Design a Smart Traffic Light Optimization System - System Design | WinJob

Problem Statement: Adaptive Urban Signal Control

Design Smart Traffic Light Optimization (SCOOT / SCATS / Surtrac-class adaptive control) for municipal traffic agencies and corridor operators. This section covers problem statement: adaptive urban signal control in the understanding phase.

Operational detail

Edge controllers run a 1–2 Hz control loop; cloud corridor optimizers publish timing plans every 30–120s with version pins.

Failure and edge cases

Cloud uplink loss with edge peer sync; stuck detector; plan hash mismatch on ACK; emergency preemption stuck on; optimizer timeout falling back to max-pressure.

Interview checkpoints

javaOne Dark Pro

1 public record IntersectionId(String corridorCode, int nodeId) {}

pythonOne Dark Pro

1 def clamp_green(sec: float, min_g: float, max_g: float) -> float:
2     return max(min_g, min(sec, max_g))

typescriptOne Dark Pro

1 export function phaseKey(ix: string, approach: string): string {
2   return `${ix}:${approach}`;
3 }

Why interviewers care

a Smart Traffic Light Optimization System interviews reward crisp scope, explicit trade-offs, and failure stories—not generic microservice diagrams.

Interview checkpoint

Name one failure story for Problem Statement: Adaptive Urban Signal Control that proves you understand real outages, not happy-path diagrams.

Key Highlights

•Replace fixed time-of-day plans with demand-responsive green splits and offsets
•Coordinate arterials for green waves while preventing gridlock spillback
•Fuse loops, radar, and camera counts into per-approach occupancy estimates
•Edge cabinet must fail-safe to flash or last validated plan when cloud partitions

1	def clamp_green(sec: float, min_g: float, max_g: float) -> float:
2	return max(min_g, min(sec, max_g))

1	export function phaseKey(ix: string, approach: string): string {
2	return `${ix}:${approach}`;
3	}