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Calibrating a Region-wide
Microsimulation Model: Maricopa Association of Governments
TRB Innovations in
Travel Modeling 2014
Baltimore , MD
Daniel Morgan (daniel@caliper.com)
David Gerstle (david@caliper.com)
Caliper Corporation
Purpose
• Add analysis of operations and traffic simulation modeling to
the services MAG offers to its member agencies
• Build a model to complement MAG’s regional travel demand
model that:
• Has the operational sensitivity to capture effects of signal
operations, ITS projects
• Is able to capture the mobility benefits of major projects whose
impacts will be felt throughout Central Phoenix
• Accurately portrays the traffic impacts of transit improvements,
namely on high-capacity transit corridors
• Provides a calibrated base model from which smaller, more
focused studies can be derived
Approach
• Study Design Stage
• Solicit stakeholder input/support
• Plan model framework, design parameters, and geographic scope
• Model Data Preparation
• Assemble traffic count and signal timing data
• Develop simulation model network
• Framework Development and Testing
• Test, calibrate, and validate the model
• Training
Approach
• Study Design Stage
• Solicit stakeholder input/support
• Plan model framework, design parameters, and geographic scope
• Model Data Preparation
• Assemble traffic count and signal timing data
• Develop simulation model network
• Framework Development and Testing
• Test, calibrate, and validate the model
• Training
Design
• A model congruous with the regional travel demand model
• Objective: To achieve a degree of integration with the regional travel demand model such that they can share key model data seamlessly
• Solution: A simulation model in TransModeler capable of reading all file formats and data structures of the regional model in TransCAD and sharing a common zonal system (and, hence, ready exchange of origin-destination matrices)
• A multi-resolution traffic simulation model
• Objective: A simulation model with an appropriate balance of high-fidelity treatment of traffic flow phenomena and practical computational performance
• Solution: A microsimulation model enabling selective application of lower-resolution (e.g., meso) and multi-resolution (e.g., hybrid micro-meso) models
Development
1. Preparation of highly
detailed lane-level
geography/geometry
2. Import of centroids
and connectors from
regional model
3. Auto-adjustment of
TAZ connectivity
4. Manual addition of
centroids along study
area boundary
Development: Geography/Geometry
Development: Centroids/Connectors
Development: Centroids/Connectors
Calibration
1: Produce Initial Estimate
of O-D Traffic Demand
from Regional Travel
Demand Model
Travel Model
Subarea
Analysis
Seed
Matrix
Traffic
Counts
Simulation-
based DTA
Travel
Times &
Delays
Simulation-
based ODME
Good
Fit? Finished
Simulated
Traffic
Volumes
No Yes
1
Calibration
2: Simulation-based
Dynamic Traffic
Assignment to Equilibrate
Route Choices
Travel Model
Subarea
Analysis
Seed
Matrix
Traffic
Counts
Simulation-
based DTA
Travel
Times &
Delays
Simulation-
based ODME
Good
Fit? Finished
Simulated
Traffic
Volumes
No Yes
2
Calibration
3: Compare 15-min.
Simulated Volumes with
15-min. Segment and
Turning Counts
Travel Model
Subarea
Analysis
Seed
Matrix
Traffic
Counts
Simulation-
based DTA
Travel
Times &
Delays
Simulation-
based ODME
Good
Fit? Finished
Simulated
Traffic
Volumes
No Yes
3
Calibration
4: Simulation-based
Dynamic O-D Estimation
to Improve Match with
Counts
Travel Model
Subarea
Analysis
Seed
Matrix
Traffic
Counts
Simulation-
based DTA
Travel
Times &
Delays
Simulation-
based ODME
Good
Fit? Finished
Simulated
Traffic
Volumes
No Yes
4
Calibration
Iterate Steps 2-4: Repeat DTA
to “recalibrate” route choice
to the changes in demand
resultant from the ODME
step
Travel Model
Subarea
Analysis
Seed
Matrix
Traffic
Counts
Simulation-
based DTA
Travel
Times &
Delays
Simulation-
based ODME
Good
Fit? Finished
Simulated
Traffic
Volumes
No Yes
4
2
3
Validation: 15-minute INRIX Speeds
Visual comparison model
speeds with INRIX speeds to
ensure start, severity, duration
of bottlenecks
Targeted adjustment of trip
table to improve match with
bottlenecks while maintaining
goodness-of-fit with counts
Calibration
ODME (Step 4) later
extended to incorporate
observed speeds
simultaneously with counts
Travel Model
Subarea
Analysis
Seed
Matrix
Traffic
Counts
Simulation-
based DTA
Travel
Times &
Delays
Simulation-
based ODME
Good
Fit? Finished
Simulated
Traffic
Volumes
No Yes
4 15-min.
INRIX
Speeds
Visual Audit
Do route choices comport with expectations, local knowledge?
Visual Audit
Do route choices comport with expectations, local knowledge?
Query paths traversing critical link, turning
movement, or arbitrary link sequence
Query used paths by
origin and destination
Goodness of Fit: RMSE
How well does the model match observed data?
AM Peak
0600-0659 0700-0759 0800-0859 0600-0859
Collector (N=62) 93.44% 73.86% 77.52% 76.75%
Arterial (N=1,162) 52.31% 40.18% 39.39% 39.36%
Ramp (N=49) 48.81% 45.98% 45.56% 45.07%
Freeway/Expressway
(N=220) 14.06% 14.28% 14.45% 12.85%
All (N=1,493) 33.03% 29.37% 29.17% 27.70%
PM Peak
1500-1559 1600-1659 1700-1759 1500-1759
Collector (N=62) 64.79% 62.78% 70.12% 62.76%
Arterial (N=1,162) 43.44% 33.25% 37.98% 35.51%
Ramp (N=49) 41.21% 41.03% 42.48% 38.73%
Freeway/Expressway
(N=220) 14.52% 15.03% 15.89% 13.70%
All (N=1,493) 30.73% 27.06% 29.80% 26.86%
Goodness of Fit: AM Scatter Plots
How well does the model match AM counts?
y = 0.9115x - 54.279R² = 0.9475
0
2000
4000
6000
8000
10000
12000
0 2000 4000 6000 8000 10000 12000
Sim
ulat
ed C
ount
s
Field Observed Counts
y = 0.9302x - 45.489R² = 0.9375
0
2000
4000
6000
8000
10000
12000
0 2000 4000 6000 8000 10000 12000
Sim
ula
ted
Co
un
ts
Field Observed Counts
y = 0.9254x - 36.982R² = 0.9419
0
2000
4000
6000
8000
10000
12000
0 2000 4000 6000 8000 10000 12000
Sim
ula
ted
Co
un
ts
Field Observed Counts
y = 0.9383x - 221.49R² = 0.9511
0
2000
4000
6000
8000
10000
12000
0 2000 4000 6000 8000 10000 12000
Sim
ula
ted
Co
un
ts
Field Observed Counts
Goodness of Fit: PM Scatter Plots
How well does the model match PM counts?
y = 0.924x - 46.772R² = 0.939
0
2000
4000
6000
8000
10000
12000
0 2000 4000 6000 8000 10000 12000
Sim
ula
ted
Co
un
ts
Field Observed Counts
y = 0.9175x - 73.142R² = 0.9249
0
2000
4000
6000
8000
10000
12000
0 2000 4000 6000 8000 10000 12000
Sim
ula
ted
Co
un
ts
Field Observed Counts
Goodness of Fit: Spatial Pattern of Fit
What is the spatial distribution of the percent errors?
AM PM
Validation: Bottleneck Matching
How well
does the
model
reflect
critical
bottlenecks
in the study
area?
Applications
• US-60/Grand Avenue COMPASS Study
• Old Town Peoria Traffic Study
• Various analyses of traffic interchange
redesigns and other roadway
improvements
Recommended