A scalable method to construct compact road networks from GPS trajectories

Abstract

The automatic generation of road networks from GPS tracks is a challenging problem that has been receiving considerable attention in the last years. Although dozens of methods have been proposed, current techniques suffer from two main shortcomings: the quality of the produced road networks is still far from those produced manually, and the methods are slow, making them not scalable to large inputs. In this paper, we present a fast four-step density-based approach to construct a road network from a set of trajectories. A key aspect of our method is the use of an improved version of the Slide method to adjust trajectories to build a more compact density surface. The network has comparable or better quality than that of state-of-the-art methods and is simpler (includes fewer nodes and edges). Furthermore, we also propose a split-and-merge strategy that allows splitting the data domain into smaller regions that can be processed independently, making the method scalable to large inputs. The performance of our method is evaluated with extensive experiments on urban and hiking data

This work was supported by the Spanish Government under Grants PID2019- 106426RB-C31 and PID2019-104129GB-I00/AEI/10.13039/501100011033; the Catalan Government under grants 2017-SGR-1101 and 2017-SGR-1640; the Universitat de Girona under grant PONTUdG2019/11; and the Chinese Academy of Sciences President’s International Fellowship Initiative under grant 2021VTB0004. Yuejun Guo acknowledges the support from Secretaria d’Universitats i Recerca del Departament d’Empresa i Coneixement de la Generalitat de Catalunya and the European Social Fund