科学素养与生活应用·英语30篇（4）

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智能交通灯如何缓解拥堵

Adaptive traffic signals use radar, cameras, and connected vehicle data to monitor vehicle flow, waiting times, and pedestrian crossings in real time.
AI algorithms process this input every few seconds and adjust green-light durations dynamically—not according to fixed timers.
At intersections with low traffic, lights stay red longer for major roads but switch quickly when side-street cars or bikes are detected.
Coordinated systems along corridors create 'green waves,' letting vehicles travel several blocks without stopping if they maintain optimal speed.
Emergency vehicles trigger priority overrides—extending green phases or clearing paths ahead using V2I (vehicle-to-infrastructure) communication.
Data from thousands of intersections feeds city-wide dashboards that identify chronic bottlenecks and guide long-term road redesigns.
Energy use drops because idle time decreases, and LED signals themselves consume 85% less power than older incandescent units.
Privacy safeguards anonymize all camera footage and aggregate vehicle counts without tracking license plates or individuals.
Pilots in cities like Los Angeles and Helsinki show average commute delays falling by 12–25% after system upgrades.
This integration of sensing, computing, and civic infrastructure proves urban mobility can be both responsive and responsible.