Health impacts of electric micromobility transitions in Barcelona: A scenario analysis

López-Dóriga, Inés; Vich, Guillem; Koch, Sarah; Khomenko, Sasha; Marquet, Oriol; Roig-Costa, Oriol; Daher, Carolyn; Rasella, Davide; Nieuwenhuijsen, Mark; Mueller, Natalie

doi:10.1016/j.eiar.2022.106836

Cited by 8 publications

(2 citation statements)

References 48 publications

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“…The micro-mobilities are also a good fit for modern and future society because of their health benefits. The transition from car to electric micro-mobility has been found to increase physical activity and prevent fatal accidents, which outweigh the air pollution exposure [22]. With these advantages, social acceptance of mixed types of mobilities, as in shared mobilities or an integration of public transportation has emerged as an alternative for private cars and public transportation [28].…”

Section: Introductionmentioning

confidence: 99%

DualTake: Predicting Takeovers across Mobilities for Future Personalized Mobility Services

Zheng,

Akash,

Misu

2024

Companion of the 2024 ACM/IEEE International Conference on Human-Robot Interaction

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A hybrid society is expected to emerge in the near future, with different mobilities interacting together, including cars, micro-mobilities, pedestrians, and robots. People may utilize multiple types of mobilities in their daily lives. As vehicle automation advances, driver modeling flourishes to provide personalized intelligent services. Thus, modeling drivers across mobilities would pave the road for future society mobility-as-a-service, and it is particularly interesting to predict driver behaviors in newer mobilities with traditional mobility data. In this work, we present takeover prediction on a micro-mobility, with car simulation data.The promising model performance demonstrates the feasibility of driver modeling across mobilities, as the first in the field. CCS CONCEPTS• Human-centered computing → User models; • Computing methodologies → Neural networks.

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Section: Introductionmentioning

confidence: 99%

DualTake: Predicting Takeovers across Mobilities for Future Personalized Mobility Services

Zheng,

Akash,

Misu

2024

Companion of the 2024 ACM/IEEE International Conference on Human-Robot Interaction

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“…One study used probabilistic behavioural models developed within random utility theory, examining factors such as drivers' demographics, drivers' households, type of use, and road conditions to analyse the effects that led to the use of e-micro mobility for commuting [35]. Another study examined changes in physical activity, personal air pollution exposure, and risk of fatal traffic accidents to measure the health effects of using electric micro-mobility vehicles for adults residing in Barcelona [36]. Nigro et al analysed that using e-scooters or e-bikes up to 3 km on weekdays and weekends for out-of-home trips in the city of Rome was associated with the distance factor [37].…”

Section: Introductionmentioning

confidence: 99%

Estimation of Postal Service Delivery Time and Energy Cost with E-Scooter by Machine Learning Algorithms

İnaç¹,

Ayözen²,

Atalan

et al. 2022

Applied Sciences

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This research aims to estimate the delivery time and energy cost of e-scooter vehicles for distributing mail or packages and to show the usage efficiency of e-scooter sharing services in postal service delivery in Turkey. The machine learning (ML) methods used to implement the prediction of delivery time and energy cost as output variables include random forest (RF), gradient boosting (GB), k-nearest neighbour (kNN), and neural network (NN) algorithms. Fifteen input variables under demographic, environmental, geographical, time, and meta-features are utilised in the ML algorithms. The correlation coefficient (R2) values of RF, GB, NN, and kNN algorithms were computed for delivery time as 0.816, 0.845, 0.821, and 0.786, respectively. The GB algorithm, which has a high R2 and the slightest margin of error, exhibited the best prediction performance for delivery time and energy cost. Regarding delivery time, the GB algorithm’s MSE, RMSE, and MAE values were calculated as 149.32, 12.22, and 6.08, respectively. The R2 values of RF, GB, NN, and kNN algorithms were computed for energy cost as 0.917, 0.953, 0.400, and 0.365, respectively. The MSE, RMSE, and MAE values of the GB algorithm were calculated as 0.001, 0.019, and 0.009, respectively. The average energy cost to complete a package or mail delivery process with e-scooter vehicles is calculated as 0.125 TL, and the required time is approximately computed as 11.21 min. The scientific innovation of the study shows that e-scooter delivery vehicles are better for the environment, cost, and energy than traditional delivery vehicles. At the same time, using e-scooters as the preferred way to deliver packages or mail has shown how well the delivery service works. Because of this, the results of this study will help in the development of ways to make the use of e-scooters in delivery service even more efficient.

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The health and economic impacts of emergency measures to combat heavy air pollution

Li,

Yao,

Zhang

et al. 2023

Journal of Cleaner Production

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Health impacts of electric micromobility transitions in Barcelona: A scenario analysis

Cited by 8 publications

References 48 publications

DualTake: Predicting Takeovers across Mobilities for Future Personalized Mobility Services

DualTake: Predicting Takeovers across Mobilities for Future Personalized Mobility Services

Estimation of Postal Service Delivery Time and Energy Cost with E-Scooter by Machine Learning Algorithms

The health and economic impacts of emergency measures to combat heavy air pollution

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