Capacity Constrained Routing Algorithms for Evacuation Planning: A Summary of Results

• Published in: Advances in Spatial and Temporal Databases pp. 291 - 307
• Main Authors: Lu, Qingsong, George, Betsy, Shekhar, Shashi
• Format: Book Chapter Conference Proceeding
• Language: English
• Published: Berlin, Heidelberg Springer Berlin Heidelberg 2005; Springer
• Series: Lecture Notes in Computer Science
• Subjects: Applied sciences; Computer science; control theory; systems; evacuation planning; Exact sciences and technology; Information systems. Data bases; Memory organisation. Data processing; routing and scheduling; Software; transportation network; Performance evaluation; Information system; Terrorism; Time series; Linear programming; Routing; Disaster; Linear time; Modeling; Routing algorithm; Upper bound; Urban transportation; Transportation network; Optimal solution; Heuristic method; Occupation time; Planning; Safety; Emergency; Route; Spatial database
• ISBN: 3540281274; 9783540281276
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/11535331_17

Evacuation planning is critical for numerous important applications, e.g. disaster emergency management and homeland defense preparation. Efficient tools are needed to produce evacuation plans that identify routes and schedules to evacuate affected populations to safety in the event of natural disasters or terrorist attacks. The existing linear programming approach uses time-expanded networks to compute the optimal evacuation plan and requires a user-provided upper bound on evacuation time. It suffers from high computational cost and may not scale up to large transportation networks in urban scenarios. In this paper we present a heuristic algorithm, namely Capacity Constrained Route Planner(CCRP), which produces sub-optimal solution for the evacuation planning problem. CCRP models capacity as a time series and uses a capacity constrained routing approach to incorporate route capacity constraints. It addresses the limitations of linear programming approach by using only the original evacuation network and it does not require prior knowledge of evacuation time. Performance evaluation on various network configurations shows that the CCRP algorithm produces high quality solutions, and significantly reduces the computational cost compared to linear programming approach that produces optimal solutions. CCRP is also scalable to the number of evacuees and the size of the network.