From Trajectories of Moving Objects toRoute-Based Traffic Prediction and

Management

by

Gyozo Gidofalvi

Ehsan Saqib

Presented by Bo Mao

Developing a Benchmark for Using Trajectories ofMoving Objects in Traffic Prediction and Management

2010-09-14 1MPA'10 (GIScience 2010)

Route-Based Traffic Prediction and Management

Example traffic prediction and management tasks: Estimate current/future traffic flow Predict the near-future locations of vehicles Which vehicles to inform in case of an event? How and which vehicles to re-route in case of an event?

2010-09-14 MPA'10 (GIScience 2010) 2

Traffic problems Adoption of GPSRoad network based movement

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Home

Work

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Pred. or act.traffic event

Renewablepseudo ID

Frequent routes are explicit inference units

Route-Based Traffic Predictionand Management Server

Steam of Evolving Traj.

RelevantTraffic Info

RecentTraj.

Traj.MiningUnit

Frequent Routes

Traj. Pred.Unit

Traffic Mngt.Unit

Frequent Route Knowledge Bank

k-anonymity

Trajectory Data

Number of objects: 1500 taxis and 400 trucks Measuring technology: GPS (+ accelerometer) based

measuring position (+ speed and heading) Location sampling: every 60 sec for taxies with passengers

(off-route less frequently) and every 30 sec for trucks Area/extent: Greater Stockholm area approximately 100km

by 100km Data rate/size: 170 million measurements per year / 1000

measurements per minute Availability: provided by Trafik Stockholm and is available at

the Transport and Logistic Division of the Department of Urban Planning and Environment, Royal Institute of Technology (KTH), Sweden

2010-09-14 MPA'10 (GIScience 2010) 3

Trajectory Data (2)

2010-09-14 MPA'10 (GIScience 2010) 4

Measurements for 100 vehicles for a day Raw trajectories for 10 vehicles for a day

Traffic Management Benchmark

Need to design a benchmark to evaluate the performance, accuracy and scalability of a proposed traffic management system.

Design considerations: Trajectory sample bias: taxis are special Absence of individual mobility patterns: methods relying on such

patterns cannot be meaningfully evaluated Need for privacy: evaluation under different privacy requirements Realistic scalability tests: simple duplication of data does not

increase spatial-temporal density of it and is thus unrealistic

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Mobility Patterns

2010-09-14 MPA'10 (GIScience 2010) 6

Frequent routes (speed + flow) for a day Speed deviations from the daily norm at 8am