Why Tacoma shuts down when it snows: A network optimization problem

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This storm seems to spark a lot of emotion. While watching Tacoma residents and California transplants loot Safeway for canned beans and Rainier, Minnesotans and Chicagoans laugh and jeer about how they walked to school in five feet of snow every winter without a jacket. Okay, maybe not quite so extreme, but how would I know – I haven’t left North Tacoma in five days because I, too, am a California transplant.

This past week has made glaringly clear the difference in protocols and design between cities that brace for snow vigilantly every winter, and cities like Tacoma and Seattle that only see a big snowstorm every several years. For example, when the newsletter CityLab asked cities the seemingly simple question of how many snow plows they had, they unwittingly stumbled upon a competition between cities to exaggerate their resources. It turns out that snow plowing prerogatives are a bigger deal then they seem; after the Seattle storm of 2008-2009, the Department of Transportation resigned. In Chicago, pressure from residents about unequal residential plowing encouraged the city to develop a snow plow tracking app. According to a resident in an Iowa small towns, farmers were even deputized during the winter for their snow removal efforts.

What is really comes down to is an optimization problem. Constrained by a limited budget, governments attempt optimize the mobility and safety of residents by making decisions like the number of snowplows to buy and store, the number of extra workers to hire, and which streets to plow. This looks very different in Seattle than in Chicago because Seattle has a smaller winter budget, forcing Seattle crews to optimize residents’ ability by maintaining access to public transit, rather than access to residential roads. The Tacoma crews will not be plowing any purely residential streets, instead prioritizing primary arterial routes and then secondary routes.

Computer software has made this task more efficient, but many governments are still using dated, simple allocation schemes. Some researchers propose that governments instead adopt a more complex but thorough plan, such as the authors of “Optimization Models for a Real-World Snow Plow Routing Problem” at Carnegie Mellon University. They develop three different models for optimizing snow clearing in Pittsburgh, PA, taking into account constraints like topology, vehicle restrictions, and the cost of fuel and salt. They propose a hybrid of these models for the most efficient optimization, while acknowledging that most cities do not use such methods. Given residents’ discontent and the unique set of constraints each city has, it may be time for more local governments to invest in this kind of analysis.


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