Abstract
Since information and communication technologies have become an integral part of our everyday lives, it only seems logical that the smart city concept should attempt to explore the role of an integrated information and communication approach to city asset management and raising the quality of life of its citizens. Raising the quality of life relies not only on improving the management of a city’s systems (e.g. transportation system) but also on the provision of timely and relevant information to its citizens to allow them to make better informed decisions. This requires the use of forecasting models. In this paper, a support vector machine-based model is developed to predict future mobility behavior from crowdsourced data. Crowdsourced data are collected through a dedicated smartphone app tracking mobility behavior. The use of a forecasting model of this type can facilitate the management of a smart city’s mobility system while simultaneously ensuring the timely provision of relevant pretravel information to its citizens.
Highlights
The influence of information and communication technologies (ICT) on transport planning is nothing new
Transport planning involves multiple complex models attempting to generalize the dynamic features of human and cargo movement, the need for mobility and predict the future state of the system
When it comes to the transportation aspect of smart cities, location information acquisition is often supported by Global Navigation Satellite System (GNSS) data
Summary
The influence of information and communication technologies (ICT) on transport planning is nothing new. Much has been done in this field over the last decades and in recent years, when the integration of the role of ICT at a higher level enabled the development of smart cities (Tranos and Gertner, 2012; Neirotti et al, 2014; MarsaMaestre et al, 2008; Beswick, 2014; Alonso and Rossi, 2011; Atzori et al, 2010; Nam and Pardo, 2011; The Climate Group, 2011) In this context, location acquisition technologies are an important basis for smart city applications (Lazaroiu and Roscia, 2012; Lu and Liu, 2012). The purpose of building a smart city is to raise the quality of life of its inhabitants by using technology to improve the efficiency of services and enable both policy makers and citizens to make better informed decisions When it comes to the transportation aspect of smart cities, location information acquisition is often supported by Global Navigation Satellite System (GNSS) data.
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