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

TL;DR

This study uses multimodal sensing to analyze e-scooter rider behavior in naturalistic settings across different infrastructure types, revealing safety challenges and infrastructure preferences to inform better urban planning.

Contribution

It introduces a novel multimodal data collection method using ego-centric views to analyze e-scooter rider behavior in real-world environments.

Findings

Cycle lanes support higher speeds and stability for e-scooter riders.

Shared spaces pose safety challenges due to rider behavior and infrastructure limitations.

Ego-centric sensing effectively captures rider behavior in naturalistic urban settings.

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.