Topic Model-Based Road Network Inference from Massive Trajectories

Recent years witnessed popular use of various mobile devices, e.g., smart phones, vehicle networks and wearable watches. Such mobile devices generate massive trajectory data, and literature have proposed various algorithms to leverage the trajectory data for map inference. Unfortunately, such algorithms are hard to achieve both high map quality and computation efficiency. In this paper, we propose a solution framework to infer road network maps with high quality and efficiency. The key of our map inference is to divide map extent into smaller cells and maintain a binary cell-trajectory matrix. The binary matrix determines whether or not a trajectory passes a cell. We infer the importance of each cell from the matrix using a popular topic model (e.g., LDA [13] and pLSA [8]). Based on such computed importance, we next infer representative points and road segments to derive a road network map. Our extensive experiments on real data sets verify that the proposed inference algorithm can achieve higher map quality and meanwhile 1.5 ×, 6.8 × and 280 × shorter running time, when compared with three state of the arts including three representative work [4], [7], [14].