A data-driven approach to manpower planning at U.S.–Canada border crossings

• Peace Arch is a busy crossing between Washington State and British Columbia.
• Vehicle arrival rates and service rates per booth both vary by time of day and week.
• We use a queuing model to capture dynamics, and estimate the model parameters.
• Given total work hours we derive a workforce schedule to minimize queueing delay.
• The suggested schedule reduces average delay by 18% relative to the current schedule.
• The suggested schedule yields more efficient and stable service levels.

We investigate the staffing problem at Peace Arch, one of the major U.S.–Canada border crossings, with the goal of reducing time delay without compromising the effectiveness of security screening. Our data analytics show how the arrival rates of vehicles vary by time of day and day of week, and that the service rate per booth varies considerably by the time of day and the number of active booths. We propose a time-varying queueing model to capture these dynamics and use empirical data to estimate the model parameters using a multiple linear regression. We then formulate the staffing task as an integer programming problem and derive a near-optimal workforce schedule. Simulations reveal that our proposed workforce policy improves on the existing schedule by about 18% in terms of average delay without increasing the total work hours of the border staff.