Trajectory Estimation From Sparse Cellular Network Data Based on the Historical Vehicular Data

By coupling data-mining techniques with historical cellular and vehicular data, it is possible to find a certain spatiotemporal logic in the observed data. The primary motivation for combining multiple data sources derives from the fact that origin-destination matrices, extracted from cellular data sets, represent only the route's start and end-point without the information about the complete trajectory. This paper proposes a method to estimate sparse cellular data trajectories by reconstructing possible paths from the historical vehicular data. The possible routes are generated by solving the shortest-path problems for given origin-destination pair. A spatiotemporal similarity value is computed to evaluate the paths relative to the ground-truth origin-destination pair. In the end, the path with the highest similarity value is selected. Three criteria for the computation of the spatiotemporal similarity are used: length-based, modified length-based and time-based. The results show the application of proposed methods on the collected historical data, with average percentage similarity ranging from 39.85% to 56.11% (depending on the transport mode and criteria used) with modified-length criteria producing the best results - in some cases producing a trajectory with 98% similarity to the actual cellular trajectory.