Assessing air quality and physical risks to E-scooter riders in urban environments through artificial intelligence and a mixed methods approach
Notice bibliographique
Résumé
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. • Electric and clean energy transportation technologies are key in future NetZero cities. • This paper uses a mixed methods approach including Artificial Intelligence to address risks to e-scooter users. • Risk to e-scooter users include air pollution from fossil-fuel cars and physical risk. • The results indicate a better strategy is required to ensure the safety of e-scooter users. • The improved strategy could include training, dedicated spaces, improved visibility, and the use of helmets.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».