By Eric DuBois
Back in August 2011, I was in Massachusetts preparing for my first season as a teacher at Nature’s Classroom in Colebrook, CT. Colebrook is located in the Berkshire Mountains in the rural northwest corner of Connecticut. To get there requires driving for about an hour on small state and local roads. Unfortunately, at the same time as I was preparing to leave, Hurricane Irene was hitting southern New England and by the time it was over, the region was a mess.
Many of the rivers across the region were flooded. As many of the roads in the region either follow, or at a minimum cross, rivers, they had become impassable in the aftermath of the storm. To further complicate matters, the huge number of downed trees and powerlines added additional roadblocks to my journey. From the outside this appears to be a fairly straightforward OR problem. All we must do is find the new shortest path. Right?
Unfortunately, in this instance as in most real disasters, the reality does not fit the model very well. Specifically, most shortest path problem models assume perfect knowledge of the network or at the very least a good working knowledge of the probabilities that the roads will be open. Two things worked against that in Connecticut.
- The ‘interdicted’ locations change over time. As the water proceeds down the mountains it floods progressively larger watercourses. So when I first start out the major mountain streams may have flooded the local roads and the smaller rivers are beginning to flood. By the time that I have reached the mountains, those small rivers have reached flood stage. Worse yet, there is no guarantee that the local roads are now passable since they may have washed out or become impassible with debris left by the receding water.
- More importantly, information is quite scarce on where the blockages are. In the actual event, the State Police could provide little more than a rough sketch of what roads outside of the suburbs were underwater and almost no information on where debris had blocked transit. It took me over two and a half hours to find a navigable route to Colebrook and that was after checking with the police and news sources for an hour. Other teachers were not so lucky, having not properly checked the news, and didn’t arrive until the day after.
The moral of this tale is understanding the importance and difficulty in maintaining an accurate understanding of the state of the system during a disaster. Significant literature is now looking at the use of social media and crowd-sourced information to flesh out the details of a disaster. Unfortunately, this information is often inaccurate and misleading. This can arise from something as simple as the lack of characters allowed in a tweet up to a complete misunderstanding of the situation by the individuals on the ground. Unlike state police or disaster responders, these individuals have likely had no formal training in communicating accurately or previous disaster experience to gauge the conditions by. So while the information provided may be better than nothing, it is certainly no panacea.
I can certainly see using the information to provide a gauge to determining where infrastructure restoration is most likely needed. However, in the eventuality that we need to route emergency vehicles around these roadblocks, probabilities and guessing is not, in my opinion, a terribly great way to go about finding the optimal choice. As operations researchers, how do you feel we can make use of this new source of data to inform out disaster response planning?