The relationship between cycle track width and the lateral position of cyclists, and implications for the required cycle track width

Schepers, Paul; Theuwissen, Eline; Nuñez Velasco, Pablo; Nabavi Niaki, Matin; van Boggelen, Otto; Daamen, Winnie; Hagenzieker, Marjan

doi:10.1016/j.jsr.2023.07.011

Cited by 6 publications

(1 citation statement)

References 38 publications

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“…Automatic Ivanišević et al [28] Ultrasonic sensor, video camera. Manual Schepers et al [18] LiDAR sensor, GPS, Arduino microcontroller. Automatic…”

Section: Semi-automaticmentioning

confidence: 99%

A Robust Methodology for Dynamic Proximity Sensing of Vehicles Overtaking Micromobility Devices in a Noisy Environment

Yap,

Paudel,

Yap

et al. 2024

Applied Sciences

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The safety of cyclists, e-scooters, and micromobility devices in urban environments remains a critical concern in sustainable urban planning. A primary factor affecting this safety is the lateral passing distance (LPD) or dynamic proximity of motor vehicles overtaking micromobility riders. Minimum passing distance laws, where motorists are required to maintain a minimum distance of 1.5 m when passing a cyclist, are difficult to enforce due to the difficulty in determining the exact distance between a moving vehicle and a cyclist. Existing systems reported in the literature are invariably used for research and require manual intervention to record passing vehicles. Further, due to the dynamic and noisy environment on the road, the collected data also need to be manually post-processed to remove errors and false positives, thus making such systems impractical for use by cyclists. This study aims to address these two concerns by providing an automated and robust framework, integrating a low-cost, small single-board computer with a range sensor and a camera, to measure and analyze vehicle–cyclist passing distance and speed. Preliminary deployments in Singapore have demonstrated the system’s efficacy in capturing high-resolution data under varied traffic conditions. Our setup, using a Raspberry Pi 4, LiDAR distance sensor, a small camera, and an automated data clustering technique, had a high success rate for correctly identifying the number of close vehicle passes for distances between 1 and 1.5 m. The insights garnered from this integrated setup promise not only a deeper understanding of interactions between motor vehicles and micromobility devices, but also a roadmap for data-driven urban safety interventions.

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“…Automatic Ivanišević et al [28] Ultrasonic sensor, video camera. Manual Schepers et al [18] LiDAR sensor, GPS, Arduino microcontroller. Automatic…”

Section: Semi-automaticmentioning

confidence: 99%

A Robust Methodology for Dynamic Proximity Sensing of Vehicles Overtaking Micromobility Devices in a Noisy Environment

Yap,

Paudel,

Yap

et al. 2024

Applied Sciences

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show abstract

Safe System approach for cyclists in the Netherlands: Towards zero fatalities and serious injuries?

Wegman,

Schepers

2024

Accident Analysis & Prevention

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Empirical study of bicycle traffic characteristics relevant for microscopic simulation

Pérez Castro,

Johansson,

Olstam

2025

Journal of Cycling and Micromobility Research

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The relationship between cycle track width and the lateral position of cyclists, and implications for the required cycle track width

Cited by 6 publications

References 38 publications

A Robust Methodology for Dynamic Proximity Sensing of Vehicles Overtaking Micromobility Devices in a Noisy Environment

A Robust Methodology for Dynamic Proximity Sensing of Vehicles Overtaking Micromobility Devices in a Noisy Environment

Safe System approach for cyclists in the Netherlands: Towards zero fatalities and serious injuries?

Empirical study of bicycle traffic characteristics relevant for microscopic simulation

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