Mobile crowd sensing for smart urban mobility

Abstract

In this paper, we present the application of the mobile crowd-sensing paradigm in supporting efficient, safe and green mobility in urban environments. We have developed the CitySensing framework demonstrating the viability of a common crowd-sourcing platform applied to various urban mobility domains. We argue that today’s mobile devices, with integrated or add-on sensors, can be efficiently used to crowd source diverse information in domains that are relevant to urban life and mobility (traffic, air quality and citizens’ everyday activities). This is illustrated by three distinct mobile applications, developed on top of the CitySensing framework, that contribute to a common goal of smarter urban mobility. Commonly integrated accelerometer and GPS are used to infer traffic events and conditions. Externally attached or integrated air quality sensors enable suggestions for city areas adequate for outdoor activities on a specific day of the week or hour of the day. Mobile phone usage statistics and analysis can present valuable information to urban planning services to better adapt to citizens’ habits and mobility. The analysis of this massive amount of crowd sensed data (so-called Big Data) within the cluster/cloud infrastructure enables detection of situations and events that influence human mobility, and dissemination of notifications and recommended actions.