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DynusT (Dynamic Urban Systems in Transportation)

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Presentation on theme: "DynusT (Dynamic Urban Systems in Transportation)"— Presentation transcript:

1 DynusT (Dynamic Urban Systems in Transportation)

2 Recent Projects IH corridor improvement (North Carolina, 2003-present)
IH tolling and congestion pricing (ELP, TX 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, present)

3 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)

4 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

5 What is Mesoscopic Traffic Simulation?
Not as detailed as microscopic models, but is as capable of high-fidelity traffic simulation of an entire region

6 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

7 How Trip-makers Adapt to Congestion

8 Macro-Meso-Micro Integration
Analyze Ingress/Egress points for weaving Meso Micro Macro Estimate toll lane usage and revenue Proposed toll lanes

9 Macro-Meso-Micro Integration
Travel Demand Models (TDM) Micro e.g. VISSIM

10 Visualizing the Model’s Results - An Example

11 Applying toll on 495 ramp may improve traffic on both 495W and 95S
I-95S AM commuting traffic impacted by spillback from 495W

12 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

13 Peak Spreading Due to Value Pricing
Change of departure time due to pricing

14 A Regional View (DynusT Animation)

15 A Closer View (VISSIM animation)

16 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

17 A Low-Hanging Fruit Strategy – Optimize Signal

18 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

19 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, ,500 hours; including learning

20 How to Get Started Capacity building
Training workshop – agency and consultants Establish baseline and future datasets Allow 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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