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A Human-Centered Behavioral Informatics Read chapter Read first chapter

2019 | OriginalPaper | Chapter

20. A Neural Network to Identify Driving Habits and Compute Car-Sharing Users’ Reputation

Authors : Maria Nadia Postorino, Giuseppe M. L. Sarnè

Published in: Quantifying and Processing Biomedical and Behavioral Signals

Publisher: Springer International Publishing

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Abstract

A 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.

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Footnotes
1
Note that some events, e.g. car body damages or interior cleaning, cannot be evaluated automatically and require a human intervention to be taken into account in the global reputation score (see Sect. 20.3).
 
2
To preserve the user’s privacy, the RS associates each user with a unique ID to which each his/her data is referred. Driver and car-owner identities are mutually disclosed only when the CS service is agreed.
 
3
Note that the OpenXC repository separates data specifically coming from “aggressive” driving sessions.
 
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Metadata
Title
A Neural Network to Identify Driving Habits and Compute Car-Sharing Users’ Reputation
Authors
Maria Nadia Postorino
Giuseppe M. L. Sarnè
Copyright Year
2019
Book
Quantifying and Processing Biomedical and Behavioral Signals

Print ISBN: 978-3-319-95094-5

Electronic ISBN: 978-3-319-95095-2

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-319-95095-2_20

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