Towards Street-Level Traffic Analysis Using Waze Crowdsourced Data

Traffic congestion represents a global challenge, significantly impacting the quality of life for urban residents. As a result, one of the main goals for traffic engineers is to optimize urban traffic flow. Advances in technology have introduced new diverse sources of traffic data, such as IoT-based sensors, mobile network operators, and crowdsourced platforms like Waze and Google Maps. This paper uses crowdsourced data from the Waze navigation application, obtained through the Waze for Cities program, to associate traffic congestions and incidents with specific street segments. The methodology is demonstrated through a usage scenario in Brno, employing two Waze datasets-Traffic Congestion and Traffic Incidents-alongside a municipal street network dataset. The proposed approach systematically maps traffic events to street segments, offering a detailed and citywide perspective on traffic conditions. To illustrate the application of this method, traffic events, and congestion levels are visualized along a computed route between two distinct locations. The route is generated using an optimized A* algorithm, modified to enhance calculation speed and efficiency.