scholarly journals Smart parking for an ecological type of transport-electric scooter

2021 ◽  
Vol 1942 (1) ◽  
pp. 012096
Author(s):  
D I Nikolaev ◽  
N V Chubur ◽  
V V Krasnoschekov ◽  
M V Diuldin
Author(s):  
Mohammed Rezwanul Islam ◽  
Sami Azam ◽  
Bharanidharan Shanmugam ◽  
Asif Karim ◽  
Jamal El-Den ◽  
...  

FACE ◽  
2021 ◽  
pp. 273250162199244
Author(s):  
Elizabeth M. Boudiab ◽  
Thomas D. Zaikos ◽  
Christopher Issa ◽  
Kongkrit Chaiyasate ◽  
Stephen M. Lu

Electric scooters are an increasingly common and convenient mode of transportation worldwide and have effectively revolutionized the shared micromobility industry. As electric scooter sharing companies have increased in popularity there has been a concomitant increase in the frequency of all electric scooter-related injuries. The purpose of this study is to describe the most up-to-date trends in craniofacial fractures and lacerations related to electric scooter use among all age groups. We queried the National Electronic Injury Surveillance System (NEISS) for craniofacial fractures and lacerations related to e-scooters between 2010 and 2019. We then compared injury trends over time and between time periods before and after 2017 when electric scooter share apps revolutionized micromobility. We compared incidence of injury overall and by day of the week, patient demographics, and case severity based on clinical disposition. We identified an increase in the frequency of craniofacial lacerations and fractures in the 3 years following the introduction of electric scooter share services in 2017 (2017 and 2019), compared to the 3 years before this time (2014-2016). Young adults (18-39 years) were the age group with the greatest interval increase in craniofacial injuries. There was also an increase in number of craniofacial injuries occurring on Mondays and a decrease number occurring on Fridays in the later time period. Finally, patients who presented with electric scooter-related craniofacial injuries in this later time period showed a higher frequency of overnight observation and hospital admission for their injuries. The number of craniofacial injuries secondary to electric scooter use has increased dramatically since the introduction of share services. Craniofacial fractures and lacerations are a common reason for craniofacial or maxillofacial surgery consultation and understanding these patterns of injury will help prepare surgeons for patient care, preventative education, and public advocacy.


Author(s):  
Awad Alharbi ◽  
George Halikias ◽  
Mohammad Yamin ◽  
Adnan Ahmed Abi Sen

2021 ◽  
Vol 21 (3) ◽  
pp. 1-21
Author(s):  
Francesco Piccialli ◽  
Fabio Giampaolo ◽  
Edoardo Prezioso ◽  
Danilo Crisci ◽  
Salvatore Cuomo

Nowadays, a sustainable and smart city focuses on energy efficiency and the reduction of polluting emissions through smart mobility projects and initiatives to “sensitize” infrastructure. Smart parking is one of the building blocks of intelligent mobility, innovative mobility that aims to be flexible, integrated, and sustainable and consequently integrated into a Smart City. By using the Internet of Things (IoT) sensors located in the parking areas or the underground car parks in combination with a mobile application, which indicates to citizens the free places in the different areas of the city and guides them toward the chosen parking, it is possible to reduce air pollution and fluidifying noise traffic. In this article, we present and discuss an innovative Deep Learning-based ensemble technique in forecasting the parking space occupancy to reduce the search time for parking and to optimize the flow of cars in particularly congested areas, with an overall positive impact on traffic in urban centres. A genetic algorithm has also been used to optimize predictors parameters. The main goal is to design an intelligent IoT-based service that can predict, in the next few hours, the parking spaces occupancy of a street. The proposed approach has been assessed on a real IoT dataset composed by over than 15M of collected sensor records. Obtained results demonstrate that our method outperforms both single predictors and the widely used strategy of the mean providing inherently robust predictions.


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