AllAboard: A System for Exploring Urban Mobility and Optimizing Public Transport Using Cellphone Data

This paper describes a system to leverage data on cell phones to understand mobility patterns and presents a large-scale network design model for public transit while considering existing service offerings. The work is motivated by the rapid urbanization in growth market cities across the world, where adequate resources to develop detailed travel demand models may be absent. Urban growth is coupled with high cell phone penetration which transit operators can leverage to better match observed demand for travel with service offerings. Based on call detail records from a telecommunications operator in Abidjan, Cote d’Ivoire, this paper describes analytics and optimization techniques that result in system-wide journey time improvements of 10%. The main contribution of this paper is to demonstrate how big data analytics and optimization tools can transform data from opportunistic sensing in the real-world to improve urban mobility outcomes.

[1]  Alex Pentland,et al.  Reality mining: sensing complex social systems , 2006, Personal and Ubiquitous Computing.

[2]  Yu Zheng,et al.  Computing with Spatial Trajectories , 2011, Computing with Spatial Trajectories.

[3]  Carlo Ratti,et al.  Real time Rome , 2006 .

[4]  PentlandAlex,et al.  Reality mining: sensing complex social systems , 2006 .

[5]  Dino Pedreschi,et al.  Unveiling the complexity of human mobility by querying and mining massive trajectory data , 2011, The VLDB Journal.