On-the-Fly Nearest-Shelter Computation in Event-Dependent Spatial Networks in Disasters

Abstract

Numerous approaches have been proposed to solve shortest-path query problems in either static or time-dependent spatial networks; however, these approaches are neither appropriately nor efficiently used to find the nearest shelter with fastest paths in disaster evacuations. In disasters, segments of a path computed and saved as the fastest might become impassable. The nearest shelter differs for people depending on their locations and can also change on the basis of an unpredictable and highly dynamic edge cost (e.g., maximum passable vehicle speed), which is influenced by disaster events. To solve this problem, this paper proposes a dynamic network model, called an event-dependent network, to represent a spatial network in a disaster. Effective approaches using multiple algorithms are proposed for on-thefly computation of the nearest shelter and fastest paths in a disaster. A distributed system consisting of a server and multiple mobile clients using our approaches is presented for navigating the fastest paths for people to evacuate a disaster area. Real-world maps, such as a map of California, were used in our experiments. The results revealed that our approaches require less than 2 ms to find a new nearest shelter and its fastest paths which is faster than other approaches for solving the fastest path problem.

Congestion Control in Spatial Networks in Case of Disasters

“…Table 1 by taking m=n/2 as mentioned before. As this paper is working on the future scope of the ONSC [6] procedure so it has all the advantage of the existing system. This approach takes less than 3 to 4 ms to compute the nearest shelter and its shortest path which is faster than other existing approaches.…”

CLOTHO: A Large-Scale Internet of Things-Based Crowd Evacuation Planning System for Disaster Management

“…However, the scheme is limited to indoor environments. P. Tsai et al [8] presents a dynamic event-dependent network model. Rapid calculation and storage of the path becomes an important link in any emergency situation.…”

“…Song et al [1], [2] proposed an evacuation probability reasoning model based on the Markov decision process, B. Tang et al [3] presents a Robot-Assisted evacuation scheme and E. Boukas et al [4] presents an accurate Cellular Automaton simulation mod-el, V.S. Kalogeiton et al [5] presented a dynamic model based on bionics, M. Di Gangi [6] proposed a dy-D namic distributed evacuation model, L. Chen et al [7] presented a distributed emergency evacuation guidance framework based on wireless sensor networks, P. Tsai et al [8] proposed a dynamic event-related network model, and S. Mukherjee et al [9] presented a population dynamic model based on the Lagrangian method. Many representative emergency evacuation planning systems have been designed and built at the same time.…”