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

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Project Prioritization Using Paramics
MicrosimulationA Case Study for the Alameda
County Central Freeway Project

• Presented by Kevin Chen
• Project Completed by Marty Beene/Allen Huang
• Dowling Associates, Inc.

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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

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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

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Project Location

• San Francisco Bay Area
• Alameda County

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Vicinity Map
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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

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Screenshot of Original Model
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Screenshot of Modified/Expanded Model
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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

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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

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Methodology Flow Chart
Exhibit 2 Traditional Approach Flow Chart
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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

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Existing Speed Data
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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.

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Calibration Results 1

• Volume Calibration

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Calibration Results 2

• Selective Link Volume Comparison

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Calibration Results 3

• Speeds
• We referred to Wisconsin DOTs 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

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Project Analysis

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

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ACCMA Model

• Analysis Sub-Area

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ACCMA Travel Demand Model

• ODME
• Additional Adjustments to Matrix

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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)

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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)

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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

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Project Elements Matrix
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Project Elements Matrix
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ACCMA Demand Model Analysis

• Baseline (No Project) Model Results

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ACCMA Demand Model Analysis

• Diversions

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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

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Productivity MOE

• I-880 SB AM Peak

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Productivity MOE

• I-880 NB AM Peak

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Mobility MOE

• Travel Time AM Peak

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Mobility MOE

• Travel Time PM Peak

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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

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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
  agencys presumption.

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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.

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Challenges of Paramics Model

• Freeway Exit/Lane Choice

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Other Challenges

• Arterial Network Time Consuming to make it work

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Something to Consider

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

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Questions and Contact Info

• Questions