Towards Street-Level Traffic Analysis Using Waze Crowdsourced Data

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F26%3A0197683" target="_blank" >RIV/00216305:26230/26:0197683 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/11037686" target="_blank" >https://ieeexplore.ieee.org/document/11037686</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/SCSP65598.2025.11037686" target="_blank" >10.1109/SCSP65598.2025.11037686</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Towards Street-Level Traffic Analysis Using Waze Crowdsourced Data

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Towards Street-Level Traffic Analysis Using Waze Crowdsourced Data

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

IEEE Xplore

ISBN

979-8-3315-2550-7

ISSN

—

e-ISSN

—

Počet stran výsledku

6

Strana od-do

1-6

Název nakladatele

Institute of Electrical and Electronics Engineers

Místo vydání

Prague

Místo konání akce

Prague

Datum konání akce

28. 5. 2025

Typ akce podle státní příslušnosti

EUR - Evropská akce

Kód UT WoS článku

—