Understanding User Mobility Based on GPS Data

Yu ZhengMicrosoft Research Asia

Outline

• Introduction• Architecture• Walk-Based Segmentation• Feature Extraction• Graph-based post-processing• Experiments• Conclusion

Introduction (1)• Goal & Results: Inferring transportation modes from raw GPS data

– Differentiate driving, riding a bike, taking a bus and walking– Achieve a 0.75 inference accuracy (independent of other sensor data)

GPS log

Users

Infer model

Introduction (2)• Motivation

– For users: • Reflect on past events and understand their own life pattern• Obtain more reference knowledge from others’ experiences

– For service provider:• Classify trajectories of different transportation modes• Enable smart-route design and recommendation

• Difficulty– Velocity-based method cannot handle this problem well (<0.5 accuracy)– People usually transfer their transportation modes in a trip– The observation of a mode is vulnerable to traffic condition and weather

Introduction (2)

• Contributions and insights– A change point-based segmentation method

• Walk is a transition between different transportation modes• Handle congestions to some extent

– A set of sophisticated features• Robust to traffic condition• Feed into a supervise learning-based inference model

– A graph-based post-processing• Considering typical user behavior• Employing location constrains of the real world

• WWW 2008 (first version)

Architecture

Change Point Clustering

Training Data

Segmentation

Extracting Feature

Knowledge Extraction

Test Data

Segmentation

Extracting Feature

Post-ProcessingSpatial

Knowledge

Model Training Inference

Model

Trans. Modes

Spatial IndexingSpatially IndexedKnowledge

Online Inference Offline Learning

Graph Building

Walk-Based Segmentation

• Commonsense knowledge from the real world– Typically, people need to walk before transferring transportation modes– Typically, people need to stop and then go when transferring modes

Walk-Based Segmentation• Change point-based Segmentation Algorithm

– Step 1: distinguish all possible Walk Points, non-Walk Points. – Step 2: merge short segment composed by consecutive Walk Points or non-Walk points– Step 3: merge consecutive Uncertain Segment to non-Walk Segment.– Step 4: end point of each Walk Segment are potential change points

WalkBus

Certain Segment

Denotes a non-walk Point: P.V>Vt or P.a>at

Denotes a possible walk point: P.V<Vt and P.a<at

(b)

(c)

Backward Forward

Car

(a)

Certain Segment3 Uncertain Segments

Car

Feature Extraction (1)

• Features

Category Features Significance

Basic Features

Dist Distance of a segment

MaxVi The ith maximal velocity of a segment

MaxAi The ith maximal acceleration of a segment

AV Average velocity of a segment

EV Expectation of velocity of GPS points in a segment

DV Variance of velocity of GPS points in a segment

Advanced

Features

HCR Heading Change Rate

SR Stop Rate

VCR Velocity Change Rate

Feature Extraction (2)• Our features are more discriminative than velocity

– Heading Change Rate (HCR)– Stop Rate (SR)– Velocity change rate (VCR)– >65 accuracy

H1p1

p2

p3

p1.V1p2.V

2

L1, T1

p1. head p2. head

Velocity

Velocity

Velocity

Distance

Distance

Distance

a) Driving

b) Bus

c) Walking

Vs

Vs

Vs

Graph-Based Post-Processing (1)• Using location-constraints to improve the inference performance??

Bus Stop Traffic Light

Crossroad

Graph-Based Post-Processing (2)• Transition probability between different transportation modes

– P(Bike|Walk) and P(Bike|Driving)

Segment[i-1]: Driving Segment[i]: Walk Segment[i+1]: Bike

P(Driving): 75%P(Bus): 10%P(Bike): 8%P(Walk): 7%

P(Bike): 62%P(Walk): 24%P(Bus): 8%P(Driving): 6%

P(Bike): 40%P(Walk): 30%P(Bus): 20%P(Driving): 10%

Ground Truth

Inference result

Transition P(Walk|Driving) Transition P(Bike|Walk)

Segment[i].P(Bike) = Segment[i].P(Bike) * P(Bike|Car)

Segment[i].P(Walk) = Segment[i].P(Walk) * P(Walk|Car)

Graph-Based Post-Processing (3)

M={Driving, Walk, Bike, Bus}, E.g., P(M0) = P(Driving); P(M3|M1)= P(Bus | Walk);

N1 N2

N7 N8

N6N5

N3

N1 N2

N5

N3

N4

N1 N4N8 N5

P18(Mi)

P185(Mi|Mj)

Building Graph

(3) Spatial indexing(4) Probability calculation

N7 N8

N6

Change points and start/end points

(1) (2)

A start or end point A change point

P85(Mi) P54(Mi)

P854(Mi|Mj)

P581(Mi|Mj) P458(Mi|Mj)

• Mine a implied road network from users’ GPS logs – Use the location constraints and typical user behaviors as probabilistic cues– Being independent of the map information

Graph-Based Post-Processing (4)

Inference model Knowledge Mining

Features X Labeled data

Posterior Probability P(mi | X)

Posterior Probability P(mi | Eij)

Prior ProbablityP(mi)

Final Results: P(mi | X, Eij)= P(mi | X) P(mi | Eij) / P(mi)

Search spatial index

Found in graph ?

Segments of a GPS trajectory

Normal post-processing

Output the mode with maxProb as

result

Is maxProb > T1?

Transition probability-based

enhancement

Is maxProb < T2?

Prior probability-based enhancement

N

NN

Output the mode with maxProb as result

Y

End

Y

Experiments (1)

• Framework of Experiments

Change point-based segmentation method

Normal post-processing Graph-based post-processing

Basic Features New Features

Inference model based on Decision Tree

Data and Devices

Experiments (2)

Rank Feature AS AD Rank Feature AS AD

1 HCR 0.345 0.561 8 DV 0.269 0.357

2 SR 0.335 0.561 9 MaxV2 0.322 0.344

3 AV 0.382 0.547 10 MaxV1 0.294 0.257

4 VCR 0.336 0.526 11 MaxA2 0.239 0.217

5 EV 0.375 0.523 12 MaxA1 0.259 0.208

6 Dist 0.302 0.499 13 MaxA3 0.256 0.197

7 MaxV3 0.334 0.365

• Single Feature Exploration

Feature Combinations

Transportation mode Chang PointAS AD Precision Recall

MaxA1 + MaxA2 + MaxA3 0.297 0.283 0.118 0.584

MaxV1 + MaxV2 + MaxV3 0.480 0.526 0.142 0.687

Distance + EV + AV 0.480 0.550 0.227 0.582

Distance + EV + MaxV1 0.548 0.597 0.217 0.55

AV + EV + MaxV1 0.558 0.621 0.253 0.603

MaxV3 + MaxA3 + AV 0.511 0.632 0.138 0.669

SR + HCR + VCR 0.575 0.644 0.286 0.643

Basic Features 0.618 0.673 0.284 0.681Basic Features + Advanced Features 0.635 0.715 0.373 0.724

AD CP/P CP/R

Enhanced Features (EF) 0.728 0.491 0.817

EF + normal post-processing 0.741 0.508 0.818

EF + graph-based post-processing 0.762 0.516 0.818

Ground truth

Predicted Results (KM)

Walk Driving Bus Bike

Walk 1026.4 122.1 386.5 357.3 0.543 Recall

Driving 42.6 2477.3 458.5 235.1 0.771

Bus 34.8 164.7 1752.4 46.2 0.877

Bike 49.3 113.5 31.9 1234.3 0.864

0.891 0.861 0.666 0.6590.762

Precision