Planning Campus Loading Docks on Data, Not Anecdotes
Stanford used Safari AI to measure vehicle counts and dwell time at its loading zones, including the School of Medicine, to schedule deliveries and plan future construction.
Measured from existing loading zone cameras
Loading bays were scarce, and decisions about them ran on anecdotes.
Supplier trucks, vans and service vehicles competed for a handful of bays. Without counts, deliveries were hard to schedule around the peaks, and expansion plans rested on impressions of how busy the docks felt.
- Delivery peaks overwhelmed the loading zones
- There was no measured dwell time at each bay
- Facility expansions were planned on anecdotal evidence
Every delivery counted, classified and timed.
Connected to loading zone cameras via an on-premise server
Existing loading zone cameras fed a lightweight on-premise server Safari AI deployed, so video was processed on site.
Counted, classified and timed vehicles
Computer vision counted vehicles at each zone, classified trucks, vans and cars, read plates where authorized, and timed every stay at a bay.
Built baselines for scheduling and planning
Dashboards showed deliveries and dwell by bay and hour, giving logistics teams a schedule to plan around and planners evidence for new construction.
Note: Illustrative data
Smoother deliveries today, better buildings tomorrow.
Every vehicle at every loading zone was counted automatically, so deliveries can be scheduled around the real peaks.
Dwell time was measured at each scarce bay, showing where space runs out, and when.
Measured demand at the School of Medicine and across campus gave planners evidence for new loading facilities, instead of anecdotes.
Action Center: From Insight to Action.
Measurements like these feed Safari AI's Action Center, which flags a congested zone or an overstaying vehicle while there is still time to clear it.
- Real-time recommendations to improve KPIs via email, SMS, robocall, mobile app, Power BI, or API.
- Recommendations grounded in your own written SOPs, not generic best practices.
See this on your own campus.
Stanford measured loading zone traffic and dwell time from existing cameras to plan deliveries and construction. We'll walk through which docks and zones are the best fit for a pilot on your cameras.
