Video analysis of bicyclist and pedestrian movement on shared-use paths under daylight and electric lighting conditions—Method exploration

Yastremska-Kravchenko, Oksana; Laureshyn, Aliaksei; Rahm, Johan; Johansson, Maria; Niska, Anna; Johnsson, Carl; Carmelo D’Agostino · 2024 · OpenAlex-citations

DOI: 10.1016/j.jcmr.2024.100032

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Summary

This study addresses the methodological challenges of analyzing the microscopic travel behavior of bicyclists and pedestrians on shared-use paths under varying lighting conditions. Motivated by the need to enhance safety and promote sustainable transport modes like cycling and walking during twilight and darkness, the research investigates how electric lighting influences user behavior compared to daylight. The authors aim to validate a progressive video analysis methodology that utilizes drone technology to capture naturalistic, undisturbed data, thereby overcoming the limitations of laboratory settings and ground-based cameras which may alter participant behavior or require extensive consent procedures. The study was conducted at two shared-use path sites in Linköping and Lund, Sweden, during winter months to ensure distinct daylight and electric lighting conditions. Data collection involved 71 drone flights capturing video footage from a height of 120 meters. The researchers gathered two types of data: an "invited" sample of university students who were aware of being filmed, and a "naturalistic" sample of unaware, undisturbed road users. Video processing included stabilization, brightness adjustment, and calibration to convert pixel coordinates into 3D world positions. The analysis focused on three research questions: comparing invited versus naturalistic behavior, assessing the impact of lighting on speed and lateral positioning, and examining bi-directional interactions among bicyclists. Illuminance levels were measured manually and dynamically to correlate with speed profiles. The results indicate significant differences between the invited and naturalistic samples. Specifically, the average speed of invited bicyclists differed by more than 1 m/s compared to naturalistic users, suggesting that awareness of observation alters behavior. Furthermore, lighting conditions significantly affected micro-scale behavior; speed profiles and lateral positions varied between daylight and electric lighting scenarios. In the analysis of bi-directional interactions, bicyclists consistently employed evasive maneuvers, swerving to the right to maintain a "safe lateral passing distance" when encountering oncoming cyclists. This behavior was observed in both lighting conditions, though the specific dynamics of interaction were influenced by visibility. The study successfully demonstrated that drone-based video analysis can effectively capture precise trajectory data, including speed and lateral positioning, without requiring individual consent from the observed subjects. The significance of this research lies in its contribution to the methodological toolkit for traffic and micromobility studies. By validating the use of drones for collecting naturalistic behavioral data, the study provides a scalable approach to assessing how lighting infrastructure impacts user safety and comfort. The findings underscore the importance of considering actual, undisturbed behavior rather than self-reported or invited participant data when designing outdoor lighting solutions. This methodology offers a pathway to optimize lighting designs that enhance visibility and safety, potentially encouraging greater adoption of active transport modes during low-light hours.

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