Abstract

Human mobility in a city represents a fascinating complex system that combines social interactions, daily constraints and random explorations. New collections of data that capture human mobility not only help us to understand their underlying patterns but also to design intelligent systems. Bringing us the opportunity to reduce traffic and to develop other applications that make cities more adaptable to human needs. In this paper, we propose an adaptive routing strategy which accounts for individual constraints to recommend personalized routes and, at the same time, for constraints imposed by the collectivity as a whole. Using big data sets recently released during the Telecom Italia Big Data Challenge, we show that our algorithm allows us to reduce the overall traffic in a smart city thanks to synergetic effects, with the participation of individuals in the system, playing a crucial role.

Highlights

  • Rapid development of wireless communication and mobile computing technologies call new research that explores the responses of urban systems to the flow of instant information

  • The presence of new information and communication technologies (ICT) provide big data sources that are allowing novel research and applications related to human mobility

  • Recent studies have advanced the knowledge on trip generation by studying the number of different locations visited by individuals through mobile phones and quantifying their frequent return to previously visited locations

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Summary

Introduction

Rapid development of wireless communication and mobile computing technologies call new research that explores the responses of urban systems to the flow of instant information. 2 Data-driven routing of human mobility We consider a geographic area of interest (e.g., a city, a district, etc.) and we discretize it into a grid G with size L × L .

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Conclusion

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