jeffrey taylor & xuesong zhou university of utah 14 th trb national transportation
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Matching Real-world Conditions: How Do We Calibrate Capacity, OD Demand, and Path Flow in a Mesoscopic Traffic Simulator?. Jeffrey Taylor & Xuesong Zhou University of Utah 14 th TRB National Transportation Planning Applications Conference May 8 th , 2013 Columbus, Ohio. - PowerPoint PPT PresentationTRANSCRIPT
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Matching Real-world Conditions: How Do We Calibrate Capacity, OD Demand, and Path Flow
in a Mesoscopic Traffic Simulator?
Jeffrey Taylor & Xuesong ZhouUniversity of Utah
14th TRB National Transportation Planning Applications Conference
May 8th, 2013Columbus, Ohio
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Motivation: Sharing Lessons Learned
• Recent work with network conversion & calibration– Macro-to-Meso conversion – Network calibration with multiple data sources• Volume, travel time, etc.
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Motivation: Sharing Lessons Learned
• Recent development work on DTALite– Lightweight open source DTA model
• Intimate knowledge of traffic simulation model details– Shortest path, queuing, merge models, capacity
constraints, intersections…
• Disclaimer: This discussion is driven by our experience with DTALite, but may also be relevant to other simulation models
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Outline
• Introduction to DTALite• Understanding Capacity– Types of capacity– Network coding and representation
• Diagnostic Procedures for Calibration– Parameters & sensitivity
• OD Demand & Path Flow Adjustment
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Brief Intro to DTALite
• Open source DTA model, GUI code.google.com/p/nexta/
• Agent-based simulation• Capacity-constrained model• Traffic simulation models– BPR, volume-delay functions– Point Queue– Spatial Queue (with jam density)– Newell’s Model
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Traffic Simulation
Time-Varying OD Demand/ Agent Data
Traffic Simulation
Shortest Path
Time-Varying Link Travel Times Path Selection
Link Traversal
Node Transfer
Path Processing
User Decisions
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Traffic Simulation Details
• Outflow capacity• Inflow capacity• Storage capacity
Exit Queue
Outflow Capacity
Inflow Capacity
Entrance List
Storage Capacity
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Traffic Simulation Details
• Node transfer
Node
Node Transfer
Check Outflow Capacity Check Inflow CapacityCheck Storage Capacity
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Difficulties in Calibrating Capacity
• Different types of capacity– Inflow capacity, Outflow capacity, Storage capacity– Simulation parameters
Exit Queue
Outflow Capacity
Inflow Capacity
Entrance List
Storage Capacity
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Traffic Flow Model (on the Link)
• Newell’s simplified kinematic wave model– Triangular flow-density relationship– Free flow speed, jam density, backward wave speed
0 20 40 60 80 100 120 140 160 180 2000
500
1000
1500
2000
Density (vpmpl)
Flow
Rat
e (v
phpl
)
0 20 40 60 80 100 120 140 160 180 2000
10
20
30
40
50
60
70
Density (vpmpl)
Spee
d (M
PH)
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Traffic Flow Model (on the Link)
• Queue propagation– Inflow capacity = outflow capacity
Outflow Capacity
Inflow Capacity
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Difficulties in Calibrating Capacity
• Different types of capacity– Inflow capacity, Outflow capacity, Storage capacity– Simulation parameters
• Network coding and representation– Geometry– Merge/diverge– Intersections
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Converting from Macro to Meso
• Directly import important network attributes– Capacity, speed, number of lanes, etc.
• First Simulation Run: Wide-scale Gridlock (Red)
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Second Attempt
• Increased ramp outflow capacity– Still experiencing significant queuing
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Merge Models
• Distribute inflow capacity to upstream links– Lane & demand-based methods
NodeAvailable Inflow Capacity
80%
20%
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Inflow Capacity Distribution
• Dynamic capacity distribution
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Diverge Models
• Different conditions by lane• First-In-First-Out (FIFO) constraint– Relaxation to prevent extreme bottlenecks
Node
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Inflow/Storage Capacity?
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Geometry Details
• Two-lane ramp, coded with one lane– Reasonable outflow
capacity
• Potential issues– Underestimated inflow
capacity– Underestimated storage
capacity
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Traffic Flow Model Sensitivity
100 120 140 160 180 200 220 2400
10
20
30
40
50
60
70
80
90
1100
1150
1200
1250
1300
1350
1400
1450
1500Avg Travel Time (min)
Avg Trip Time Index=(Mean TT/Free-flow TT)
Avg Speed (mph)
Network Clearance Time (in min)
Jam Density (veh/mile/lane)
Net
wor
k Cl
eara
nce
Tim
e (m
inut
es)
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Third Attempt
• Reset outflow capacity, adjusted inflow & storage capacity
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Combined Modifications
• Combination of adjusting outflow and storage capacity appears more reasonable
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Signalized Intersections
• Simplified representation in DTALite– Effective green time,
saturation flow rate, movement-based capacity
– Relaxed inflow constraints
• Data sources– Manual input/adjustment– Signal timings from another
model– QEM for signal timing
estimation
Cumulative Flow Count
TimeRed Green
Arrival
Departure
Green
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Signalized Intersections
• Model sensitivity– Location dependent – Related to assignment
model?
• Initial testing with QEM– SLC network – Requires further testing
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Roundabouts
• Link lengths limited by simulation time interval– Travel time < interval
• Difficulties – Merge priority, delay– Queue storage
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OD Demand Estimation
• Combined simulation/estimation model• Gradient-based approach– Calculate travel time difference from changing one
unit of flow– Dependent upon signals, capacity
• Recommendation: Smaller adjustment %
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Path Flow Adjustment
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Recommendations
1. Macroscopic capacity may not be appropriate for mesoscopic capacity constraints
2. Understand the traffic flow model– Understand limitations, special cases
3. Adjust capacity before OD demand, path flow4. Start with fewer capacity constraints to
remove possible unrealistic bottlenecks– Point queue → Spatial queue → Shock wave →
Speed-density relationships
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Questions?