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Industry processing algorithms:

Carola Wenk, Tulane University. Extract road network from density Topological approach on neighborhood complex: [GSBW11]: Uses Reeb graph to model branching structure Intersection linking [FK10, KP12]: First detect intersections, then connect them using road segments Incremental trajectory insertion [AW12]: Insert one trajectory at a time using partial map-matching.

Uses a single precision parameter. Biagioni, J. Wang, Y. Li , Efficient map reconstruction and augmentation via topological methods. Ge, I. Safa, M. Belkin, Y. Wang, Data skeletonization via Reeb graphs, Conf. Neural Inf. Systems: , Dey, J. Ahmed, C. Fathi, J. Karagiorgou, D. Buchin, M. Buchin, D. Duran, B. Fasy, R. Jacobs, V. Sacristan, R.


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Silveira, F. Staals, C. Asahara, A.

Map Construction Algorithms | lehejymosido.ml

Sato, and K. Evaluation of trajectory clustering based on information criteria for human activity analysis. In 10th Int. Buchin, J. Gudmundsson, M. Detecting commuting patterns by clustering subtrajectories. Finding long and similar parts of trajectories. Dahlbom and L. Trajectory clustering for coastal surveillance.

Lee, J. Han, and K. Trajectory clustering: A partition-and-group framework. In Proc.

Algorithms for Map Construction and Comparison:

Li, W. Hu, and W. A coarse-to-ne strategy for vehicle motion trajectory clustering. In 18th Int. Incremental clustering for trajectories. Master's thesis, University of Illinois at Urbana-Champaign, Li, J. Lee, X. Li, and J. Clustering of time series data - a survey.

Pattern Recognition, , Zhang and D. A trajectory clustering algorithm based on symmetric neighborhood. Zhou, T. Safa, M. Belkin, Y. Wang, Data skeletonization via Reeb graphs, Conf. Neural Inf.

Trie Data Structure (EXPLAINED)

Systems: , Dey, J. Ahmed, C. Fathi, J. Karagiorgou, D.

Map Construction Algorithms

Buchin, M. Buchin, D. Duran, B. Fasy, R. Jacobs, V. Sacristan, R. Silveira, F. Staals, C. Asahara, A. Sato, and K. Evaluation of trajectory clustering based on information criteria for human activity analysis. In 10th Int. Buchin, J. Gudmundsson, M. Detecting commuting patterns by clustering subtrajectories. Finding long and similar parts of trajectories. Dahlbom and L. Trajectory clustering for coastal surveillance.

Lee, J. Han, and K. Trajectory clustering: A partition-and-group framework. In Proc. Li, W. Hu, and W. A coarse-to-ne strategy for vehicle motion trajectory clustering. In 18th Int. Incremental clustering for trajectories. Master's thesis, University of Illinois at Urbana-Champaign, Li, J. Lee, X. Li, and J.

Apply a Metaheuristic

Clustering of time series data - a survey. Pattern Recognition, , Zhang and D. A trajectory clustering algorithm based on symmetric neighborhood. Zhou, T. Bag of segments for motion trajectory analysis. ICIP, p , Detect adaptive spatial proximity parameter automatically different road widths Map construction algorithm based on sub-trajectory clustering Phase 1: Compute relevant bundles Phase 2: Construct map by greedy incremental insertion of bundles Trajectory bundles can handle data with irregular sampling rates.

Extract finer-grained geometry zig-zags ; useful for hiking trail maps Carola Wenk, Tulane University. A relevant bundle is maximal, stable, and large. Consider bundles in order of decreasing size. Stability: Compute bundle evolution by pairwise bundle comparison. Sweep Ls until there are at least l cluster curves, then sweep Lt until there are less than l cluster curves. For the discrete Frechet distance, cluster curves can be found by greedily finding monotone curves in free space graph.


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Add edges to map. Subset of the Chicago dataset mapconstruction.