T-drive: driving directions based on taxi trajectories
Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems
A conceptual framework and taxonomy of techniques for analyzing movement
Journal of Visual Languages and Computing
Driving with knowledge from the physical world
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Where to find my next passenger
Proceedings of the 13th international conference on Ubiquitous computing
Discovering personalized routes from trajectories
Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Location-Based Social Networks
Making a pictorial and verbal travel trace from a GPS trace
W2GIS'12 Proceedings of the 11th international conference on Web and Wireless Geographical Information Systems
Exploration of ground truth from raw GPS data
Proceedings of the ACM SIGKDD International Workshop on Urban Computing
Proceedings of the ACM SIGKDD International Workshop on Urban Computing
Quick map matching using multi-core CPUs
Proceedings of the 20th International Conference on Advances in Geographic Information Systems
Encoding network-constrained travel trajectories using routing algorithms
International Journal of Knowledge and Web Intelligence
U-Air: when urban air quality inference meets big data
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Large-scale joint map matching of GPS traces
Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Crowd sensing of traffic anomalies based on human mobility and social media
Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
From taxi GPS traces to social and community dynamics: A survey
ACM Computing Surveys (CSUR)
Hi-index | 0.00 |
Matching a raw GPS trajectory to roads on a digital map is often referred to as the Map Matching problem. However, the occurrence of the low-sampling-rate trajectories (e.g. one point per 2 minutes) has brought lots of challenges to existing map matching algorithms. To address this problem, we propose an Interactive Voting-based Map Matching (IVMM) algorithm based on the following three insights: 1) The position context of a GPS point as well as the topological information of road networks, 2) the mutual influence between GPS points (i.e., the matching result of a point references the positions of its neighbors; in turn, when matching its neighbors, the position of this point will also be referenced), and 3) the strength of the mutual influence weighted by the distance between GPS points (i.e., the farther distance is the weaker influence exists). In this approach, we do not only consider the spatial and temporal information of a GPS trajectory but also devise a voting-based strategy to model the weighted mutual influences between GPS points. We evaluate our IVMM algorithm based on a user labeled real trajectory dataset. As a result, the IVMM algorithm outperforms the related method (ST-Matching algorithm).