Data-driven trajectory smoothing

Authors:
Frederic Chazal;Daniel Chen;Leonidas Guibas;Xiaoye Jiang;Christian Sommer
Affiliations:
INRIA Saclay -- Île-de-France, Orsay, France;Stanford University, Stanford, CA;Stanford University, Stanford, CA;Stanford University, Stanford, CA;MIT, Cambridge, MA
Venue:
Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Year:
2011

Citing 21
Cited 1

A randomized algorithm for closest-point queries

SIAM Journal on Computing
Stochastic differential equations (3rd ed.): an introduction with applications

Stochastic differential equations (3rd ed.): an introduction with applications
Approximate nearest neighbors: towards removing the curse of dimensionality

STOC '98 Proceedings of the thirtieth annual ACM symposium on Theory of computing
A Unified Approach to Detecting Spatial Outliers

Geoinformatica
A Replacement for Voronoi Diagrams of Near Linear Size

FOCS '01 Proceedings of the 42nd IEEE symposium on Foundations of Computer Science
An Introduction to the Kalman Filter

An Introduction to the Kalman Filter
Cover trees for nearest neighbor

ICML '06 Proceedings of the 23rd international conference on Machine learning
Nearest neighbors search using point location in balls with applications to approximate Voronoi decompositions

Journal of Computer and System Sciences
Detecting graph-based spatial outliers

Intelligent Data Analysis
Near-optimal hashing algorithms for approximate nearest neighbor in high dimensions

Communications of the ACM - 50th anniversary issue: 1958 - 2008
GeoAnalytics visual inquiry and filtering tools in parallel coordinates plots

Proceedings of the 15th annual ACM international symposium on Advances in geographic information systems
Closest-point problems

SFCS '75 Proceedings of the 16th Annual Symposium on Foundations of Computer Science
Cleaning uncertain data with quality guarantees

Proceedings of the VLDB Endowment
From GPS traces to a routable road map

Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Activity identification from GPS trajectories using spatial temporal POIs' attractiveness

Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Location Based Social Networks
A variant of particle filtering using historic datasets for tracking complex geospatial phenomena

Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems
Usability analysis of compression algorithms for position data streams

Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems
Spatial outlier detection: random walk based approaches

Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems
Learning travel recommendations from user-generated GPS traces

ACM Transactions on Intelligent Systems and Technology (TIST)
Integration of GPS traces with road map

Proceedings of the Second International Workshop on Computational Transportation Science
Geometric Inference for Probability Measures

Foundations of Computational Mathematics

Of motifs and goals: mining trajectory data

Proceedings of the 20th International Conference on Advances in Geographic Information Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

Motivated by the increasing availability of large collections of noisy GPS traces, we present a new data-driven framework for smoothing trajectory data. The framework, which can be viewed of as a generalization of the classical moving average technique, naturally leads to efficient algorithms for various smoothing objectives. We analyze an algorithm based on this framework and provide connections to previous smoothing techniques. We implement a variation of the algorithm to smooth an entire collection of trajectories and show that it performs well on both synthetic data and massive collections of GPS traces.