1. From Trajectories of Moving Objects to Route-Based Traffic Prediction and Management by Gyozo Gidofalvi Ehsan Saqib Presented by Bo Mao Developing a Benchmark for Using Trajectories of Moving Objects in Traffic Prediction and Management 2010-09-141MPA'10 (GIScience 2010)

2. 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? 2010-09-14MPA'10 (GIScience 2010)2 Traffic problems MOD Adoption of GPS Road network based movement Location anonymization Home Work (s i,Δt i ) Pred. or act. traffic event Renewable pseudo ID Frequent routes are explicit inference units Route-Based Traffic Prediction and Management Server Steam of Evolving Traj. Relevant Traffic Info Recent Traj. Mining Unit Frequent Routes Traj. Pred. Unit Traffic Mngt. Unit Frequent Route Knowledge Bank k-anonymity

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

4. Trajectory Data (2) 2010-09-14MPA'10 (GIScience 2010)4 Measurements for 100 vehicles for a dayRaw trajectories for 10 vehicles for a day

5. 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 2010-09-14MPA'10 (GIScience 2010)5

6. Mobility Patterns 2010-09-14MPA'10 (GIScience 2010)6 Frequent routes (speed + flow) for a daySpeed deviations from the daily norm at 8am