Hierarchical optimization of optimal path finding for transportation applications
CIKM '96 Proceedings of the fifth international conference on Information and knowledge management
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SSD '99 Proceedings of the 6th International Symposium on Advances in Spatial Databases
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Computing the shortest path: A search meets graph theory
SODA '05 Proceedings of the sixteenth annual ACM-SIAM symposium on Discrete algorithms
On map-matching vehicle tracking data
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Heuristic shortest path algorithms for transportation applications: state of the art
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Addressing the Need for Map-Matching Speed: Localizing Globalb Curve-Matching Algorithms
SSDBM '06 Proceedings of the 18th International Conference on Scientific and Statistical Database Management
Adaptive fastest path computation on a road network: a traffic mining approach
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Scalable network distance browsing in spatial databases
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
GeoLife: Managing and Understanding Your Past Life over Maps
MDM '08 Proceedings of the The Ninth International Conference on Mobile Data Management
GeoLife2.0: A Location-Based Social Networking Service
MDM '09 Proceedings of the 2009 Tenth International Conference on Mobile Data Management: Systems, Services and Middleware
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SeMiTri: a framework for semantic annotation of heterogeneous trajectories
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WAIM'11 Proceedings of the 12th international conference on Web-age information management
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Map-matching is the process of aligning a sequence of observed user positions with the road network on a digital map. It is a fundamental pre-processing step for many applications, such as moving object management, traffic flow analysis, and driving directions. In practice there exists huge amount of low-sampling-rate (e.g., one point every 2--5 minutes) GPS trajectories. Unfortunately, most current map-matching approaches only deal with high-sampling-rate (typically one point every 10--30s) GPS data, and become less effective for low-sampling-rate points as the uncertainty in data increases. In this paper, we propose a novel global map-matching algorithm called ST-Matching for low-sampling-rate GPS trajectories. ST-Matching considers (1) the spatial geometric and topological structures of the road network and (2) the temporal/speed constraints of the trajectories. Based on spatio-temporal analysis, a candidate graph is constructed from which the best matching path sequence is identified. We compare ST-Matching with the incremental algorithm and Average-Fréchet-Distance (AFD) based global map-matching algorithm. The experiments are performed both on synthetic and real dataset. The results show that our ST-matching algorithm significantly outperform incremental algorithm in terms of matching accuracy for low-sampling trajectories. Meanwhile, when compared with AFD-based global algorithm, ST-Matching also improves accuracy as well as running time.