# A Sensor-Based and GIS-Linked Analysis of Road Characteristics Influencing Lateral Passing Distance Between Motor Vehicles and Bicycles in Austria

## TL;DR

This study uses sensor data and GIS to analyze how road features affect the safety distance between cars and cyclists in Austria, finding that rural roads and narrower lanes lead to smaller passing distances.

## Contribution

The study introduces a sensor-based and GIS-linked nonparametric method to analyze infrastructure effects on cyclist safety.

## Key findings

- LPD is significantly higher in rural areas compared to urban ones.
- Compliance with legal passing distance thresholds is lower in rural areas (19%) than in cities (40%).
- LPD correlates positively with lane width, speed limit, and road class.

## Abstract

What are the main findings?
Using 11,399 OpenBikeSensor (OBS) overtaking records linked to Austria’s national GIS road graph (GIP), the study reveals statistically robust patterns in lateral passing distance (LPD) between motor vehicles and cyclists related to infrastructure.LPD is significantly higher in rural areas than in urban ones, with compliance to Austria’s 2023 legal thresholds averaging ~40% in cities (≥1.5 m) and ~19% in rural areas (≥2.0 m). Small but consistent positive correlations were found between LPD and lane width, speed limit, and functional road class.

Using 11,399 OpenBikeSensor (OBS) overtaking records linked to Austria’s national GIS road graph (GIP), the study reveals statistically robust patterns in lateral passing distance (LPD) between motor vehicles and cyclists related to infrastructure.

LPD is significantly higher in rural areas than in urban ones, with compliance to Austria’s 2023 legal thresholds averaging ~40% in cities (≥1.5 m) and ~19% in rural areas (≥2.0 m). Small but consistent positive correlations were found between LPD and lane width, speed limit, and functional road class.

What are the implications of the main findings?
The results highlight that road geometry and network hierarchy shape overtaking safety, emphasising the need for more explicit cyclist allocation and physical separation in constrained or high-speed environments.The study demonstrates how sensor-based, GIS-linked nonparametric analysis can support evidence-based road design and policy evaluation regarding safe passing distances.

The results highlight that road geometry and network hierarchy shape overtaking safety, emphasising the need for more explicit cyclist allocation and physical separation in constrained or high-speed environments.

The study demonstrates how sensor-based, GIS-linked nonparametric analysis can support evidence-based road design and policy evaluation regarding safe passing distances.

Lateral passing distance (LPD) when motor vehicles overtake cyclists is a key safety metric, yet infrastructure-aware evidence remains limited. This study analyses 11,399 overtaking measurements from Austria’s OpenBikeSensor (OBS) project, spatially linked to the national road graph (GIP), with urban and rural networks examined separately. LPD was treated as a continuous dependent variable, and bivariate relationships were tested using nonparametric methods: Spearman’s rho/Kendall’s tau for metric predictors (speed limit, lane width, number of lanes) and Kruskal–Wallis tests with Dunn–Holm post hoc adjustments for categorical factors (Functional Road Class, Road Configuration, Infrastructure Type). Effect sizes and confidence intervals supported substantive interpretation. LPD was higher in rural than urban contexts, with compliance to Austria’s 2023 legal thresholds averaging 40% in cities (≥1.5 m) and 19% in rural areas (≥2.0 m). Positive correlations were found between LPD and lane width, speed limit, and functional class. The findings highlight infrastructure-sensitive patterns in sensor-generated LPD and emphasise the importance of clear cyclist allocation or physical separation, especially where high speeds or spatial constraints increase close-passing risk.