4.6 Article

Classification of users' transportation modalities from mobiles in real operating conditions

期刊

MULTIMEDIA TOOLS AND APPLICATIONS
卷 81, 期 1, 页码 115-140

出版社

SPRINGER
DOI: 10.1007/s11042-021-10993-y

关键词

User behaviour analysis; Smart city; Mobile phones; Transportation modes; Classification model; Machine learning

资金

  1. Universita degli Studi di Firenze within the CRUI-CARE Agreement

向作者/读者索取更多资源

The advancement of modern mobile phones and digital transport networks has facilitated access to useful information about user's mean of transportation, leading to the development of innovative applications in sustainable mobility, smart transportation, and e-health. A new approach has been proposed to collect real-time data from mobile phones for personalized assistance messages for city users, contributing to a better understanding of travel behavior and enhancing user experience in urban environments.
The modern mobile phones and the complete digitalization of the public and private transport networks have allowed to access useful information to understand the user's mean of transportation. This enables a plethora of old and new applications in the fields of sustainable mobility, smart transportation, assistance, and e-health. The precise understanding of the travel means is at the basis of the development of a large range of applications. In this paper, a number of metrics has been identified to understand whether an individual on the move is stationary, walking, on a motorized private or public transport, with the aim of delivering to city users personalized assistance messages for: sustainable mobility, health, and/or for a better and enjoyable life, etc. Differently from the state-of-the-art solutions, the proposed approach has been designed to provide results, and thus collect metrics, in real operating conditions (imposed on the mobile phones as: a range of different mobile phone kinds, operating system constraints managing Applications, active battery consumption manager, etc.). The paper reports the whole experimentations and results. The solution has been developed in the context of Sii-Mobility Km4City Research Project infrastructure and tools, performed with the collaboration of public transport operators, and GDPR compliant. The same solution has been used in Snap4City mobile Apps with experiments performed in Antwerp and Helsinki.

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