Watch Out E-scooter Coming Through: Multimodal Sensing of Mixed Traffic Use and Conflicts Through Riders Ego-centric Views

TL;DR

This study investigates e-scooter rider behaviour across pedestrian-shared paths, cycle lanes, and roadways using a naturalistic, multimodal design with 23 participants wearing eye-tracking glasses and recording devices. Two sequential rounds on a predetermined route enabled robust comparisons of speed, gaze, head movements, and encounters across infrastructure types, analyzed with quantitative (PELT change-point detection, fixation metrics, and YOLO-based encounter detection) and qualitative (thematic video analysis) methods. Key findings show cycle lanes deliver higher average speeds with fewer speed-change points and reduced head movements, indicating safer and more stable operation, while roadways and mixed-use spaces present greater complexity and interaction demands. The results inform infrastructure planning and policy, advocating dedicated cycle lanes and targeted safety measures to improve mixed-traffic safety for e-scooter riders, with the dataset and code openly available for reproducibility.

Abstract

E-scooters are becoming a popular means of urban transportation. However, this increased popularity brings challenges, such as road accidents and conflicts when sharing space with traditional transport modes. An in-depth understanding of e-scooter rider behaviour is crucial for ensuring rider safety, guiding infrastructure planning, and enforcing traffic rules. This study investigated the rider behaviour through a naturalistic study with 23 participants equipped with a bike computer, eye-tracking glasses and cameras. They followed a pre-determined route, enabling multi-modal data collection. We analysed and compared gaze movements, speed, and video feeds across three transport infrastructure types: a pedestrian-shared path, a cycle lane and a roadway. Our findings reveal unique challenges e-scooter riders face, including difficulty keeping up with cyclists and motor vehicles due to speed limits on shared e-scooters, risks in signalling turns due to control lose, and limited acceptance in mixed-use spaces. The cycle lane showed the highest average speed, the least speed change points, and the least head movements, supporting its suitability as dedicated infrastructure for e-scooters. These findings are facilitated through multimodal sensing and analysing the e-scooter riders' ego-centric view, which show the efficacy of our method in discovering the behavioural dynamics of the riders in the wild. Our study highlights the critical need to align infrastructure with user behaviour to improve safety and emphasises the importance of targeted safety measures and regulations, especially when e-scooter riders share spaces with pedestrians or motor vehicles. The dataset and analysis code are available at https://github.com/HiruniNuwanthika/Electric-Scooter-Riders-Multi-Modal-Data-Analysis.git.

Figures (10)

• Figure 1: Different behaviours of P7 at turning points.
• Figure 2: Participant equipped with (A) Insta 360 camera, (B) Tobii Pro 3 Glasses, (C) Garmin bike computer and a following rider equipped with (D) GoPro Camera.
• Figure 3: The route.
• Figure 4: Sensor data analysis workflow.
• Figure 5: Distribution of Speed Change Points by Segment.