Technical Report

Predicting and Optimizing Border Wait Time Using Artificial Intelligence

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Artificial intelligence (including machine learning, data science, advanced analytics) has been successfully applied in many social and economic spheres, including weather forecasting, targeted political campaigns, automated detection of targets and events, optimization of system performance, and maximizing profits of various industries. In our work it is applied to predict and optimize Border Wait Time (BWT), which is one of the key performance metrics for the Agency. A novel scientific approach is developed to allow the Agency to predict and minimize BWT. The approach consists of two stages. In the first (Traffic prediction) stage, hourly rate of vehicle arrival at the border is estimated from the historical data using traditional machine learning techniques such as regression and classification. In the second (BWT prediction) stage, BWT is estimated from the predicted traffic as a function of available resources (the number of lanes) using the queuing theory implemented through a computer simulation.

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Technical Report
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This report presents the outcomes of the “Risk analysis of face and iris biometrics in border/access control applications” (CSSP-2013-CP-1020) study conducted by the Canada Border Services Agency in partnership with the University of Calgary through support from the Defence Research and Development Canada, Canadian Safety and Security Program (CSSP). This study relates directly to the technologies that apply to e-passportbased gate systems and iris-recognition-based registered traveller programs such as NEXUS. It also contributes to the development of a new generation of automated border control (ABC) systems and processes that are currently being developed by many countries, including Canada. The summarized outcomes include: establishing the terminology, metrics and tools for describing and analyzing ABC systems, analysis of issues with currently deployed systems, and investigation into further development of ABC and other traveller screening technologies within a larger e-border process that deals with automation of traveller clearance at the border.
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.
The U.S.-Canada Border: Cost Impacts, Causes, and Short to Long Term Management Options
  • J C Taylor
  • D Robideaux
  • G C Jackson
Taylor, J.C., Robideaux, D., Jackson, G.C., 2003. The U.S.-Canada Border: Cost Impacts, Causes, and Short to Long Term Management Options. Technical Report. Grand Valley State University, Allendale, Michigan
Simulation tools for e-border
  • Dmitry Gorodnichy
Dmitry Gorodnichy. Simulation tools for e-border, CBSA S&E Memorandum, March 30, 2015, Memo DG20150327 -Simulation tools for e-border.pdf (