The amount of GPS data that can be collected is increasing tremendously, thanks to the increased popularity of Global Position System (GPS) devices (e.g., smartphones). This article aims to develop novel methods of converting crowd-sourced GPS traces into road topology maps. 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 use of estimation theory for map inference. In particular, our approach addresses the unique challenges of vehicular GPS data. This data is plentiful but suffers from noise in location and variable coverage of regions. This makes it difficult to differentiate between noise and sparsely covered regions when increasing coverage and reducing noise. Due to the asynchronous, variable sampling rate, and often under-sampled nature of the data, 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.
We evaluated our method for map inference by comparing to Open Street Map maps as ground truth. We use the F-Measure, Precision, and Recall metrics to evaluate our method on Tsinghua University’s Beijing Taxi Dataset and Shanghai Jiao Tong University’s SUVnet Dataset. On these datasets, we obtained a mean<?brk?> F-Measure, Precision, and Recall of 0.7212, 0.9165, and 0.6021, respectively, outperforming a well-known method based on Kernel Density Estimation in terms of these evaluation metrics.
An Investigation of Parallel Road Map Inference from Big GPS Traces Data
