Assessing air quality and physical risks to E-scooter riders in urban environments through artificial intelligence and a mixed methods approach
Bibliographic record
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. • 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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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".