| Reference : A scalable method to construct compact road networks from GPS trajectories |
| Scientific journals : Article | |||
| Engineering, computing & technology : Computer science | |||
| http://hdl.handle.net/10993/45530 | |||
| A scalable method to construct compact road networks from GPS trajectories | |
| English | |
Guo, Yuejun  [Universitat de Girona > Graphics and Imaging Lab] | |
| Bardera, Anton [Universitat de Girona > Graphics and Imaging Lab] | |
| FOrt, Marta [Universitat de Girona > Graphics and Imaging Lab] | |
| Silveira, Rodrigo [Universitat Politècnica de Catalunya > Department de Matemàtiques] | |
| 16-Oct-2020 | |
| International Journal of Geographical Information Science | |
| Taylor & Francis | |
| 0 | |
| 0 | |
| 1-37 | |
| Yes (verified by ORBilu) | |
| International | |
| 1365-8816 | |
| 1365-8824 | |
| United Kingdom | |
| [en] GPS trajectory ; road network construction ; split-and-merge strategy ; Slide method | |
| [en] 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. | |
| Spanish Government under Grants PID2019- 106426RB-C31 and PID2019-104129GB-I00/AEI/10.13039/501100011033 ; Catalan Government under grants 2017-SGR-1101 and 2017-SGR-1640 ; Universitat de Girona under grant PONTUdG2019/11 ; Chinese Academy of Sciences President’s International Fellowship Initiative under grant 2021VTB0004 ; Secretaria d’Universitats i Recerca del Departament d’Empresa i Coneixement de la Generalitat de Catalunya ; European Social Fund | |
| http://hdl.handle.net/10993/45530 | |
| 10.1080/13658816.2020.1832229 |
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