1. Jim Henricksen, MnDOT Steve Ruegg, WSP
Development and Application of a Regional DTA model for the Twin Cities
Jim Henricksen, MnDOT
Steve Ruegg, WSP

2. Presentation Outline Need and Motivation for a DTA model
Regional Modeling Background
DTA Model Development
Model Calibration
Application
ABM to DTA development
Lessons Learned

3. Need and Motivation
Several Major Re-Construction Projects in the region anticipated within the next few years.
I35W/Lake Street
TH169
I94
I35W River Bridge

4. Need and Motivation
With so many major construction projects being done during the same time period, it is not obvious what the traffic impacts will be
Therefore, a model that can estimate traffic flow changes over the entire system is needed to evaluate impacts and select the optimum construction phasing.

5. Regional Modeling Background
Traditional 4-step trip-based model covers 20-county area
1,601 zones
25,000 links
11,200 nodes
Activity-based model covers 19-county area
3,030 zones
55,800 links
23,800 nodes

6. DTA Model Development Preparation of Demand Matrices
Synthetic Matrix Estimation adjustments done in regional model
Vehicle Classes: SOV, HOV, Truck
Hourly to 15 min demand conversion
Custom Java program to convert CUBE matrix to DynusT input format
Preparation of Network
Import planning network to DynusT format
Deletion of centroid connectors and designation of access/egress nodes
Network checking and manual corrections

7. DTA Model Development
Simplified approach to toll modeling for 4-step model
Allow toll-eligible trips
Prohibitive tolls for SOVs and Trucks
Signal Timing
Initial unconstrained assignment was conducted
DynuStudio tool was used to calculate default signal timing parameters
Full DTA model run, with feedback, was then run to convergence

8. Model Calibration: Observed Data
MnDOT detector data – 15 minute counts
Tuesday, Wednesday and Thursday for October 2014
Speed Data
TomTom data for arterials -- average speed over a 2-hour time period
Detector data in 15 min intervals on freeways

9. Model Calibration: Flow Model Calibration

10. Model Calibration: Fit to Counts

11. Link Average Speeds -- Freeways

12. Link Average Speeds -- Arterials

13. Model Calibration: Before and After Randomized Departure Times
Initial Departure Profile
Final Departure Profile

14. Model Application Construction Impacts Planning Analysis
I35W/Lake Street – 4 construction alternatives with 2 phases
TH169 – Bridge closure impacts, subarea analysis to local roads
I94 – Impacts with and without TH169 closure
Planning Analysis
Impact of proposed Managed Lane flows at termini

15. Model Application -- Reporting
Flow Difference Plots
MOE’s:
Vehicle Delay by range of delay/vehicle
Vehicle-Hours
Vehicle-Miles
Selected Link statistics:
Average trip length
Ramp to Ramp flows for simulation input
Change in demand on parallel routes

16. Road User Cost Assumptions
Type of Vehicle
User Cost per Hour
User Cost per Mile
Passenger vehicle
$16.00
$0.31
Commercial vehicle
$27.30
$0.96
Region*
$16.74
$0.35
Road User Costs by Scenario
Scenario
Daily User Cost
Days of Construction
Total User
Cost 1.
YR 1: One lane closed,bridge open
YR 2: One lane closed, Bridge open, construction on I-35W and I-94
YR 1: $195,500
YR 2: $450,200
335
215
550
$65,492,500
$96,763,000
162,285,500
2. One lane closed, bridge closed
$382,000
$127,970,000
3. Directional closures
$342,500
$188,375,000
RUC Assumptions are Taken from benefit/Cost Analysis Process.
RUCs are balanced against Construction Costs, which is the case of US 169, I significant cost savings was achieved by not maintaining bridge access during construction.

17. Model Application: Heat Maps to Identify Queues
US 10/US 61 On Ramp
Mounds/Kellogg Blvd Off Ramp
6th St/US 52 S Off Ramp
Mounds/Kellogg Blvd On Ramp
US 52 On Ramp
94W to 35E Ramp
E 12th St Off Ramp
E 12th St On Ramp
35E to 94 W Ramp
Commons
Marion St. Off Ramp
W Kellogg Blvd On Ramp
Dale St. Off Ramp AM PM

18. Model Application: Speed Profile by Time

19. ABM to DTA – Demand Data
Twin Cities ABM Model will be used on most, if not all new planning analysis
Trip Records instead of trip tables – allows for more detailed time stratification
Cube Application developed to convert ABM to DynusT format trip records
Class stratification – SOV, HOV, Truck – others for VOT stratification?

20. ABM-compatible Network
Used True Shape network
Detailed network editing and error correction – node orientation
Used parcel-level data to identify access nodes and shares by zone
Implement Managed Lane Algorithm for tolling
Intersection control coding, aided by initial assignment

21. Lessons Learned Subarea vs. Regional model application
Use of DTA travel times to inform mode share
Extracting ramp to ramp movements and other details of mining the results
Considerable time to clean networks in DTA
ABM-type detailed, discrete demand is more compatible with DTA

22. Potential for Future Applications
Independent estimate for dynamically-tolled lanes
Operational analysis -- queuing
“Real Time” construction management
Feedback to ABM: times and demand?
Input to simulation

23. Contact(s) Steve Ruegg ,PE Senior Technical Principal
Systems Analysis Group

24. QUESTIONS?