Signal reconstruction approach for map inference from crowd-sourced GPS traces

Thanks to the increased popularity of Global Position System (GPS) devices (such as smartphones and GPS navigators), the amount of GPS data that can be collected is increasing tremendously. This paper aims to develop novel methods of inferring and updating road topology maps from a large amount of crowd-sourced GPS data. We explore map inference using a three-stage approach, which incorporates a novel Multi-Source Variable Rate (MSVR) signal reconstruction mechanism. Unlike conventional map inference methods based on map graph theory, our approach, to the best of our knowledge, is the first estimation theory method used for map inference. In particular, our approach explicitly leverages the nature of GPS error models, and addresses the unique challenges of vehicular GPS data (asynchronous, varying sampling rate, and under-sampled); as a result, our MSVR approach can better handle inherent GPS errors, reconstruct road shapes more accurately, and better deal with variable GPS data density in empirical environments. The maps inferred from this data are compared to Open Street Map (OSM) maps as ground truth. We evaluate our method using the Tsinghua University's Beijing Taxi Dataset and Shanghai Jiao Tong University's SUVnet Dataset.