首页|Researchers from University of Birmingham Report Findings in Machine Learning (E xploring the Influence of Socio-economic Aspects On the Use of Electric Scooters Using Machine Learning Applications: a Case Study In the City of Palermo)
Researchers from University of Birmingham Report Findings in Machine Learning (E xploring the Influence of Socio-economic Aspects On the Use of Electric Scooters Using Machine Learning Applications: a Case Study In the City of Palermo)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Researchers detail new data in Machine Learning. According to news reporting originating in Birmingham, United Kingdom, by NewsRx journalists, research stated, "Most European countries have been committed to r educing their carbon footprint, combating climate change, and reducing the air p ollution typical in large cities over the past decade. Among current solutions t hat can be adopted are the replacement of fuel-powered means of transport with e lectric ones, as well as the introduction of car sharing, bike sharing and elect ric scooters." The news reporters obtained a quote from the research from the University of Bir mingham, "The postpandemic phase was characterized by a greater propensity to u se these means of transport as they were perceived as a healthier choice (for a greater possibility of implementing social distancing) and cheaper (for the diff usion of shared services). The study of modal choice depends on socio-economic s tructures. The present work analyses data related to socio-economic factors (wor k, income and other) to examine the tendency to use electric scooters in the met ropolis of Palermo, Sicily, through machine learning algorithms. The comparison of different algorithms allowed us to underline how the multilayer perceptron al gorithm obtained the best classification among the minimal sequential optimizati on algorithms. The findings also highlight middle-income and freelancer people a s being more likely to use micro-mobility than others. Contrary to what was thou ght, these findings revealed that micro-mobility is not just a preferred mode of transport for low-income people or students."
BirminghamUnited KingdomEuropeAlgo rithmsCyborgsEmerging TechnologiesMachine LearningUniversity of Birmingh am
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