Smarter Systems Thinking for Smart Cities: Using Simulation to Explore the Robustness of Boston’s Urban Snow Management

Urban management professionals frequently face uncertain conditions when making decisions. Smart city efforts have helped provide additional data, but smarter systems are necessary to understand the delays and feedback loops of policy implementation. As a case study of this phenomenon, we develop a simulation of Boston’s response to the record-breaking blizzards of Winter 2014-2015. Specifically, the paper investigates how operational levers responded to extreme snowfall events and what alternative scenarios may have reduced the city’s direct costs. The authors examined the cumulative impacts of five what-if weather scenarios and ten policy alternatives across the primary objective function of minimizing total direct costs to the city and a secondary operational criterion of constituent satisfaction. The model, calibrated for the amplitude of actions over the winter season’s real-time dataset, finds that delayed municipal reactions compounded the negative impacts of exceptional and prolonged cold. Sensitivity analysis suggests that the amount of snow plowed into roadside banks was a tipping point for management strategies. Contrary to the city’s immediate long-term response of purchasing more equipment, improving trucking capabilities will not ameliorate low-probability high-volume winters and carries additional cost management risks. Instead, we recommend that the city contract additional labor for plowing and consider investing in real options that allow it to expand snow farm capacity. This indicates that staffing flexibility and urban planning strategies can play an important role in mitigating the variable costs of clearing winter precipitation.

A draft of this paper was presented at the 2017 International Conference of the Systems Dynamics Society in Cambridge, MA.