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

2025-02-24

AI Summary

Electric scooters pose significant safety risks in mixed-traffic environments, necessitating fine-grained understanding grounded in real-world riding behavior. To address this, we conducted a naturalistic driving study with 23 riders, integrating first-person eye-tracking, vehicle-mounted multi-sensor data (IMU, GPS, brake/accelerator status), and synchronized multi-angle video recordings—yielding the first open-source multimodal naturalistic e-scooter dataset. Through spatiotemporal alignment and behavioral modeling, we identify novel risk mechanisms: speed constraints exacerbate car-following difficulties, induce steering instability, and reduce acceptance of shared road space. Empirical analysis demonstrates that dedicated bicycle lanes support the highest mean speed, lowest speed variability, and minimal head motion—establishing them as the optimal infrastructure for e-scooters. These findings provide data-driven foundations for safety-critical design, urban infrastructure planning, and regulatory policy formulation.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Application Domains: Mobility, Driving & Flight
• Natural Language Processing: Safety and Robustness

Application Category

• Security and Privacy: Large-scale security measurements
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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.

Problem

• Analyze e-scooter rider behavior
• Assess safety in mixed traffic
• Evaluate infrastructure impact on riders

Innovation

• Multimodal sensing captures rider behavior
• Ego-centric view analysis enhances safety insights
• Naturalistic study with sensors validates infrastructure