Controlling Traffic Signals TRIZ Cases

Selective bypass or wait instructions compare arrival times to prevent deadlocks between moving objects in fixed passages.

When adjacent work zones block predefined passages, the controller compares detour and wait times to avoid deadlocks and speed arrivals.

Temporary traffic signals and transport-mode reconfiguration let autonomous farm machines cross public roads safely with less downtime.

Partially separated roadways and coordinated vehicle assignment ease urban chokepoints while improving transit capacity, flow, and wait times.

Future traffic density is estimated from partial trajectories and ingress-point entry probabilities, reducing manual tuning for routing and control.

Surroundings analysis generates traffic control suggestions for operators, cutting manual assessment time in dynamic vehicle traffic situations.

Cooperative autonomous vehicles on separated roadways use real-time assignment and speed control to ease urban congestion and cut wait times.

Vehicles request crossing permission from an intersection controller that checks occupancy conflicts and sends movement instructions without traffic lights.

Grade-separated roadways and autonomous vehicle assignment increase urban transport capacity while preserving flexible routing and lower infrastructure cost.

Roadside sensing and hierarchical traffic control shift autonomy tasks off vehicles, cutting onboard complexity while enabling timely lane and route guidance.

Roadside units and hierarchical traffic centers shift sensing and control off vehicles, cutting onboard complexity while improving CAV safety.

Predicted vehicle speeds and adaptive signal timing reduce stop-start traffic, easing congestion, fuel use, and waiting time.

A circular radar array steers one roadside camera toward approaching vehicles, cutting camera count and data processing while keeping detection accuracy.

Sensor-guided chute switching keeps bulk material loading continuous across utility vehicles in multiple rows, reducing delays and spills.

Intersection images and reward feedback let reinforcement learning adjust signal phases with less training data and better congestion mitigation.