People Flow Reconstruction in Cities

Abstract

People flows are of primary importance in a city environment, making up for an essential component of interest in every city. Yet, study of people flows has to face severe problems, mainly due to the high cost/benefit ratio of trying to get flow information. People flows tend to be seen as secondary with respect to traffic in most parts of the cities. The result of this policy is that the detection of their actual status, and corresponding maintenance, is often far from optimal. In this study we tackle the problem of extracting people flow information, and also show a concrete example of usage of the data, that allows to monitor the pedestrian infrastructure of a city. Following the Smart Cheap City (SCC) approach, we design and implement a system of sensors that allows to gather people flow data by staying within a very limited budget. We then show how this raw data can actually be used to reconstruct people flows, and then investigate the relationship between this flow information and the problem of infrastructure monitoring. We experiment with the system in a major experiment involving five cities, using various configurations, and show the effectiveness of the method when used on the field. The overall lesson is that the problem of reconstructing people flows within cities can be faced even when employing very limited resources, also allowing for a better handling of the related transportation infrastructures.