Microsoft holds an event every year called Location Summit. It's a two day event where we invite scientists and others who are deep into the mapping technologies to come and share in the research they are doing. I got looped into this thing to cover for a friend of mine who had to present "10 Demos in 10 Minutes" I only got through 9 because... well, let's just say that the internet isn't on all the time. After my session I saw a presentation by a Computer Science Professor at the University of Minnesota named, Shashi Shekhar who had created a new system that determines evacuation plans for cities when emergencies happen such as a terrorist attack or some other disaster that requires lots of people to leave an area quickly.

This new system he designed is called, Capacity Constrained Route Planner (CCRP) and it uses a new system of heuristics that can scale to handle calculations for millions of people. Not only that, the methodology and reducing the total time from a length of time where most die to times where most are saved. From his own words here is below from his Abstract for his research.

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. Current methods are based on linear programming paradigm and suffer from two limitations. First, they may not scale up to large (e.g. 100,000 nodes and 1 Million people) transportation networks in urban scenarios as they use time-expanded networks requiring large amount of computer storage and aim at computing optimal path incurring exorbitant computational costs. Second, they require users to provide an estimate of an upper bound on the total evacuation time. Incorrect estimate may lead to failure of the paradigm.

We present a new heuristic approach, namely Capacity Constrained Route Planner (CCRP), to quickly identify feasible evacuation plan. This method may be used to provide an upper bound on optimal evacuation time for the methods based on linear programming paradigm. Alternatively, our method may be used to determine plausible evacuation plans for very large transportation networks when resource constraints or dynamic conditions make it infeasible or uninteresting to determine the optimal routes. Proposed CCRP approach models has two key ideas. First, it models node/edge attributes as functions of time rather than fixed numbers. Thus node/edge capacities, node occupancies, etc. are modeled as time-series. Second, it iteratively considers all pairs of sources and destinations. In each iteration, it schedules evacuation of a group of evacuees across the closest source-destination pair. Special graphs construction is used eliminate redundant computation in this step. Experiments with real and synthetic transportation networks show that the proposed approach scales up to much larger networks, where software based on linear programming method crashes. For smaller networks, where software based on linear programming can be used, CCRP produces high quality solutions with evacuation times comparable to those achieved by linear programming methods.

Evaluation of our methods for evacuation planning for a disaster at the Monticello nuclear power plant near Minneapolis/St. Paul Twin Cities metropolitan area shows that the new methods lowered evacuation time relative to existing plans by identifying and removing bottlenecks, by providing higher capacities near the destination and by choosing shorter routes. In 2005, CCRP was used for evacuation planning (transportation component) for the Minneapolis-St. Paul twin-cities metropolitan area. It facilitated explorations of scenarios (e.g. alternative locations and times) as well as options (e.g. alternative transportation modes of pedestrian and vehicle). It also led to an interesting discovery that walking able-bodied evacuees (instead of letting them drive) reduces evacuation time significantly for small area (e.g. 1-mile radius) evacuations.

In future work, we plan to formally characterize the quality of solutions identified by the CCRP approach. We will explore new ideas, e.g. phased evacuations and contra-flow, to further reduce evacuation times. In addition, we would like to improve modeling of other transportation modes such as public transportation.

This is actually in use in the Minneapolis / St. Paul area and got covered by the local Fox affiliate TV station Fox 9 in Minneapolis. Here is a clip (apologies for the resolution, will try to get a better one soon). I think its really cool that scientists are using Virtual Earth to do such great work.


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