Mining bus stops from raw GPS data of bus trajectories

Published in "Communication Systems and Networks"
Nandani Garg , Gitakrishnan Ramadurai , Sayan Ranu

An efficient public bus transit service is critical to achieving sustainable urban transport. To design an efficient bus transit system, a fundamental requirement is the complete list of all bus stops in the city. Often the bus stops are owned and maintained by a different government entity from that which plans and operates the buses. As a result, bus stop information is incomplete, erroneous, and outdated leading to sub-optimal planning and operations and consequent reduction in the transit agency’s ridership. In this work, we propose an algorithm to mine bus stops automatically from bus GPS trajectories. The proposed technique is powered by a novel combination of feature mining with classification algorithms to predict bus stops in a city. Our technique negates the need for manual field visits to annotate bus stops and saves time and cost for transit agencies. We perform extensive empirical analysis on real datasets from Chennai, India and firmly establish the reliability of our technique by achieving an accuracy of almost 99%.