A Low Cost Edge Computing and LoRaWAN Real Time Video Analytics for Road Traffic Monitoring

Traffic congestion is a major problem in many cities. It happens due to the demand-supply imbalance in the transportation network and poor management. Traffic flow slows down when the number of vehicles that travels on the road increases or the roadway capacity decreases due to various reasons. In order to solve this problem, different solutions are proposed to provide reliable, real-time transport management services in an Intelligent Transportation System (ITS). In this paper, we propose a novel real-time video analytics using low-cost IoT devices and LoRaWAN networks to realize new services and applications that include traffic management through IoT edge computing. The use of LoRaWAN for such application is our main contribution. We retrain YOLO v3 object detection machine learning model (transfer learning) for vehicle detection and counting, to make it lightweight and fast enough to be able to run on a Raspberry Pi, a single-board computer with limited RAM. The edge node, with low-cost smart camera and connectivity through LoRaWAN networks counts the number of vehicles using real-time video analytic and report only traffic count to the server. This experimental work provides insight into the applicability of a low-cost IoT system to traffic management with a resource-constrained environment. Real-world video analysis of vehicle detection and counting show the effectiveness of the designed solution. The results demonstrate the effectiveness of the proposed approach.