dynust dynamic urban systems in transportation
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DynusT (Dynamic Urban Systems in Transportation). Recent Projects. IH corridor improvement (North Carolina, 2003-present) IH tolling and congestion pricing (ELP, TX-2003-present) IH work zone planning (ELP, TX-2004)

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Presentation Transcript
recent projects
Recent Projects

IH corridor improvement (North Carolina, 2003-present)

IH tolling and congestion pricing (ELP, TX-2003-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, 2006-present)

on going efforts
On-going Efforts

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
What DynusT Represents?

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

Enabled by

Mesoscopic Traffic Simulation

Dynamic Traffic Assignment (DTA)

Micro-meso-micro integration

what is mesoscopic traffic simulation
What is Mesoscopic Traffic Simulation?

Not as detailed as microscopic models, but is as capable of high-fidelity traffic simulation of an entire region

what is dynamic traffic assignment
What is Dynamic Traffic Assignment?

A method to predict/estimate how trip-makers may shift to other routes or departure time in response to:

Congestion

Pricing

Controls

Incidents

Improvements

Understand how individual travel decisions impact an entire region, by

Time of day

Origin-Destination (OD) zones

Transportation modes

macro meso micro integration
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 integration9
Macro-Meso-Micro Integration

Macro

Travel Demand Models (TDM)

Micro

e.g. VISSIM

slide11
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
A “What-if” Pricing Scheme

Variable toll on I-95 S to I-495 W ramp

Toll increases with congestion level

Morning peak period (5AM - 11AM)

LOV 89%

HOV 11%

Value of Time: $10/hour

Pricing

Distance based tolls: $0.25 /mile

$2 for through traffic

Peak-period tolls: 7AM - 9AM

Dimensions of impacts

Departure time

Route

Both

peak spreading due to value pricing
Peak Spreading Due to Value Pricing

Change of departure time due to pricing

addressing diversion
Addressing Diversion

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

Signal optimization, information provision, transit operation, peak spreading

other freeway operations scenarios strategies
Other Freeway Operations Scenarios/Strategies

Dynamic message signs

Congestion warning

Mandatory detour

Speed advisory

Information strategies

Pre-trip information

In-vehicle information

Incident

Work zone

Managed lanes

Truck only

Truck restriction

resource considerations
Resource Considerations

Initial TDM import and conversion

100+ hrs

Data collection and model calibration

300+ hrs

Scenario analysis and reporting

400+ hrs

Total man-hours

800+

Budget 1,000 - 1,500 hours; including learning

how to get started
How to Get Started

Capacity building

Training 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

Lesson learned from Minneapolis collapse

Plan ahead and get the model built

We are ready to act when needs arise

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