Add analysis of operations and traffic simulation modeling to the set of

Transcription

1 D l t and d Development Application of a Traffic Simulation and dd Dynamic y i Traffic Assignment Model Framework f Centrall Phoenix h i for

2 Purpose Add analysis of operations and traffic simulation modeling to the set of services MAG offers to its member agencies p g Build a model to complement MAG s regional travel demand model that: Has the operational sensitivity to capture effects of signal operations operations, ITS projects IIs able bl tto capture t the th mobility bilit b benefits fit off major j projects j t whose h iimpacts t will ill b be f lt th felt throughout h t Central C t l Phoenix Ph i Accurately portrays the traffic impacts of transit improvements, namely on highcapacity transit corridors Provides a calibrated base model from which smaller, more focused studies can be derived

3 Approach Study Design Stage Solicit stakeholder input/support Scope the model framework, framework design parameters, parameters and geographic scope M d l Data D t Preparation P ti Model Assemble traffic count and signal g timing g data Develop simulation model network and relationship to travel demand moel Framework Development and Testing Test, calibrate, and validate the model T i i Training

4 Design A model congruous with the regional travel demand model Objective: To achieve a degree of integration with the regional travel demand model such that they can share key model data seamlessly Solution: A simulation model in TransModeler capable of reading all file formats and data structures of the regional g model in TransCAD and sharing g a common zonal system y (and, ( hence, readyy exchange g of origin-destination g matrices)) A multi-resolution multi resolution traffic simulation model Objective: A simulation model with an appropriate balance of high-fidelity treatment of traffic flow phenomena and practical computational performance Solution: A microsimulation model enabling of lowerg selective application pp g meso)) and multi-resolution ((e.g., g hybrid y resolution ((e.g., micro-meso)) models

5 Development 1. 1 Preparation of highly detailed lane-level lane level geography/geometry 2. Import of centroids p and connectors from regional g model 3 3. Auto-adjustment Auto adjustment of TAZ connectivity 4. Manual addition of centroids along study area boundary

6 Development 1. 1 Preparation of highly detailed lane-level lane level geography/geometry 2. Import of centroids p and connectors from regional g model 3 3. Auto-adjustment Auto adjustment of TAZ connectivity 4. Manual addition of centroids along study area boundary

7 Development 1. 1 Preparation of highly detailed lane-level lane level geography/geometry 2. Import of centroids p and connectors from regional g model 3 3. Auto-adjustment Auto adjustment of TAZ connectivity 4. Manual addition of centroids along study area boundary

8 Development 1. 1 Preparation of highly detailed lane-level lane level geography/geometry 2. Import of centroids p and connectors from regional g model 3 3. Auto-adjustment Auto adjustment of TAZ connectivity 4. Manual addition of centroids along study area boundary

9 Development 1. 1 Preparation of highly detailed lane-level lane level geography/geometry 2. Import of centroids p and connectors from regional g model 3 3. Auto-adjustment Auto adjustment of TAZ connectivity 4. Manual addition of centroids along study area boundary

10 Development 1. 1 Preparation of highly detailed lane-level lane level geography/geometry 2. Import of centroids p and connectors from regional g model 3 3. Auto-adjustment Auto adjustment of TAZ connectivity 4. Manual addition of centroids along study area boundary

11 Calibration 1 Step 1: Produce Initial Estimate of O-D Traffic Demand from Regional Travel Demand Model Travel Model Subarea Analysis Seed Matrix Dynamic Traffic Assignment Simulated Historical T Travel l Times Ti & Delays S u at o based Dynamic Matrix Adj t Adjustment t Satisfactory S ti f t Match? No Yes Finished

12 Calibration Travel Model Subarea Analysis Step 2: Dynamic Traffic Assignment to Equilibrate Route Choices Seed Matrix 2 Dynamic Traffic Assignment Simulated Historical T Travel l Times Ti & Delays S u at o based Dynamic Matrix Adj t Adjustment t Satisfactory S ti f t Match? No Yes Finished

13 Calibration Travel Model Subarea Analysis Step 3: Compare 15-min. Simulated Volumes with 15-min. Segment and Turning Counts Seed Matrix Dynamic Traffic Assignment Historical T Travel l Times Ti & Delays Simulated S u at o based Dynamic Matrix Adj t Adjustment t 3 No Satisfactory S ti f t Match? Yes Finished

14 Calibration Travel Model Subarea Analysis Step 4: Dynamic O-D Estimation to Improve Match with Counts Seed Matrix Dynamic Traffic Assignment 4 Simulated Historical T Travel l Times Ti & Delays S u at o based Dynamic Matrix Adj t Adjustment t Satisfactory S ti f t Match? No Yes Finished

15 Calibration Travel Model Subarea Analysis Iterate Steps 2-4: Repeat DTA to recalibrate route choice to the changes in demand resultant from the ODME step Seed Matrix 2 Dynamic Traffic Assignment 4 Historical T Travel l Times Ti & Delays Simulated S u at o based Dynamic Matrix Adj t Adjustment t 3 No Satisfactory S ti f t Match? Yes Finished

16 Validation Travel Model Subarea Analysis Visual comparison of 15-min. speed maps with 15-min. INRIX maps to ensure start, severity, duration of bottlenecks Targeted adjustment of trip table toseed improve Matrix match with bottlenecks while maintaining goodness-of-fit with counts 2 Dynamic Traffic Assignment 4 Historical T Travel l Times Ti & Delays Simulated S u at o based Dynamic Matrix Adj t Adjustment t 3 No Satisfactory S ti f t Match? Yes Finished

17 Validation Travel Model Subarea Analysis On-going, independent research effort to incorporate INRIX speed data into the Dynamic ODME in Step 4 Seed Matrix 2 Dynamic Traffic Assignment 4 15-min. 5 INRIX Speeds Simulated S u at o based Dynamic Matrix Adj t Adjustment t Historical T Travel l Times Ti & Delays 3 No Satisfactory S ti f t Match? Yes Finished

18 Visual Audit Do route choices comport with expectations local knowledge? expectations,

19 Visual Audit Do route choices comport with expectations local knowledge? expectations, Query paths traversing critical link, turning movement, or arbitrary link sequence

20 Visual Audit Do route choices comport with expectations local knowledge? expectations, Query paths traversing critical link, turning movement, or arbitrary link sequence Query used paths by origin i i and dd destination ti ti

21 Goodness of Fit How well do simulated volumes match the field data? In %RMSE: All Counts Freeway y and Ramp All Counts Freeway y and Ramp Observations 1, Observations 1, :00 7:00 AM 37.8% 20.4% 3:00 4:00 PM 31.9% 17.7% 7:00 8:00 AM 31.9% 18.0% 4:00 5:00 PM 29.6% 18.7% 8:00 9:00 AM 30 7% 30.7% 18 0% 18.0% 5:00 6:00 PM 34 4% 34.4% 21 0% 21.0% 6:00 9:00 AM 29.25% 15.7% 3:00 6:00 PM 28.8% 16.7% AM Period PM Period

22 Applications / US-60/Grand Avenue COMPASS Studyy Old Town Peoria Traffic Study y g Various analyses of traffic interchange redesigns g and other roadwayy improvements p