ABSTRACT
Road networks are important datasets for an increasing number of applications. However, the creation and maintenance of such datasets pose interesting research challenges. This work proposes an automatic road network generation algorithm that takes vehicle tracking data in the form of trajectories as input and produces a road network graph. This effort addresses the challenges of evolving map data sets, specifically by focusing on (i) automatic map-attribute generation (weights), (ii) automatic road network generation, and (iii) by providing a quality assessment. An experimental study assesses the quality of the algorithms by generating a part of the road network of Athens, Greece, using trajectories derived from GPS tracking a school bus fleet.
- http://www.openstreetmap.org/.Google Scholar
- M. Aanjaneya, F. Chazal, D. Chen, M. Glisse, L. J. Guibas, and D. Morozov. Metric graph reconstruction from noisy data. In Proc. 27th SoCG, pages 37--46, 2011. Google ScholarDigital Library
- M. Ahmed and C. Wenk. Constructing street networks from gps trajectories. In Proc. 20th ESA conf., 2012. Google ScholarDigital Library
- J. K. Alireza Fathi. Inferring the road network from gps data. In Geographic Information Science, volume 6292, pages 56--69, 2010. Google ScholarDigital Library
- A. Baumgartner, S. Hinz, and C. Wiedemann. Efficient methods and interfaces for road tracking. In International Archives of Photogrammetry and Remote Sensing, page B:28, 2002.Google Scholar
- S. Brakatsoulas, D. Pfoser, R. Salas, and C. Wenk. On map-matching vehicle tracking data. In Proc. 31st VLDB conf., pages 853--864, 2005. Google ScholarDigital Library
- R. Bruntrup, S. Edelkamp, S. Jabbar, and B. Scholz. Incremental map generation with gps traces. In Proc. IEEE ITS conf., pages 574--579, 2005.Google ScholarCross Ref
- L. Cao and J. Krumm. From gps traces to a routable road map. In Proc. 17th ACM GIS conf., pages 3--12, 2009. Google ScholarDigital Library
- D. Chen, L. J. Guibas, J. Hershberger, and J. Sun. Road network reconstruction for organizing paths. In Proc. 21st ACM-SIAM Symp. on Discrete Algorithms, pages 1309--1320, 2010. Google ScholarDigital Library
- Y. Chen and J. Krumm. Probabilistic modeling of traffic lanes from gps traces. In Proc. 18th ACM GIS conf., pages 81--88, 2010. Google ScholarDigital Library
- S. Edelkamp and S. Schrödl. Computer science in perspective. chapter Route planning and map inference with global positioning traces, pages 128--151. Springer-Verlag New York, Inc., New York, NY, USA, 2003. Google ScholarDigital Library
- T. Guo, K. Iwamura, and M. Koga. Towards high accuracy road maps generation from massive gps traces data. In Proc. IEEE IGARSS conf., pages 667--670, 2007.Google ScholarCross Ref
- J. Hu, A. Razdan, J. Femiani, M. Cui, and P. Wonka. Road network extraction and intersection detection from aerial images by tracking road footprints. IEEE T. Geoscience and Remote Sensing, 45(12--2):4144--4157, 2007.Google Scholar
- J.-G. Lee, J. Han, and K.-Y. Whang. Trajectory clustering: a partition-and-group framework. In Proc. SIGMOD conf., pages 593--604, 2007. Google ScholarDigital Library
- Y. Liu. An automation system: generation of digital map data from pictorial map resources. Pattern Recognition, 35(9):1973--1987, 2002.Google ScholarCross Ref
- S. Morris and K. Barnard. Finding trails. In Proc. IEEE CVPR conf., 2008.Google ScholarCross Ref
- S. Schroedl, K. Wagstaff, S. Rogers, P. Langley, and C. Wilson. Mining gps traces for map refinement. Data Min. Knowl. Discov., 9:59--87, July 2004. Google ScholarDigital Library
- M. Tavakoli and A. Rosenfeld. Building and road extraction from aerial photographs. IEEE Transactions on Systems, Man and Cybernetics, 12(1):84--91, 1982.Google ScholarCross Ref
- S. Worrall and E. Nebot. Automated process for generating digitised maps through gps data compression. In Australasian Conference on Robotics and Automation, 2007.Google Scholar
- H. Zhao, J. Kumagai, M. Nakagawa, and R. Shibasaki. Semi automatic road extraction from high resolution satellite image. In Proc. Photogrammetric Computer Vision ISPRS Commission III Symp., pages 406--411, 2002.Google Scholar
Index Terms
- On vehicle tracking data-based road network generation
Recommendations
Dynamic travel time provision for road networks
GIS '08: Proceedings of the 16th ACM SIGSPATIAL international conference on Advances in geographic information systemsThe application domain of intelligent transportation is plagued by a shortage of data sources that adequately assess traffic situations. Typically, to provide routing and navigation solutions map attributes in the form of static weights as derived from ...
Road Map Generation and Feature Extraction from GPS Trajectories Data
IWCTS'19: Proceedings of the 12th ACM SIGSPATIAL International Workshop on Computational Transportation ScienceRoad maps are important in our personal lives and are widely used in many different applications. Therefore, an up-to-date road map is essential. The huge amount of GPS data collected from moving objects provides an opportunity to generate an up-to-date ...
Virtual running model for locating road intersections using GPS trajectory data
IMCOM '17: Proceedings of the 11th International Conference on Ubiquitous Information Management and CommunicationMap construction from vehicle trajectories has been an active challenge topic due to the progress of positioning technologies and high cost of map constructions since last decades. In our work, we focus on road network generation from a massive GPS data ...
Comments