In the future, mobility will increasingly be a service with the individual at the center, based on multimodal, clean and safe solutions. In this paper, with reference to Milan commuters, a solution is proposed that interconnects private cars with dedicated e-bikes placed at the most important urban and suburban transport hubs. This is suggested as a practical and sustainable alternative to public transportation which, in particular in the post-covid-19 era, is collecting a lot of distrust. The design and sizing of the e-bikes service relies on a real, space-time telematics dataset of numerous vehicles registered in Milan and its neighboring provinces. One year true driving patterns are mined from data and each regular city commuter is associated, through a clustering analysis, to one of the commuting junctions with the public transport network. By appropriately selecting these exchange parkings as those at a distance from the center compatible with an e-bike, a statistical and data analytics methodology, on the basis of adequate hypotheses on contemporaneity and minimum use time of the e-bikes to be compatible with a work activity, allows to size, for each parking, the number of dedicated shared e-bikes to be placed on site in order to meet all the demand.
Concept and sizing of an e-bike sharing service for commuters to a major metropolitan area
D. Penati;S. Strada;S. Savaresi
2021-01-01
Abstract
In the future, mobility will increasingly be a service with the individual at the center, based on multimodal, clean and safe solutions. In this paper, with reference to Milan commuters, a solution is proposed that interconnects private cars with dedicated e-bikes placed at the most important urban and suburban transport hubs. This is suggested as a practical and sustainable alternative to public transportation which, in particular in the post-covid-19 era, is collecting a lot of distrust. The design and sizing of the e-bikes service relies on a real, space-time telematics dataset of numerous vehicles registered in Milan and its neighboring provinces. One year true driving patterns are mined from data and each regular city commuter is associated, through a clustering analysis, to one of the commuting junctions with the public transport network. By appropriately selecting these exchange parkings as those at a distance from the center compatible with an e-bike, a statistical and data analytics methodology, on the basis of adequate hypotheses on contemporaneity and minimum use time of the e-bikes to be compatible with a work activity, allows to size, for each parking, the number of dedicated shared e-bikes to be placed on site in order to meet all the demand.File | Dimensione | Formato | |
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