Efficient mobility is a key aspect for the future smart cities. The real-time optimization of vehicular and public transportation flows to reduce traffic congestions, costs and emissions is the real added value for smart cities. In this paper, we describe a novel use of big data coming from the cellular network of the Vodafone Italy Telco operator to compute mobility patterns for smart cities. These mobility patterns are able to describe different mobility scenarios of the city, starting from how people move around Point Of Interests of the city in real-time. These mobility patterns can be exploited by Policy makers to improve the mobility in a city or by Navigation Systems and Journey Planners to provide final users with accurate travel plans. The paper discusses five main new mobility patterns and their experimental validation in a real industrial setting and for the Milan metropolitan city.

Big Data from Cellular Networks: Real Mobility Scenarios for Future Smart Cities

TOSI, DAVIDE;
2016

Abstract

Efficient mobility is a key aspect for the future smart cities. The real-time optimization of vehicular and public transportation flows to reduce traffic congestions, costs and emissions is the real added value for smart cities. In this paper, we describe a novel use of big data coming from the cellular network of the Vodafone Italy Telco operator to compute mobility patterns for smart cities. These mobility patterns are able to describe different mobility scenarios of the city, starting from how people move around Point Of Interests of the city in real-time. These mobility patterns can be exploited by Policy makers to improve the mobility in a city or by Navigation Systems and Journey Planners to provide final users with accurate travel plans. The paper discusses five main new mobility patterns and their experimental validation in a real industrial setting and for the Milan metropolitan city.
9781509022519
9781509022519
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11383/2049194
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