Transcript Slide 1

DynusT
(Dynamic Urban Systems in
Transportation)
Recent Projects
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IH corridor improvement (North Carolina,
2003-present)
IH tolling and congestion pricing (ELP, TX2003-present)
IH work zone planning (ELP, TX-2004)
Evacuation operational Planning (HOU, TX,
2007, Baltimore, MD, 2005, Knoxville, TN,
2003)
Downtown improvement (ELP, TX, 2004)
ICM AMS modeling (Bay Area, CA, 2006present)
On-going Efforts
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Military deployment transportation improvement in
Guam (PB, FHWA)
Interstate highway corridor improvement (TTI,
TxDOT, ELP MPO)
Value pricing (ORNL, FHWA; SRF, Mn/DOT, TTI,
TxDOT)
Evacuation operational planning (UA, ADOT; LSU,
LDOT; Noblis, FHWA; Univ. of Toronto)
Integrated Corridor Management modeling (CS,
FHWA)
Bay area regional modeling (CS, MTC)
Florida turnpike system traffic and evacuation
analysis (FDOT Turnpike)
What DynusT Represents?
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Regional Operational Planning Capability
 Regional
- area larger than corridor
 Operational - traffic flow dynamics sensitive to signals,
road configurations
 Planning - short-term impact and long-term equilibrium
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Enabled by
 Mesoscopic
Traffic Simulation
 Dynamic Traffic Assignment (DTA)
 Micro-meso-micro integration
What is Mesoscopic Traffic
Simulation?
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Not as detailed as microscopic models, but is as
capable of high-fidelity traffic simulation of an
entire region
What is Dynamic Traffic
Assignment?
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A method to predict/estimate how trip-makers may shift to
other routes or departure time in response to:
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Congestion
Pricing
Controls
Incidents
Improvements
Understand how individual travel decisions impact an entire
region, by
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Time of day
Origin-Destination (OD) zones
Transportation modes
How Trip-makers Adapt to Congestion
Macro-Meso-Micro Integration
Macro
Meso
Micro
Proposed toll lanes
Analyze
Ingress/Egress
points for weaving
Estimate toll lane
usage and revenue
Macro-Meso-Micro
Integration
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Macro
 Travel
Demand
Models (TDM)
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Micro
 e.g.
VISSIM
Visualizing the Model’s Results
- An Example
Applying toll on 495
ramp may improve
traffic on both 495W
and 95S
I-95S AM commuting
traffic impacted by
spillback from 495W
A “What-if” Pricing Scheme
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Variable toll on I-95 S to I-495 W ramp
Toll increases with congestion level
Morning peak period (5AM - 11AM)
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Value of Time: $10/hour
Pricing
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LOV 89%
HOV 11%
Distance based tolls: $0.25 /mile
$2 for through traffic
Peak-period tolls: 7AM - 9AM
Dimensions of impacts
 Departure time
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Route
Both
Peak Spreading Due to Value Pricing
Change of departure time due to pricing
Base Case
12000
Peak Spreading
10000
8000
6000
4000
2000
Tim e
10:30
10:00
9:30
9:00
8:30
8:00
7:30
7:00
6:30
6:00
5:30
0
5:00
# of Vehicles Generated
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A Regional View (DynusT Animation)
A Closer View (VISSIM animation)
Addressing Diversion
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Tolling may cause diversion on alternative routes and/or other
transportation modes
Turnkey solution package needed to improve the capacity to
which the traffic may be diverted
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Signal optimization, information provision, transit operation, peak
spreading
A Low-Hanging Fruit Strategy –
Optimize Signal
Other Freeway Operations
Scenarios/Strategies
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Dynamic message signs
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Information strategies
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Congestion warning
Mandatory detour
Speed advisory
Pre-trip information
In-vehicle information
Incident
Work zone
Managed lanes
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Truck only
Truck restriction
Resource Considerations
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Initial TDM import and conversion
 100+
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Data collection and model calibration
 300+
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hrs
Scenario analysis and reporting
 400+
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hrs
hrs
Total man-hours
 800+
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Budget 1,000 - 1,500 hours; including learning
How to Get Started
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Capacity building
 Training
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workshop – agency and consultants
Establish baseline and future datasets
 Allow
12-18 months with sufficient budget
 Will be a valuable asset for many future applications
 Save $$$ for agency in the long-run
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Lesson learned from Minneapolis collapse
 Plan
ahead and get the model built
 We are ready to act when needs arise