3. New Generation Travel Models
Travel models are increasingly
disaggregate
• 30 Activity Based Models (ABM)
underway in North America
• Tour-based models in Australia
• Generally increasing market
segmentation, temporal and spatial
resolution
Data produced is on a different scale:
• Seattle ABM ~ 3 million people, 14
million activities, 1.6 million parcels
• Phoenix ABM ~ 4 million people, 20
million activities, 26000 MAZs
4. Challenges
Working with disaggregate mobility
data
• Computationally intensive
Processing
Querying
Visualizing
• Aggregations
All aggregations result in data loss
Time consuming
• Reproducibility
• Mobility vs Spatial
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9. Large-scale Traffic Simulation Models
• Metropolitan to regional scale
• Higher realism/fidelity “operational”
models
• Desire to inform demand models with
traffic simulation
Data produced is on a different scale:
• San Francisco ~ 700,000 veh demand
• Seattle ~ 460,000 vehicles veh
demand
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12. Other models and mobility data
• Passive mobility data collection
AVL, AFC
Cell, GPS
Trajectories
• Commercial freight and goods data
• Household travel surveys
• Mobile phones -> demand
• Land-use models
• We need
Methods to explore this data
And tools to work with them efficiently
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21. A Lot of Model Data Still Goes Unseen
• Meaningful data from models, but very disaggregate
Opportunities
• Validation and QA
• Decision support and planning with integrated models
• “Socialization” of models
22. Visual analytics for large-scale spatial and mobility data
• Animate large-scale data sets in 3D
• Explore, chart and analyze data through interactive visual
queries
• Detailed and responsive scenes at the metropolitan and
regional level with millions of geometries in the frame
• Filter, color, query, chart
• Reproducible visual analytics with Python
CityPhi