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From Trajectories of Moving Objects to Route-Based Traffic Prediction and Management

From Trajectories of Moving Objects to Route-Based Traffic Prediction and Management. Developing a Benchmark for Using Trajectories of Moving Objects in Traffic Prediction and Management. by Gyozo Gidofalvi Ehsan Saqib Presented by Bo Mao. Route-Based Traffic Prediction and Management.

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From Trajectories of Moving Objects to Route-Based Traffic Prediction and Management

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

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

  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 MPA'10 (GIScience 2010)

  4. Trajectory Data (2) Measurements for 100 vehicles for a day Raw trajectories for 10 vehicles for a day MPA'10 (GIScience 2010)

  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 MPA'10 (GIScience 2010)

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

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