DynusT (Dynamic Urban Systems in Transportation)

1. DynusT (Dynamic Urban Systems in Transportation)

2. 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)

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

9. Macro-Meso-Micro Integration Macro 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 1,000 - 1,500 hours; including learning

20. 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