Assessing air quality and physical risks to E-scooter riders in urban environments through artificial intelligence and a mixed methods approach

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Al-Habaibeh, A. ORCID: 0000-0002-9867-6011, Watkins, M., Shakmak, B. ORCID: 0000-0003-4534-9196, Javareshk, M.B. and Allison, S. ORCID: 0000-0002-7509-9979, 2024. Assessing air quality and physical risks to E-scooter riders in urban environments through artificial intelligence and a mixed methods approach. Applied Energy, 376 (Part B): 124282. ISSN 0306-2619

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Official URL: https://doi.org/10.1016/j.apenergy.2024.124282

Abstract

The need to develop green and smart transport solutions for NetZero cities to reduce carbon emissions through the use of clean energy is driving innovation in cities around the world. A result of this trend is a rise in micro-mobility solutions such as e-scooters in cities around the globe. Nottingham (UK) is one of the cities that conducted an e-scooter pilot scheme permitting the rental of e-scooters to travel around the city in a bid to encourage more sustainable personal transport use. However, to ensure pedestrian safety, e-scooters are required to be ridden on the road network among cars. Hence, giving rise to two potential risks for e-scooter users: the air quality that they breathe and the physical risk of being near cars, whose drivers may be unfamiliar with seeing e-scooters on the road.

This study seeks to explore this interaction using a mixed methods approach to explore the experiences of e-scooter riders in respect to their physical safety and exposure to air pollution. The research makes use of two quantitative surveys an international e-scooter user survey n = 801 and a survey of UK car drivers n = 92, focussed qualitative e-scooter rider interviews and quantitative in-depth road data collection trials comprising of air quality particulate sensing, video capturing around the rider and GPS tracking. The in-depth road data was analysed using an AI approach utilising the ASPS approach, the automated sensor and signal processing approach, implemented for image and signal processing to detect the existence of cars alongside the pollution readings.

The findings show that e-scooter riders are typically aware of physical dangers to their safety from other road users, as well as how their presence among pedestrians can impact on more vulnerable users; however, they were unaware of the prevalence and effects of air pollution on them whilst riding. The study highlights the need for a multifaceted approach to improvements in safety for micro-mobility users, predominately considering suitable infrastructure to sperate them from motor vehicles and pedestrians but also the need to consider the proximity to emission emitting vehicles, developing infrastructure in green spaces to address these air pollution levels.

Item Type:

Journal article

Publication Title:

Applied Energy

Creators:

Al-Habaibeh, A., Watkins, M., Shakmak, B., Javareshk, M.B. and Allison, S.

Publisher:

Elsevier

Date:

15 December 2024

Volume:

376