Methods
The traffic solution was tested in three different municipalities: Cascais, Loures, and Oeiras. Nine locations were identified (L1- L9) with a total of seventeen vision sensors deployed in the public lighting infrastructure (see Figure S1 in the Supporting Information section). With the objective of measuring the total traffic flow, three locations have power available 24 hours a day (L1-3) and correspond to key intersections where high traffic flow and conflicts are expected, especially during rush hours. The other locations, L4-9, only have energy available during the night (powered by switched grids) and correspond to individual roads. These locations correspond to residential areas – or accesses to residential areas – where the goal was to identify moments of potential excess noise pollution.
Data was collected over two months (April and May) with random interruptions due to unexpected down time of the devices. Nevertheless, approximately 30 days of data was collected per device. The vision sensors were connected to Jetson Xavier NX devices running Jetpack 5.0.2 GA with all software deployed and managed via docker containers. Inferencing is performed by a modified version of the YOLO-v7 model [3, 4] that was adapted for improved performance when fed with the top-down video streams, typical when sensors are deployed on lighting poles. The Deep SORT algorithm [5] was used to track objects detected by the vision model. These objects are then counted when they cross a barrier configured for each camera perspective. For this purpose, we consider the sum of all vehicles (cars, buses, trucks, motorbikes, and bicycles).
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