Presentation on theme: "Project Prioritization - 1 Project Prioritization Using Paramics Microsimulation: A Case Study for the Alameda County Central Freeway Project Presented."— Presentation transcript:
Project Prioritization - 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.
Project Prioritization - 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
Project Prioritization - 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
Project Prioritization - 4 Project Location San Francisco Bay Area Alameda County
Project Prioritization - 5 Vicinity Map
Project Prioritization - 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
Project Prioritization - 7 Screenshot of Original Model
Project Prioritization - 8 Screenshot of Modified/Expanded Model
Project Prioritization - 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
Project Prioritization - 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
Project Prioritization - 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
Project Prioritization - 13 Existing Speed Data
Project Prioritization - 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.
Project Prioritization - 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
Project Prioritization - 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
Project Prioritization - 19 ACCMA Model Analysis Sub-Area
Project Prioritization - 20 ACCMA Travel Demand Model ODME Additional Adjustments to Matrix
Project Prioritization 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)
Project Prioritization - 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)
Project Prioritization - 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
Project Prioritization - 24 Project Elements Matrix
Project Prioritization - 25 Project Elements Matrix
Project Prioritization - 26 ACCMA Demand Model Analysis Baseline (No Project) Model Results
Project Prioritization - 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
Project Prioritization - 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
Project Prioritization - 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.
Project Prioritization - 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.
Project Prioritization - 36 Challenges of Paramics Model Freeway Exit/Lane Choice
Project Prioritization - 37 Other Challenges Arterial Network Time Consuming to “make it work”
Project Prioritization - 38 Something to Consider… Carefully Plan Out Network Coding Recognize Existing Bottleneck Location When Laying out Nodes-Links Consider using Feedback or Dynamic Assignment
Project Prioritization - 39 Questions and Contact Info Questions