This paper assesses the potential of ride-sharing for reducing traffic in a city -based on mobility data extracted from 3G Call Description Records (CDRs), for the cities of Madrid and Barcelona (BCN), and from OSNs, such as Twitter and Foursquare (FSQ), collected for the cities of New York (NY) and Los Angeles (LA). First, we analyze these data sets to understand mobility patterns, home and work locations, and social ties between users. Then, we develop an efficient algorithm for matching users with similar mobility patterns, considering a range of constraints, including social distance. The solution provides an upper bound to the potential decrease in the number of cars in a city that can be achieved by ridesharing. Our results indicate that this decrease can be as high as 31%, when users are willing to ride with friends of friends. the bootstrapping problem (the more users a ride-sharing service has, the more the ride-sharing opportunities). However, even if the above problems were completely solved, the opportunities for ride-sharing would still depend on the underlying human mobility patterns and the layout of a city, which ultimately determine the route overlap.
Mobile and online social data is used in REF to assess the potential of ride-sharing for reducing traffic in the cities of Barcelona and Madrid.
Assessing the potential of ride-sharing using mobile and social data: a tale of four cities
Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing
Computer Science Physics