Sarne Postorino 2018a - Revision history

Scipediacontent at 08:08, 6 March 2021

2021-03-06T08:08:51Z

Scipediacontent: Scipediacontent moved page Draft Content 943022259 to Sarne Postorino 2018a

2021-02-08T08:39:11Z

Scipediacontent moved page Draft Content 943022259 to Sarne Postorino 2018a

Scipediacontent: Created page with " == Abstract == main question in urban environments is the continuous growth of private mobility with its negative effects such as traffic congestion and pollution. To mitig..."

2021-02-08T08:39:07Z

Created page with " == Abstract == main question in urban environments is the continuous growth of private mobility with its negative effects such as traffic congestion and pollution. To mitig..."

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== Abstract ==

main question in urban environments is the continuous growth of private mobility with its negative effects such as traffic congestion and pollution. To mitigate them, it is important to promote different forms of mobility among the citizens. Car-sharing systems give users the same flexibility and comfort of private cars but at smaller costs. For this reason, car-sharing has continuously increased its market share although rather slowly. To boost such growth, car-sharing systems needs to increase vehicle fleet, improve company profits and, at the same time, make it more affordable for consumers. In this paper the promotion of car-sharing by reputation is proposed. Neural networks have been used to identify drivers’ habits in using car-sharing vehicles. To verify the effectiveness of the proposed approach, some experiments based on real and simulated data were carried out with promising results.

== Original document ==

The different versions of the original document can be found in:

* [http://hdl.handle.net/11585/700653 http://hdl.handle.net/11585/700653]

* [http://link.springer.com/content/pdf/10.1007/978-3-319-95095-2_20 http://link.springer.com/content/pdf/10.1007/978-3-319-95095-2_20],
: [http://dx.doi.org/10.1007/978-3-319-95095-2_20 http://dx.doi.org/10.1007/978-3-319-95095-2_20] under the license http://www.springer.com/tdm

* [https://link.springer.com/chapter/10.1007%2F978-3-319-95095-2_20 https://link.springer.com/chapter/10.1007%2F978-3-319-95095-2_20],
: [https://academic.microsoft.com/#/detail/2885377396 https://academic.microsoft.com/#/detail/2885377396]

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 == Abstract ==
- main question in urban environments is the continuous growth of private mobility with its negative effects such as traffic congestion and pollution. To mitigate them, it is important to promote different forms of mobility among the citizens. Car-sharing systems give users the same flexibility and comfort of private cars but at smaller costs. For this reason, car-sharing has continuously increased its market share although rather slowly. To boost such growth, car-sharing systems needs to increase vehicle fleet, improve company profits and, at the same time, make it more affordable for consumers. In this paper the promotion of car-sharing by reputation is proposed. Neural networks have been used to identify drivers’ habits in using car-sharing vehicles. To verify the effectiveness of the proposed approach, some experiments based on real and simulated data were carried out with promising results.
+Main question in urban environments is the continuous growth of private mobility with its negative effects such as traffic congestion and pollution. To mitigate them, it is important to promote different forms of mobility among the citizens. Car-sharing systems give users the same flexibility and comfort of private cars but at smaller costs. For this reason, car-sharing has continuously increased its market share although rather slowly. To boost such growth, car-sharing systems needs to increase vehicle fleet, improve company profits and, at the same time, make it more affordable for consumers. In this paper the promotion of car-sharing by reputation is proposed. Neural networks have been used to identify drivers’ habits in using car-sharing vehicles. To verify the effectiveness of the proposed approach, some experiments based on real and simulated data were carried out with promising results.
-−
 == Original document ==

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