Sustainability Problem: Cities have an difficult time allocating resources to specific locations during large events. Inefficiencies and dangers occur when assistance is not provided in a specific location to meet the demand. Moreover, the arrival of resources can be delayed due to traffic, crowd sizes, and routing issues.
- Team at Northwestern developed an algorithm to optimize, in real time, staffing locations and demand for medical medical volunteers and aid workers at Chicago’s marathon.
- The team created a mobile data visualization dashboard that provides real time analysis. The mobile dashboard also allow staff to enter real time data.
- The machine learning algorithm uses historical observations to predict and determine optimal location for medical tents. In addition, the algorithm predicts likely number of individuals seeking medical attention at specific tents base on historical and real time data.
- Dashboard provides optimal routing for medical.
- City Chicago
- Marathon organizers
- Marathon participants
- Medical staff
- City Chicago Police
- Marathon onlookers
- Northwestern University – School Engineering / Medical School
- Improve the training dataset of the algorithm / algorithm itself
- Provide service to marathon organizers and/ or any large event organizers
- Provide algorithm to Chicago Police Department for future events.