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Project Prioritization Using Paramics Microsimulation: A Case Study for the Alameda County Central Freeway Project

Project Prioritization Using Paramics Microsimulation: A Case Study for the Alameda County Central Freeway Project. Presented by: Kevin Chen Project Completed by: Marty Beene/Allen Huang Dowling Associates, Inc. Introduction & Objective.

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Project Prioritization Using Paramics Microsimulation: A Case Study for the Alameda County Central Freeway Project

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  1. Project Prioritization Using Paramics Microsimulation:A Case Study for the Alameda County Central Freeway Project Presented by: Kevin Chen Project Completed by: Marty Beene/Allen Huang Dowling Associates, Inc.

  2. Introduction & Objective • Project Sponsored by Alameda County Congestion Management Agency (ACCMA), California. • Subcontracted to Kimley-Horn and Associates, Inc. (Civil) • Study Area Includes Five Local Cities: Union City, Hayward, San Leandro, Castro Valley, and Oakland • Objectives: • Use Traffic Analysis Tools to Evaluate Various Combination of Project Alternatives • Provide Recommendation on Project Priorities based on Analysis Results

  3. Backgrounds • Project Location: San Francisco Bay Area (East Bay Area), California • Cities Included: Union City, Hayward, Castro Valley, San Leandro, Oakland • Study Includes 3 Interstate Freeways: • I-880, I-238, and I-580 • I-880 is the Central Corridor • Total Study Length is Approximately 15 miles, including 15 interchanges

  4. Project Location San Francisco Bay Area Alameda County

  5. Vicinity Map

  6. Project Model Background • Why Use Paramics • Systemetrics Established Area-Wide Paramics Model for Corridor System Management Plan • Caltrans Headquarter Compliance • Paramics Model Developed using Version 5.2 • University of California at Irvine Developed Plug-ins for Caltrans HOV, Ramp Metering (Occupancy Based), and Data Collection

  7. Screenshot of Original Model

  8. Screenshot of Modified/Expanded Model

  9. General Study Approach • Traditional Microsimulation in Conjunction with Travel Demand Model • Utilized Alameda Countywide Travel Demand Model to Produce forecast - Cube Based • Evaluated AM and PM peak hours • Provide Recommendation on Project Priorities based on Analysis Results – From both Demand Model and Microsimulation Model

  10. Specific Methodology • Expand and Modified Original Paramics Networks • Produced Trip Tables from Regional Travel Demand Model (Base and Future Years) • Extracted Sub-Area Networks • Produced OD Matrices from Demand Model • Applied OD to Paramics Model • Calibrated and Validated Base Year Paramics Model • Created and Simulated Future Baseline and Project Specific Paramics Models

  11. Methodology Flow Chart Exhibit 2: Traditional Approach Flow Chart

  12. Existing Data Collection • Existing Mainline and Ramp Counts from Automated Stations at 14 Locations – PEMS Data, UC Berkeley & Caltrans • Ramp Counts Reconciled with Ramp Intersection Counts – Checked for Consistency and Continuity • Travel Speed Obtained from • Floating Car Survey during the Peak Hours

  13. Existing Speed Data

  14. Model Parameters • Caltrans Vehicle File • Developed by UCI • Agreed by Caltrans Headquarter • Ramp Metering • Mainline Occupancy Detection • Link Categories • Link Types Defined in Setting Original Model • Headway Factor, etc.

  15. Calibration Results 1 • Volume Calibration

  16. Calibration Results 2 • Selective Link Volume Comparison

  17. Calibration Results 3 • Speeds • We referred to Wisconsin DOT’s Microsimulation Guideline • Severe Congestion at the I-880/I-238, and I-238/I-580 Junctions – wide range of speed variation resulting calibration difficulties • AM Model: 10/16, PM Model: 15/16 Segments Matched • In addition - we checked animation output of the bottlenecks and queues

  18. Project Analysis • Used Countywide Regional Model to Evaluate: • ACCMA Model • 2015 • 2035 • Used Paramics Microsimulation to Evaluate: • 2015 • Compared Project Scenarios to Future Baseline

  19. ACCMA Model • Analysis Sub-Area

  20. ACCMA Travel Demand Model • ODME • Additional Adjustments to Matrix

  21. 2015 Baseline • Baseline (No Project) Included ten Projects: • Arterial Extensions • Interchange Improvements • I-238 Widening Project • I-580 Redwood Interchange Improvements • I-880/SR-92 Interchange Improvements • I-880 Southbound HOV Lane from Hegenberger to Marina (Oakland Airport Vicinity)

  22. Project Elements • List of Project Elements • Widen NB 238 to NB 880 Connector to 2 lanes • Reconstruct Washington, Lewelling Interchange Connections and Widen Over/Under crossings • Extend NB 880 HOV Lane from Hacienda to Hegenberger • Add Aux. Lane to each Direction of I-880 between Winton and A Street • Add NB off-ramp at Industrial (currently on-ramp only)

  23. Project Elements (2) • List of Project Elements • Add Auxiliary Lane Between Whipple and Industrial Road in Both Directions • Improve Whipple Interchange to Enhance Truck Movement • Reconstruct Davis Interchange • Reconstruct Marina Interchange • Reconstruct Winton Interchange • Extend WB 580 off-ramp over Strobridge to Connect to Castro Valley Blvd

  24. Project Elements Matrix

  25. Project Elements Matrix

  26. ACCMA Demand Model Analysis • Baseline (No Project) Model Results

  27. ACCMA Demand Model Analysis • Diversions

  28. Paramics Model Results • Alternative Packages were Compared to Future Baseline Scenario • Measures of Effectiveness (MOE): • Productivity – Volume Throughput • Mobility – Travel Time (reverse of speed) • Results Gathered Using UCI Developed Plug in

  29. Productivity MOE • I-880 SB – AM Peak

  30. Productivity MOE • I-880 NB – AM Peak

  31. Mobility MOE • Travel Time – AM Peak

  32. Mobility MOE • Travel Time – PM Peak

  33. Other Project Activities • Project Further Evaluated with Refined Alternative on a different date • Provided Paramics and Demand Model MOEs • Other Considerations: • Construction Cost • ROW • Environmental Impacts • Construction Feasibility

  34. Pros of Traditional Approach • This case study demonstrated the benefit of combining a simulation model with a demand model to evaluate the benefits of a freeway improvement project. • Helped the agency to prioritize the funding sequence of all project scenarios. • The simulation model results showed that some systemwide benefits of certain project scenarios were off-set by the increased volumes. Thus, the overall travel time saving was less than the agency’s presumption.

  35. Cons of Traditional Approach • Labor Intensive in OD Adjustments for Larger Networks • The traditional approach (adjusting the demand outside of the demand model) is feasible to perform manually (with the assistance of a spreadsheet) for small microsimulation study areas employing no more than 50 origin and destination zones. This approach becomes too laborious for larger study areas. Larger microsimulation study areas would require greater automation of the post-demand model adjustment process.

  36. Challenges of Paramics Model • Freeway Exit/Lane Choice

  37. Other Challenges • Arterial Network Time Consuming to “make it work”

  38. Something to Consider… • Carefully Plan Out Network Coding • Recognize Existing Bottleneck Location When Laying out Nodes-Links • Consider using Feedback or Dynamic Assignment

  39. Questions and Contact Info • Questions

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