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Hengfeng Li, Lars Kulik, Kotagiri Ramamohanarao Department of Computing and Information Systems The University of Melbourne [email protected] Robust Inferences of Travel Paths from GPS Trajectories PROBLEM METHOD CASE STUDY CONTRIBUTIONS Problem: How to infer a travel path on the road network from a GPS trace under noisy conditions? We propose a spatial-linear clustering algorithm to group noisy GPS points: Proposing an efficient cluster-based mapping algorithm; Conducting extensive experiments on both synthetic and real data sets; Demonstrating a case study by applying resulting travel paths for traffic flow monitoring. p 1 p 2 p 3 p 4 p 5 p 6 e 1 e 2 e 3 e 4 p 1 p 2 p 3 p 4 p 5 p 6 e 1 e 2 e 3 e 4 GPS Point Road Segment Travel Path (Ground Truth) Linear Cluster p 1 p 2 p 3 p 4 p 5 p 6 e 1 e 2 e 3 e 4 p 0 4 Projection Point p 0 3 p 00 4 (a) A GPS trace (1Hz sampling rate) (b) A possible solution (c) A better solution Our Method: (a) employ spatial-linear clustering to group GPS points; (b) derive and amalgamate sub-paths of point clusters. Noisy Point Anchor Point GPS Point Road Segment d error R 1 R 2 p 2 p 3 p 1 p outlier R 1 R 2 p 2 p 1 p 3 p outlier (a) (b) An example to build groups of GPS points by increasing an oriented bounding rectangle with error-bounded distance. Anchor Point Road Segment E 1 E 2 E 3 R E 4 E 5 E 6 E 7 E 8 E 9 Major axis E 0 X E 0 E 1 E 2 E 3 E 4 E 5 E 6 E 7 E 8 E 9 X An example of generating possible routes bounded by the spatial- linear cluster. (a) (b) We conduct a case study to analyze the impact of map matching for the estimation of traffic flow of a large area. (a) Travel distance and travel time (b) Actual overall traffic (c) Ground truth (d) OBR-HMM (e) HMM (f) Ground truth (g) OBR-HMM (h) HMM (i) Ground truth (j) OBR-HMM (k) HMM OBR-HMM: Our method HMM: Competing algorithm RELATED PUBLICATIONS Hengfeng Li, Lars Kulik, Kotagiri Ramamohanara: Robust Inferences of Travel Paths from GPS Trajectories. Submitted to International Journal of Geographical Information Science (under review). Hengfeng Li, Lars Kulik, Kotagiri Ramamohanara: Spatio-Temporal Trajectory Simplification for Inferring Travel Paths. In proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL 2014), pages 63-72. We derive travel paths for point clusters and combine them into a single complete path: Poster theme adopted from TikZposter

Robust Inferences of Travel Paths from GPS Trajectories · Poster theme adopted from TikZposter. Title: cis-poster-2015 Created Date: 7/1/2015 6:26:43 AM

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Page 1: Robust Inferences of Travel Paths from GPS Trajectories · Poster theme adopted from TikZposter. Title: cis-poster-2015 Created Date: 7/1/2015 6:26:43 AM

Hengfeng Li, Lars Kulik, Kotagiri Ramamohanarao Department of Computing and Information SystemsThe University of [email protected]

Robust Inferences of Travel Paths from GPS Trajectories

PROBLEM

METHOD

CASE STUDY

CONTRIBUTIONS

Problem: How to infer a travel path on the road network from a GPS trace under noisy conditions?

We propose a spatial-linear clustering algorithm to group noisy GPS points:

Proposing an efficient cluster-based mapping algorithm; Conducting extensive experiments on both synthetic and real data sets; Demonstrating a case study by applying resulting travel paths for traffic flow monitoring.

p1p2

p3

p4

p5p6

e1 e2

e3

e4

p1p2

p3

p4

p5p6

e1

e2

e3

e4

GPS Point

Road SegmentTravel Path (Ground Truth)Linear Cluster

p1p2

p3

p4

p5p6

e1 e2

e3

e4

p04

Projection Point

p03 p004

(a) A GPS trace (1Hz sampling rate)

(b) A possible solution (c) A better solution

Our Method:

(a) employ spatial-linear clustering to group GPS points; (b) derive and amalgamate sub-paths of point clusters.

Noisy PointAnchor Point

GPS Point

Road Segment

derrorR1

R2

p2

p3

p1

poutlier

R1

R2

p2

p1

p′

3

poutlier

(a) (b)An example to build groups of GPS points by increasing an oriented bounding rectangle with error-bounded distance.

Anchor PointRoad Segment

E1

E2

E3

RE4

E5

E6

E7

E8

E9

Major axis

E0

X

E0 E1

E2E3 E4

E5E6 E7

E8 E9

✓ ✓

✓X

An example of generating possible routes bounded by the spatial-linear cluster.

(a) (b)

We conduct a case study to analyze the impact of map matching for the estimation of traffic flow of a large area.

(a) Travel distance and travel time (b) Actual overall traffic

(c) Ground truth (d) OBR-HMM (e) HMM

(f) Ground truth (g) OBR-HMM (h) HMM

(i) Ground truth (j) OBR-HMM (k) HMM

OBR-HMM: Our method HMM: Competing algorithm

RELATED PUBLICATIONSHengfeng Li, Lars Kulik, Kotagiri Ramamohanara: Robust Inferences of Travel Paths from GPS Trajectories. Submitted to International Journal of Geographical Information Science (under review). Hengfeng Li, Lars Kulik, Kotagiri Ramamohanara: Spatio-Temporal Trajectory Simplification for Inferring Travel Paths. In proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL 2014), pages 63-72.

We derive travel paths for point clusters and combine them into a single complete path:

Poster theme adopted from TikZposter