Abstract. 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.

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Given a transportation network, a vulnerable population, and a set of destinations, evacuation route planning identifies routes to minimize the time to evacuate the vulnerable population. Evacuation route planning is a vital components of efforts by civil authorities to prepare for both natural and man-made disasters (e.g., hurricanes, terrorist acts, etc). However, evacuation route planning is computationally challenging due to the size of transportation networks, the large number of evacuees, and capacity constraints. For example, the number of evacuees often far exceeds the bottleneck capacity, i.e., the minimum cut of a given network. Current approaches (e.g., linear programming and Capacity Constrained Route Planner (CCRP), a recently proposed evacuation planning algorithm) do not scale well because of intensive computation needs in order to produce the schedules of evacuees as well as routing plans.This paper presents innovative heuristics scalable to very large transportation networks. The Intelligent Load Reduction heuristic accelerates the routing computation by the reduction of evacuees using the bottleneck saturation. The performance of Intelligent Load Reduction is evaluated using real world scenarios. Results show that the Intelligent Load Reduction heuristic significantly improve the runtime of CCRP. We propose another heuristic named Incremental Data Structure. While the Intelligent Load Reduction gains performance increase by giving up the schedules of evacuees, the Incremental Data Structure heuristic can reduce calculation time of the CCRP algorithm by the enhanced data structures without affecting the outputs.

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Developing a model that facilitates the representation and knowledge discovery on sensor data presents many challenges. With sensors reporting data at a very high frequency, resulting in large volumes of data, there is a need for a model that is memory efficient. Since sensor data is spatio-temporal in nature, the model must also support the time dependence of the data. Balancing the conflicting requirements of simplicity, expressiveness and storage efficiency is challenging. The model should also provide adequate support for the formulation of efficient algorithms for knowledge discovery. Though spatio-temporal data can be modeled using time expanded graphs, this model replicates the entire graph across time instants, resulting in high storage overhead and computationally expensive algorithms. In this paper, we propose Spatio-Temporal Sensor Graphs (STSG) to model sensor data at the conceptual. logical and physical levels. This model allows the properties of edges and nodes to be modeled as a time series of measurement data. Data at each instant would consist of the measured value and the expected error. Also, we evaluate the model using methods to find interesting patterns such as growing hotspots in sensor data and present analytical comparison of the algorithms with methods based on existing models.

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