Speeding Behaviour Across Urban Street Segments Using Low-Cost Telraam Sensors: Evidence from Žilina, Slovakia

Road safety is an important aspect of sustainable mobility in urban transportation, with excessive speed a primary contributor to the severity of traffic accidents. Monitoring it is a challenge for current transport systems; however, the current state of open data technologies allows for the collection, monitoring and analysis of traffic data using low-cost devices that can help local citizens verify and support their concerns about dangerous situations in specific areas. This study presents the results of speeding on various street segments in the city of Žilina, Slovakia, based on the data gathered from Telraam low-cost traffic sensors. Differences among the various locations were tested with nonparametric statistical tests (Kruskal–Wallis test and Dunn’s post hoc comparisons). The data provide insight into the largely passenger-car traffic at all sites and show significant variation in the proportions of bikes, pedestrians, and heavy vehicles. The particular differences in speeding behaviour in the spatial context were observed in some locations: Oslobodenia, Okruzna, and Zavodskeho showed the highest V85 values and increased proportions of vehicles exceeding 50 km/h, while Hviezdoslavova, Osvety, and Univerzitna showed substantially lower operating speeds. The findings indicate that some residential collector road segments record a higher share of overspeeding than the main road. Such findings suggest that low-cost devices may be useful technology to support citizens and their initiatives in a real way, enhancing road safety. The findings suggest that low-cost open-data sensors can support preliminary speed-management screening by identifying locations with elevated speeding indicators that warrant more detailed investigation.

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Publication Details

Journal
Urban Science
Published
2026-09-01
DOI
https://doi.org/10.3390/urbansci10090502
Primary Topic
Traffic Prediction and Management Techniques
Type
article
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article

Speeding Behaviour Across Urban Street Segments Using Low-Cost Telraam Sensors: Evidence from Žilina, Slovakia

Marián Gogola, Dominik Krčmár
Urban Science
Traffic Prediction and Management Techniques
article

Speeding Behaviour Across Urban Street Segments Using Low-Cost Telraam Sensors: Evidence from Žilina, Slovakia

Marián Gogola, Dominik Krčmár
article en

Abstract

Road safety is an important aspect of sustainable mobility in urban transportation, with excessive speed a primary contributor to the severity of traffic accidents. Monitoring it is a challenge for current transport systems; however, the current state of open data technologies allows for the collection, monitoring and analysis of traffic data using low-cost devices that can help local citizens verify and support their concerns about dangerous situations in specific areas. This study presents the results of speeding on various street segments in the city of Žilina, Slovakia, based on the data gathered from Telraam low-cost traffic sensors. Differences among the various locations were tested with nonparametric statistical tests (Kruskal–Wallis test and Dunn’s post hoc comparisons). The data provide insight into the largely passenger-car traffic at all sites and show significant variation in the proportions of bikes, pedestrians, and heavy vehicles. The particular differences in speeding behaviour in the spatial context were observed in some locations: Oslobodenia, Okruzna, and Zavodskeho showed the highest V85 values and increased proportions of vehicles exceeding 50 km/h, while Hviezdoslavova, Osvety, and Univerzitna showed substantially lower operating speeds. The findings indicate that some residential collector road segments record a higher share of overspeeding than the main road. Such findings suggest that low-cost devices may be useful technology to support citizens and their initiatives in a real way, enhancing road safety. The findings suggest that low-cost open-data sensors can support preliminary speed-management screening by identifying locations with elevated speeding indicators that warrant more detailed investigation.

Urban ScienceVol. 10(9)
University of Žilina (SK)
Sustainable cities and communities
Openalex Percentile: Top 14%
Traffic Prediction and Management Techniques
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