Given the changing dynamics of mobility patterns and rapid growth of cities, transport agencies seek to respond more rapidly to needs of the public with the goal of offering an effective and competitive public transport system. A more data-centric approach for transport planning is part of the evolution of this process. In particular, the vast penetration of mobile phones provides an opportunity to monitor and derive insights on transport usage. Real time and historical analyses of such data can give a detailed understanding of mobility patterns of people and also suggest improvements to current transit systems. On its own, however, mobile geolocation data has a number of limitations. We thus propose a joint telco-and-farecard-based learning approach to understanding urban mobility. The approach enhances telecommunications data by leveraging it jointly with other sources of real-time data. The approach is illustrated on the first- and last-mile problem as well as route choice estimation within a densely-connected train network.

Filed under: Big Data