in the Transport System at Urban Level

Predicting user behavior using data profiling and hidden Markov model

An Overview on Decision Support Systems for Risk Management in Emergency Conditions: Present, Past and Future Trends

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