Result Details
Towards Street-Level Traffic Analysis Using Waze Crowdsourced Data
Hynek Jiří, Ing., Ph.D., DIFS (FIT)
Burget Radek, doc. Ing., Ph.D., DIFS (FIT)
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.
Waze, Waze for Cities, traffic analysis, data processing, route planning
@inproceedings{BUT197683,
author="Magdaléna {Ondrušková} and Jiří {Hynek} and Radek {Burget}",
title="Towards Street-Level Traffic Analysis Using Waze Crowdsourced Data",
booktitle="IEEE Xplore",
year="2025",
series="2025 Smart City Symposium Prague (SCSP)",
pages="1--6",
publisher="Institute of Electrical and Electronics Engineers",
address="Prague",
doi="10.1109/SCSP65598.2025.11037686",
isbn="979-8-3315-2550-7",
url="https://ieeexplore.ieee.org/document/11037686"
}