Big data analytics for smart mobility: a case study

1. APPLICATION SCENARIO This paper presents a real case study were several mobility data sources are collected in a urban context, integrated and analyzed in order to answer a set of key questions about mobility. The study of the human mobility is a very sensitive topic for both public transport (PT) companies and local administrations. This work is a contribution in the understanding of some aspects of the mobility in Cosenza, a town in the South of Italy, and the realization of corresponding services in order to aswer to the following questions identified in collaboration with the PT experts. Question 1: How is PT able to substitute private mobility? The objective is to compare public and private mobility to verify the capability of PT to satisfy the user mobility needs. Question 2: How di erent zones of the city are reachable using PT? This question focuses on understanding how much di erent zones of the city are served by PT considering di erent times of the day. Question 3: Are there usual time deviations between real travel times and o cial time tables? We want to verify if usual time deviations between real travel times and o cial time tables exist highlighting chronic delays in the service. Question 4: Can we spot visitors and commuters by their behavior? We aim at identifying important categories of people estimating their segmentation in order to evaluate the corresponding demand of services. For this case study we use data from Cosenza area: a GSM dataset 1, a GPS dataset 2, and data from the PT system 3. GSM data contains 25 mln of phone calls made by about 350K distinct users from 15 October to 9 November 2012. GPS dataset contains about 1.5 mln of private vehicle tracks gathered in February-March and July-August 2012, while PT data consist of a set of GPS logs obtained by the on-