Intelligent Emergency Traffic Evacuation Spatial Decision Support System for Urban Disasters

Abstract

Extreme emergencies, whether caused by man or natural disasters, increasingly threaten urban life. Emergency traffic evacuation management is a complex procedure that includes routing evacuees, identifying exits, and designing traffic control. The evacuation routing models are vital to the evacuation planning problem. Real-time traffic evacuation management has become an increasingly attractive research topic, as a result of the rapid development and wide-spread deployment of a large number of applications for traffic data collection, processing, and dissemination. In this paper we extend our existing work on developing Intelligent Emergency Traffic Evacuation System (IETES) as a spatial decision support system based on Exploratory Spatial Data Analysis (ESDA) technology for urban disaster environment traffic control. The IETES has been enhanced with the Dynamic Traffic Assignment (DTA) model and Real-time Evacuation Algorithm (REA) to solve emergency traffic resource distribution and dynamic existing problems. ESDA performs spatial traffic data mining and data analysis functions from urban Geographic Information System (GIS) databases to set up relation links between dynamic traffic flows and varying disaster environment, which could improve DTA and REA efficiencies. An inference engine designed on rules and cases could make IETES capable of thinking, learning, and decision supporting.