Domestic Use of E-Cargo Bikes and Other E-Micromobility: Protocol for a Multi-Centre, Mixed Methods Study DOI Open Access
Ian Philips, Labib Azzouz, Alice de Séjournet

et al.

International Journal of Environmental Research and Public Health, Journal Year: 2024, Volume and Issue: 21(12), P. 1690 - 1690

Published: Dec. 19, 2024

Physical inactivity is a leading risk factor for non-communicable diseases. Climate change now regarded as the biggest threat to global public health. Electric micromobility (e-micromobility, including e-bikes, e-cargo bikes, and e-scooters) has potential simultaneously increase people’s overall physical activity while decreasing greenhouse gas emissions where it substitutes motorised transport. The ELEVATE study aims understand impacts of e-micromobility, identifying people, places, circumstances they will be most beneficial in terms improving health also reducing mobility-related energy demand carbon emissions. A complex mixed methods design collected detailed quantitative qualitative data from multiple UK cities. First, nationally representative (n = 2000), city-wide 400 each three cities; total 1200), targeted area surveys 996) on travel behaviour, levels activity, vehicle ownership, use, well attitudes towards e-micromobility. Then, provide insights an understudied type 49 households were recruited take part bike one-month trials. Self-reported participants validated with objective data-using such GPS trackers smartwatches’ recordings routes activities. CO2 e-micromobility use calculated. Participant interviews provided information preferences, expectations, experiences, barriers, enablers

Language: Английский

Selecting E-bikes using a Multi-Criteria Integrated Analytic Hierarchy Approach for Sustainable Transportation Option DOI Creative Commons
Rohit Bansal,

Yasmeen Ansari,

Neha Gupta

et al.

Global Transitions, Journal Year: 2025, Volume and Issue: 7, P. 94 - 108

Published: Jan. 1, 2025

Language: Английский

Citations

0
Understanding e-scooter rider crash severity using a built environment typology: A two-stage clustering and random parameter model analysis DOI Creative Commons
Amirhossein Abdi, Steve O’Hern

Accident Analysis & Prevention, Journal Year: 2025, Volume and Issue: 215, P. 108018 - 108018

Published: March 30, 2025

E-scooters are an emerging transport mode that is transforming urban mobility; however, their proliferation has raised concerns about safety. This study combines UK e-scooter crash data with built environment characteristics from the locations. A two-stage framework was followed: first, a typology of environments developed using K-means++; second, severity within each cluster analysed random parameter binary logit model. Four clusters were identified: (1) car-centric and mixed-use zones, (2) commercial industrial (3) intersection-dense areas, (4) residential central areas. Collisions motor vehicles, younger riders, higher speed limits most common risk factors across clusters, first two showing impact these on likelihood severe crashes. In second riding carriageway significantly increased injury severity. cluster, three collision types significant, more than in other where only side-impact collisions significant. indicates high e-scooter-motor vehicle friction cluster. Among all types, head-on outcomes others. third fourth peak hours associated lower crashes, while this variable showed opposite The results highlight consideration surrounding paramount when analysing severity, as unique contributing identified specific to type, along varying magnitudes or directions marginal effects.

Language: Английский

Citations

0
Analysis of Road Damages for Micro Mobility Vehicles Via Synthetic Data: Three-Axis Accelerometer-Based Machine Learning DOI Creative Commons
Ömer Kaya

Brilliant Engineering, Journal Year: 2025, Volume and Issue: 6(1), P. 1 - 8

Published: Jan. 1, 2025

The effect of road damages on the surface driver safety and comfort depends damping mechanism vehicle. Since micro mobility vehicles have small wheels, damage affects them with varying severity. This study aims to determine based response bicycles, e-bikes, e-scooters surface. In order achieve this goal, firstly synthetic data approach is adopted. There are 10 000 samples in set it was produced Google Colab Python. These simulate collected a three-axis accelerometer. for distributions represent real world, flat roads (undamaged), cracks potholes determined as 7 000, 2 1 samples, respectively. prevent distribution from being biased eliminate overfitting problem, unbalanced class sensor noise simulated. Random Forest algorithm used classification damages. accuracy rate 95%. addition, K-Means clustering helps analyze how each vehicle type responds Silhouette Score 0.543, which shows intertwined clusters separate they other. results confirm that proposed integrates well real-world data. To validate model performance, researchers should collect accelerometer alongside simulated

Language: Английский

Citations

0
Examining Biological Motion as a Potential Factor in E-Scooter Conspicuity and Safety DOI

M Mabry,

Curtis M. Craig,

Peter Easterlund

et al.

Human Factors The Journal of the Human Factors and Ergonomics Society, Journal Year: 2025, Volume and Issue: unknown

Published: May 22, 2025

Background E-scooter injuries have risen in recent years and riders report a relatively high prevalence of accidents. Collisions with motor vehicles pose risk to e-scooter users. move fast relative runners but lack movement limbs that present aspects biological motion drivers, which may diminish conspicuity. Method Two experiments measured participants’ detection point light representations beneath masking visual noise. Study 1 presented runner, rider, rectangular object. 2 modified the stimuli remove sway added alternative presentations, one moving lights consistent other same reverse, inconsistent motion. Results found main effect figure type, runner resulting superior detection, recognition, response time compared performed better than perception performance for including reverse e-scooter. Conclusion Findings suggest reduced produced by users slightly worsens slows their road indicates an advantage human body configurations. Any inclusion apparent improve especially near ground, should be Application Visual display alterations (e.g., lighting) introduce mimics movements or is potentially confer over patterns.

Language: Английский

Citations

0
Advances in Vehicle Safety and Crash Avoidance Technologies DOI Creative Commons
Chuan Xu, Chuanyun Fu, Xinguo Jiang

et al.

Applied Sciences, Journal Year: 2025, Volume and Issue: 15(11), P. 5955 - 5955

Published: May 26, 2025

Per the World Health Organization, traffic accidents cause significant financial losses and fatalities annually [...]

Language: Английский

Citations

0
Understanding factors influencing e-scooterist crash risk: A naturalistic study of rental e-scooters in an urban area DOI Creative Commons

Rahul Rajendra Pai,

Marco Dozza

Accident Analysis & Prevention, Journal Year: 2024, Volume and Issue: 209, P. 107839 - 107839

Published: Nov. 12, 2024

In recent years, micromobility has seen unprecedented growth, especially with the introduction of dockless e-scooters. However, rapid emergence e-scooters led to an increase in crashes, resulting injuries and fatalities, highlighting need for in-depth analysis understand underlying mechanisms. While helpful quantifying problem, traditional crash database cannot fully explain causation mechanisms, e.g., human adaptation failures leading safety-critical events. Naturalistic data have proven extremely valuable understanding why crashes happen, but most studies addressed cars trucks. This study is first systematically analyze factors contributing near-crashes involving rental urban environment, utilizing naturalistic data. The collected dataset included 6868 trips, covering 9930 km over 709 h 4694 unique participants. We identified 61 events, including 19 42 near-crashes, subsequently labeled variables associated each event according codebook using video Our odds ratio that rider experience behavior (e.g., phone usage, single-handed riding, pack riding) significantly risk. Given accessibility individuals regardless their experience, our findings emphasize training addition education. Influenced by bicycles, riders may anticipate a similar self-stabilizing mechanism found which compromises balance, poses heightened risk, underscoring crucial role balance safe e-scooter operation. Furthermore, purpose (leisure or commute) directness (point-to-point detour) trip were also as influencing suggesting user intent plays Interestingly, underscores importance adapting near-crash definitions when working two-wheeled vehicles, those shared mobility system.

Language: Английский

Citations

1
Ready, set, scoot! Investigating implicit attitudes toward risky e-scooter riding situations: A go/no-go association task study DOI
Anat Meir, Yisrael Parmet, Avinoam Borowsky

et al.

Safety Science, Journal Year: 2024, Volume and Issue: 182, P. 106712 - 106712

Published: Nov. 18, 2024

Language: Английский

Citations

0
Geofencing to prevent collisions in drivers’ interactions with emergency vehicles DOI Creative Commons
Kajsa Weibull, Tereza Kunclová, Björn Lidestam

et al.

Transportation Research Interdisciplinary Perspectives, Journal Year: 2024, Volume and Issue: 28, P. 101297 - 101297

Published: Nov. 1, 2024

Language: Английский

Citations

0
Domestic Use of E-Cargo Bikes and Other E-Micromobility: Protocol for a Multi-Centre, Mixed Methods Study DOI Open Access
Ian Philips, Labib Azzouz, Alice de Séjournet

et al.

International Journal of Environmental Research and Public Health, Journal Year: 2024, Volume and Issue: 21(12), P. 1690 - 1690

Published: Dec. 19, 2024

Physical inactivity is a leading risk factor for non-communicable diseases. Climate change now regarded as the biggest threat to global public health. Electric micromobility (e-micromobility, including e-bikes, e-cargo bikes, and e-scooters) has potential simultaneously increase people’s overall physical activity while decreasing greenhouse gas emissions where it substitutes motorised transport. The ELEVATE study aims understand impacts of e-micromobility, identifying people, places, circumstances they will be most beneficial in terms improving health also reducing mobility-related energy demand carbon emissions. A complex mixed methods design collected detailed quantitative qualitative data from multiple UK cities. First, nationally representative (n = 2000), city-wide 400 each three cities; total 1200), targeted area surveys 996) on travel behaviour, levels activity, vehicle ownership, use, well attitudes towards e-micromobility. Then, provide insights an understudied type 49 households were recruited take part bike one-month trials. Self-reported participants validated with objective data-using such GPS trackers smartwatches’ recordings routes activities. CO2 e-micromobility use calculated. Participant interviews provided information preferences, expectations, experiences, barriers, enablers

Language: Английский

Citations

0