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Effective Visualization of Large
Scale Dynamic Traffic Information
from a Traffic Simulation Model
Robert Tung, PhD
RST International Inc.
Bellevue, WA
7th International Symposium on Visualization in
Transportation
October 24, 2013
Beckman Center
Irvine, CA
DynuStudio/DynusT/DTA
Big Data in Travel Models
• Volume (travel models become fine grained time dependent and requires
lots of supporting data)
– From aggregate to disaggregate
– From static to dynamic
– From planning to operation
– High spatial representation
• Velocity (new data are produced in unprecedented speed)
– Real time continuous traffic data
– GPS & Smart devices
• Variety/Complexity
– Data come in many formats (video, Bluetooth, GPS)
10/24/2013 Robert Tung, RST International 2
Macro-Meso-Micro
Network Models
• Macroscopic
– Zone based trip behavior assumption
– Discrete Time period based link performance
– Non-capacity constrained static UE assignment
– Good for regional travel demand forecasting
• Mesoscopic
– Average platoon driving behavior assumption
– Continuous time based link performance
– Flow Density based DUE assignment & simulation
– Good for route choice & mode choice behavior analysis
• Microscopic
– Individual vehicle driving behavior assumption
– Sub-second vehicle simulation
– Based on car-following and lane-changing theories
– Good for inter vehicles and corridor operational studies
10/24/2013 Robert Tung, RST International 3
Network Representation
in Three Levels
10/24/2013 Robert Tung, RST International 4
Dynamic Traffic Assignment
(DynusT DTA)
• Anisotropic Mesoscopic Simulation
– Simplified car following & lane changing behavior
• Rich travel behavior representation
– Departure time choice
– Route choice & mode choice
– Diversion choice
– Sensitive to:
• Work zone, Congestion, En-route Information, Pricing,
Evacuation scenarios
• Fast computing and can handle large regional
network for 24h continuous simulation
10/24/2013 Robert Tung, RST International 5
Recent DynuStudio/DynusT DTA Model
Deployment
Model Total Lane
Miles
24 Hour
Demand
(mil)
Density
Factor
CAMPO 11,461 4.6 400
MAG 16,696 6.2 400
SCAG 67,389 34.5 500
CMAP 41,721 20.5 500
DRCOG 15,794 7.5 500
SACOG 9,594 5.2 550
PSRC 13,753 9.5 700
10/24/2013 Robert Tung, RST International 6
Visualization Needs for DTA
Simulation Models
• Diagnosis, Analysis & Presentation:
– Vehicle trajectory
– Congestion pattern
– Bottleneck and spillback
– Traffic diversion
– Temporal profiles
– System MoE
– Scenario Comparison
– Real time data
10/24/2013 Robert Tung, RST International 7
Vehicle Trajectory Display
10/24/2013 Robert Tung, RST International 8
Spillback & Bottleneck Analysis
10/24/2013 Robert Tung, RST International 9
Congestion Pattern Analysis
Density & Delays
Density & Flows
10/24/2013 Robert Tung, RST International 10
Scenario Comparison
10/24/2013 Robert Tung, RST International 11
Real Time Data
Speed & Delays
10/24/2013 Robert Tung, RST International 12
Temporal Profiles
10/24/2013 Robert Tung, RST International 13
Time Space Heat Map
10/24/2013 Robert Tung, RST International 14
Data Mining
10/24/2013 Robert Tung, RST International 15
THANK YOU!
10/24/2013 Robert Tung, RST International 16

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Trb 7th international_symposium_on_visualization_in_transportation_by_robert_tung

  • 1. Effective Visualization of Large Scale Dynamic Traffic Information from a Traffic Simulation Model Robert Tung, PhD RST International Inc. Bellevue, WA 7th International Symposium on Visualization in Transportation October 24, 2013 Beckman Center Irvine, CA DynuStudio/DynusT/DTA
  • 2. Big Data in Travel Models • Volume (travel models become fine grained time dependent and requires lots of supporting data) – From aggregate to disaggregate – From static to dynamic – From planning to operation – High spatial representation • Velocity (new data are produced in unprecedented speed) – Real time continuous traffic data – GPS & Smart devices • Variety/Complexity – Data come in many formats (video, Bluetooth, GPS) 10/24/2013 Robert Tung, RST International 2
  • 3. Macro-Meso-Micro Network Models • Macroscopic – Zone based trip behavior assumption – Discrete Time period based link performance – Non-capacity constrained static UE assignment – Good for regional travel demand forecasting • Mesoscopic – Average platoon driving behavior assumption – Continuous time based link performance – Flow Density based DUE assignment & simulation – Good for route choice & mode choice behavior analysis • Microscopic – Individual vehicle driving behavior assumption – Sub-second vehicle simulation – Based on car-following and lane-changing theories – Good for inter vehicles and corridor operational studies 10/24/2013 Robert Tung, RST International 3
  • 4. Network Representation in Three Levels 10/24/2013 Robert Tung, RST International 4
  • 5. Dynamic Traffic Assignment (DynusT DTA) • Anisotropic Mesoscopic Simulation – Simplified car following & lane changing behavior • Rich travel behavior representation – Departure time choice – Route choice & mode choice – Diversion choice – Sensitive to: • Work zone, Congestion, En-route Information, Pricing, Evacuation scenarios • Fast computing and can handle large regional network for 24h continuous simulation 10/24/2013 Robert Tung, RST International 5
  • 6. Recent DynuStudio/DynusT DTA Model Deployment Model Total Lane Miles 24 Hour Demand (mil) Density Factor CAMPO 11,461 4.6 400 MAG 16,696 6.2 400 SCAG 67,389 34.5 500 CMAP 41,721 20.5 500 DRCOG 15,794 7.5 500 SACOG 9,594 5.2 550 PSRC 13,753 9.5 700 10/24/2013 Robert Tung, RST International 6
  • 7. Visualization Needs for DTA Simulation Models • Diagnosis, Analysis & Presentation: – Vehicle trajectory – Congestion pattern – Bottleneck and spillback – Traffic diversion – Temporal profiles – System MoE – Scenario Comparison – Real time data 10/24/2013 Robert Tung, RST International 7
  • 8. Vehicle Trajectory Display 10/24/2013 Robert Tung, RST International 8
  • 9. Spillback & Bottleneck Analysis 10/24/2013 Robert Tung, RST International 9
  • 10. Congestion Pattern Analysis Density & Delays Density & Flows 10/24/2013 Robert Tung, RST International 10
  • 11. Scenario Comparison 10/24/2013 Robert Tung, RST International 11
  • 12. Real Time Data Speed & Delays 10/24/2013 Robert Tung, RST International 12
  • 13. Temporal Profiles 10/24/2013 Robert Tung, RST International 13
  • 14. Time Space Heat Map 10/24/2013 Robert Tung, RST International 14
  • 15. Data Mining 10/24/2013 Robert Tung, RST International 15
  • 16. THANK YOU! 10/24/2013 Robert Tung, RST International 16