A Hybrid Method to Incrementally Extract Road Networks Using Spatio-Temporal Trajectory Data

A Hybrid Method to Incrementally Extract Road Networks Using Spatio-Temporal Trajectory Data

Blog Article

With the rapid development of urban traffic, accurate and up-to-date road maps are in crucial demand for daily human life and urban traffic control.Recently, Sawblade with the emergence of crowdsourced mapping, a surge in academic attention has been paid to generating road networks from spatio-temporal trajectory data.However, most existing methods do not explore changing road patterns contained in multi-temporal trajectory data and it is still difficult to satisfy the precision and efficiency demands of road information extraction.Hence, in this paper, we propose a hybrid method to incrementally extract urban road networks from spatio-temporal trajectory data.

First, raw trajectory data were partitioned into K time slices and were used to initialize K-temporal road networks by a mathematical morphology method.Then, the K-temporal road networks were adjusted according to a gravitation force model so as to amend their geometric inconsistencies.Finally, road networks were Instrument Accessories geometrically delineated using the k-segment fitting algorithm, and the associated road attributes (e.g.

, road width and driving rule) were inferred.Several case studies were examined to demonstrate that our method can effectively improve the efficiency and precision of road extraction and can make a significant attempt to mine the incremental change patterns in road networks from spatio-temporal trajectory data to help with road map renewal.