Cohort-based evacuation time estimation using TSIS-CORSIM

Park, Sunghyun; Sohn, Seokwoo; Jae, Moosung

doi:10.1016/j.net.2020.11.028

Cited by 2 publications

(2 citation statements)

References 3 publications

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“…Equations ( 3)-( 5) should be calculated sequentially, and the results of Equations ( 3) and ( 4) should be compared. The larger value should be compared with Equation (5), and the smaller value should be selected as the short-term dispersion factor for the given wind speed and atmospheric stability.…”

Section: Monin-obukov Length/mmentioning

confidence: 99%

“…Several scholars have investigated this aspect. Park et al used TSIS-CORSIM to collect data and construct a multi-model network to identify factors influencing evacuation time uncertainty [5]. Li et al proposed a Fuzzy Gradient Chance-Constrained Evacuation Model (FGCCEM) to address various uncertainties in evacuation management and planning [6].…”

Section: Introductionmentioning

confidence: 99%

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An Integrated Solution for Nuclear Power Plant On-Site Optimal Evacuation Path Planning Based on Atmospheric Dispersion and Dose Model

Ren,

Zhang,

Zheng

et al. 2024

Sustainability

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Safety in nuclear energy utilization is crucial. In the event of a radioactive release incident, coupled with meteorological uncertainties, a radioactive plume can impact personnel evacuation. This paper presents an integrated solution for radionuclide release accident assessment and emergency evacuation decision making. The solution consists of three processes: atmospheric dispersion calculation, dose calculation, and path planning. The individual processes are connected through data exchange, thus allowing users to choose specific models based on experience. The proposed scheme combination is the Gaussian plume model, the dose conversion factor method, and an improved Dijkstra’s path planning algorithm. This algorithm, combined with dispersion and dose results, weighs nodes using the moving expected dose, facilitating the path with minimum dose risk. A program for Atmospheric Diffusion and Dose Calculation (ADDC) is developed based on the recommended scheme. Advantages include ease of use, minimal data requirements, data accessibility, and efficient evacuation. Dose estimates and optimal evacuation routes can be obtained quickly and at very low cost in response to rapidly changing environmental conditions. In a case study at a Chinese planned nuclear plant, we consider a spent fuel pool water loss scenario, assessing dose risks from 2020 to 2022 meteorological statistics. In dose calculation, results reveal that during an SFP drying accident, the radiation dose in the core area (100 m away) can reach 30–150 mSv within 2 h, and at 500 m away, it can reach 5–15 mSv. The dose in all downwind directions can drop below 250 mSv within 60 m. In path planning, results reveal the program is capable of accurately and efficiently calculating the minimum dose evacuation route. The program’s path reduces the effective dose by up to 67.3% compared to the shortest route, enhancino safety, and guiding post-accident decision making and planning.

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Section: Monin-obukov Length/mmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Integrated Solution for Nuclear Power Plant On-Site Optimal Evacuation Path Planning Based on Atmospheric Dispersion and Dose Model

Ren,

Zhang,

Zheng

et al. 2024

Sustainability

View full text Add to dashboard Cite

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A Novel Markov Model-Based Traffic Density Estimation Technique for Intelligent Transportation System

Beenish

Javid

Fahad

et al. 2023

Sensors

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An intelligent transportation system (ITS) aims to improve traffic efficiency by integrating innovative sensing, control, and communications technologies. The industrial Internet of things (IIoT) and Industrial Revolution 4.0 recently merged to design the industrial Internet of things–intelligent transportation system (IIoT-ITS). IIoT sensing technologies play a significant role in acquiring raw data. The application continuously performs the complex task of managing traffic flows effectively based on several parameters, including the number of vehicles in the system, their location, and time. Traffic density estimation (TDE) is another important derived parameter desirable to keep track of the dynamic state of traffic volume. The expanding number of vehicles based on wireless connectivity provides new potential to predict traffic density more accurately and in real time as previously used methodologies. We explore the topic of assessing traffic density by using only a few simple metrics, such as the number of surrounding vehicles and disseminating beacons to roadside units and vice versa. This research paper investigates TDE techniques and presents a novel Markov model-based TDE technique for ITS. Finally, an OMNET++-based approach with an implementation of a significant modification of a traffic model combined with mathematical modeling of the Markov model is presented. It is intended for the study of real-world traffic traces, the identification of model parameters, and the development of simulated traffic.

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Cohort-based evacuation time estimation using TSIS-CORSIM

Cited by 2 publications

References 3 publications

An Integrated Solution for Nuclear Power Plant On-Site Optimal Evacuation Path Planning Based on Atmospheric Dispersion and Dose Model

An Integrated Solution for Nuclear Power Plant On-Site Optimal Evacuation Path Planning Based on Atmospheric Dispersion and Dose Model

A Novel Markov Model-Based Traffic Density Estimation Technique for Intelligent Transportation System

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