Improving Urban Traffic Evacuation Capability in Emergency Response by Using Smart Phones

Zhang, Ping; Liu, Yi; Yang, Rui; Zhang, Hui; Gong, Zengli

doi:10.1007/978-3-319-45835-9_21

Cited by 4 publications

(2 citation statements)

References 19 publications

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“…To prevent this, it is important to provide real-time evacuation information to evacuees. In this respect, real-time evacuation guidance using mobile applications is drawing attention from researchers (e.g., [35][36][37]). By applying the methodology proposed in this study, it is possible to minimize the casualties caused during the evacuation process by selecting the evacuation route when flooding occurs.…”

Section: Flood Hazard and Walking Evacuationmentioning

confidence: 99%

Flood Evacuation Routes Based on Spatiotemporal Inundation Risk Assessment

Lee

Kim

Han

et al. 2020

Water

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For flood risk assessment, it is necessary to quantify the uncertainty of spatiotemporal changes in floods by analyzing space and time simultaneously. This study designed and tested a methodology for the designation of evacuation routes that takes into account spatial and temporal inundation and tested the methodology by applying it to a flood-prone area of Seoul, Korea. For flood prediction, the non-linear auto-regressive with exogenous inputs neural network was utilized, and the geographic information system was utilized to classify evacuations by walking hazard level as well as to designate evacuation routes. The results of this study show that the artificial neural network can be used to shorten the flood prediction process. The results demonstrate that adaptability and safety have to be ensured in a flood by planning the evacuation route in a flexible manner based on the occurrence of, and change in, evacuation possibilities according to walking hazard regions.

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Section: Flood Hazard and Walking Evacuationmentioning

confidence: 99%

Flood Evacuation Routes Based on Spatiotemporal Inundation Risk Assessment

Lee

Kim

Han

et al. 2020

Water

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“…The system can support emergency managers when designing and evaluating more realistic traffic evacuation plans to produce the best evacuation scheme. With the development of emerging technologies, some scholars have begun to focus on intelligent evacuation [40][41][42][43].…”

Section: Introductionmentioning

confidence: 99%

Assessing Emergency Shelter Demand Using POI Data and Evacuation Simulation

Chen

Yao

Zhai

et al. 2020

IJGI

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Mapping the fine-scale spatial distribution of emergency shelter demand is crucial for shelter planning during disasters. To provide shelter for people within a reasonable evacuation distance under day and night disaster scenarios, we formed an approach for examining the distribution of day and night shelter demand at the plot-scale using point of interest (POI) data, and then analyzed the supply and demand status of shelters after an evacuation simulation built in Python programming language. Taking the downtown areas of Guangzhou, China as a case study, the results show that significant differences exist in the size and spatial distribution of shelter demand in daytime and nighttime, and the total demand is 7.929 million people, which is far larger than the resident population. The average evacuation time of all 16,883 routes is 12.6 min, and after the evacuation, 558 of 888 shelters exceed their capacity to varying degrees, accounting for 62.84% of the total, indicating that the shelters cannot completely receive the potential evacuees. The method proposed in this paper provides a direct quantitative basis for the number and size of new shelter resources being planned during urban renewal activities, and form a reference for land reuse and disaster prevention space organization in future urban planning.

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