SUPERINTELLIGENT CITY
A city that responds
The same neighborhood. The same requests. Can coordination improve daily life?
Starts a fresh coordinated run and a fixed-loop baseline using your selected scenario and fleet.
Where the city is responding
Service schematic · stop counts are people/parcels awaiting pickup; amber marks demand. Pods: gray idle, amber collecting, green carrying, red unavailable.
Scenario & city priorities
Changing mode, scenario, priority or fleet starts a fresh comparison with identical demand. Explore starts with a hot afternoon. Advance 1 min explicitly progresses the city even while paused.
0s simulated
Live city twin
Measured outcomes
Same requests and fleet; elapsed simulation time| Metric | Chosen mode | Fixed loop |
|---|
Wait averages cover boarded residents and deliveries; assigned requests stay in the waiting counts until pickup. Energy estimates use 0.08 kWh per vehicle-km.
Service by neighborhood stop
Adaptive infrastructure
Illustrative kW model: cooling is protected; flexible charging follows solar surplus. Deferred charging and irrigation remain outstanding obligations; lower current demand does not mean the work disappeared. No real utility connections.
Request a service
Recognizes the five stops, appointments, groceries and accessibility needs. Review the plan below before requesting.
Why the city acted
Rules & resident control
Accessible requests require a four-seat cabin and receive priority. Aging prevents short pickups from always winning. Fairness gives waiting time four times the weight used by Efficiency. Both weigh pickup and journey time. Review flags the latest dispatch locally; it does not contact a person. Change priorities and restart to test another choice.
This is a deterministic concept simulation, not a general AI. Service stops stand in for future facilities; transfers, collision avoidance and real occupancy are not modeled. No personal data is collected. Predictions are scenario schedules, not learned forecasts.