MARIO: A spatio-temporal data mining framework on Google Cloud to explore mobility dynamics from taxi trajectories

Abstract

With the major advances in location acquisition techniques, deployment of GPS enabled devices and increasing number of mobile users, substantial amount of location traces are generated from different geographical regions. It provides unprecedented opportunities to analyze and derive valuable insights of urban dynamics, specifically, time-dependent mobility patterns and region-specific travel demands. This work proposes an end-to-end mobility association rule mining framework called MARIO, conducive to extract urban mobility dynamics through analysing large taxi trip traces of a city. The MARIO framework consists of (i) generating mobility-dynamics network by spatio-temporal analysis of taxi-trips, (ii) finding travel demand variations in different functional regions of the urban area, (iii) extracting mobility association rules and (iv) predicting travel demands and traffic dynamics using extracted associative rules. The proposed MARIO framework is implemented in Google Cloud Platform and an extensive set of experiments using real GPS trace dataset of NYC Green and Yellow Taxi trace, Roma Taxi Dataset and San Francisco Taxi Dataset have been carried out to demonstrate the effectiveness of the framework. The performance of the proposed approach is significantly better than the baseline methods in predicting travel demands (with the reduction of average MAPE value and execution time by 50%).