Smart Traffic Lights with Crowd Edge Intelligence

The smart traffic light (STL) system, as one of the key components of smart cities, relies on the ubiquitous intelligence of edge computing. Various artificial intelligence (AI) services are enhanced by the resource-constrained edge servers (ESs). However, deploying ESs and resources to establish a largescale service coverage can be extremely costly and requires tremendous efforts. In this paper, we unveil the potential of crowd edge intelligence by designing EdgeLight, a novel STL system that does not require dedicated deployment of ESs. The key insight of EdgeLight is to empower STLs by exploiting the edge devices carried by the surrounding crowds. The video frames captured by the traffic cameras will be offloaded to the crowd devices for processing, and the results are then sent to the traffic lights for smart control. We establish the above paradigm by proposing novel techniques including service discovery and task splitting. We implement EdgeLight and conduct real world experiments, and the comparison to existing STL solutions shows a significant improvement, with a 92.41% reduction in total latency and a 53.26% reduction in payment.