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Microsimulation for Rural and Exurban Regions: Lake County, California

Microsimulation for Rural and Exurban Regions: Lake County, California. David Gerstle (presenting) & Zheng Wei Caliper Corporation. Executive Summary. Microsimulation is an important tool for modeling exurban and rural areas Congestion is often not an important driver of travel times

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Microsimulation for Rural and Exurban Regions: Lake County, California

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  1. Microsimulation for Rural and Exurban Regions:Lake County, California David Gerstle (presenting) & ZhengWei Caliper Corporation

  2. Executive Summary • Microsimulation is an important tool for modeling exurban and rural areas • Congestion is often not an important driver of travel times • “Minutia” such as grade, curvature, and lane widths are vitally important • Shown using a case-study of Lake County, California using Caliper’s TransModelermicrosimulation software: • Show how we calibrated & validated the model • Show failure to validate absent grade, curvature, lane widths, etc.

  3. Outline • Project Background • Model Scope • Model Preparation • Model Minutia

  4. Outline • Project Background • Model Scope • Model Preparation • Model Minutia

  5. Project Background • Lake County Area Microsimulation Model (LAMM) • To develop a traffic simulation model that: • Supports planning and operational analysis • Focuses on SR-20, SR-53, and SR-29 and the communities surrounding Clear Lake • Extends and complements existing models and modeling activities • To evaluate future-year scenarios

  6. Project Background • Lake County Area Microsimulation Model (LAMM) • To develop a traffic simulation model that: • Supports planning and operational analysis • Focuses on SR-20, SR-53, and SR-29 and the communities surrounding Clear Lake • Extends and complements existing models and modeling activities • To evaluate future-year scenarios

  7. Lake County

  8. Lake County • Approx. 2 hr. drive from SFO to southern Lake County

  9. Lake County Lake County

  10. Project Background • Lake County Area Microsimulation Model (LAMM) • To develop a traffic simulation model that: • Supports planning and operational analysis • Focuses on SR-20, SR-53, and SR-29 and the communities surrounding Clear Lake • Extends and complements existing models and modeling activities • To evaluate future-year scenarios

  11. Dominant route for through traffic passes through populated areas

  12. Outline • Project Background • Model Scope • Model Preparation • Model Minutia

  13. Outline • Project Background • Model Scope • Geography • Time Periods & Vehicle Population • Model Preparation • Model Minutia

  14. Lake County Study Area

  15. Lake County • 450 square miles of Lake County, from Middletown (Napa border) to Upper Lake (Mendocino border) Study Area

  16. Lake County Level of Detail

  17. Lake County • 720 miles of roadway (120 miles on State Routes) • 4,200 Links and 3,300 Nodes • All roads in the regional travel demand model are included Level of Detail

  18. Lake County Nice • High level of detail for local streets Level of Detail

  19. Lake County • Intersection geometry accurately reproduced Level of Detail

  20. Outline • Project Background • Model Scope • Geography • Time Periods & Vehicle Population • Model Preparation • Model Minutia

  21. Time Periods & Vehicle Population • Times of day include two peak periods • 6:00 – 9:00 AM • 3:00 – 6:00 PM • Vehicle Population • Auto • Truck

  22. Time Periods & Vehicle Population • Times of day include two peak periods • 6:00 – 9:00 AM • 3:00 – 6:00 PM • Vehicle Population • Auto • Truck

  23. Outline • Project Background • Model Scope • Model Preparation • Data Collection • Model Calibration • Model Validation • Model Minutia

  24. Outline • Project Background • Model Scope • Model Preparation • Data Collection • Model Calibration • Model Validation • Model Minutia

  25. Data Collection • GPS-recorded travel times • O-D surveys • Turning movement counts • Directional counts

  26. Data Collection GPS Travel Times O-D Survey Sites (5) Turning Movement (20) Directional Counts (26)

  27. Data Collection GPS Travel Times O-D Survey Sites (5) Turning Movement (20) Directional Counts (26)

  28. Data Collection GPS Travel Times O-D Survey Sites (5) Turning Movement (20) Directional Counts (26)

  29. Data Collection GPS Travel Times O-D Survey Sites (5) Turning Movement (20) Directional Counts (26)

  30. Outline • Project Background • Model Scope • Model Preparation • Data Collection • Model Calibration • Model Validation • Model Minutia

  31. Model Calibration • Take the calibrated travel demand model as the starting point • Iteratively cycle between • Trying to match turn & directional counts • Trying to equilibrate route choices • Target traffic count calibration standards set by Caltrans

  32. Model Calibration Calibrated Travel Demand Model • Take the calibrated travel demand model as the starting point • Iteratively cycle between • Trying to match turn & directional counts • Trying to equilibrate route choices • Target traffic count calibration standards set by Caltrans Match Counts (ODME) Match Times (DTA) Calibrated Micro-simulation Model

  33. Model Calibration • Take the calibrated travel demand model as the starting point • Iteratively cycle between • Trying to match turn & directional counts • Trying to equilibrate route choices • Target traffic count calibration standards set by Caltrans

  34. Outline • Project Background • Model Scope • Model Preparation • Data Collection • Model Calibration • Model Validation • Model Minutia

  35. Model Validation • Take the calibrated traffic simulation model as the starting point • Iteratively cycle between • Trying to match point-to-point travel times • Reviewing/revisiting model development and calibration steps • Target travel time calibration standards set by Caltrans

  36. Model Validation Calibrated Traffic Simulation Model • Take the calibrated traffic simulation model as the starting point • Iteratively cycle between • Trying to match point-to-point travel times • Reviewing/revisiting model development and calibration steps • Target travel time calibration standards set by Caltrans Revisit Calibration (ODME/DTA) Match Times (Simulation) Validated Micro-simulation Model

  37. Model Validation Point-to-Point Travel Times Boundary Upper Lake Lucerne SR-53 Kelseyville Lower Lake Middletown

  38. Model Validation AM Southbound Travel Times GPS: 6.63 min TSM: 6.82 min : 2.9% GPS: 15.95 min TSM: 15.89 min : 0.36% Boundary GPS: 13.35 min TSM: 15.03 min : 11.82% Upper Lake Lucerne SR-53 GPS: 15.92 min TSM: 16.17 min : 1.61% Kelseyville GPS: 11.29 min TSM: 8.95 min : 23.07% GPS: 16.92 min TSM: 17.79 min : 5.01% Lower Lake GPS: 20.25 min TSM: 21.63 min : 6.85% Middletown

  39. Model Validation AM Northbound Travel Times GPS: 6.48 min TSM: 6.97 min :7.21% GPS:15.65 min TSM: 16.53 min :5.49% Boundary GPS: 13.87 min TSM: 14.87 min :6.99% Upper Lake Lucerne SR-53 GPS: 15.22 min TSM: 16.56 min : 10.56% Kelseyville GPS: 13.12 min TSM: 8.93 min :37.98% GPS: 17.52min TSM: 18.21 min : 3.84% Lower Lake GPS: 20.53 min TSM: 21.86 min :6.31% Middletown

  40. Model Validation PM Southbound Travel Times GPS: 6.24 min TSM: 6.96 min :10.92% GPS: 15.58 min TSM: 16.38 min : 5.01% Boundary GPS: 13.51 min TSM: 15.34 min :12.73% Upper Lake Lucerne SR-53 GPS: 16.03 min TSM: 16.61 min :3.56% Kelseyville GPS: 10.66 min TSM: 9.30min :13.64% GPS: 16.86min TSM: 17.95 min :6.26% Lower Lake GPS: 20.52 min TSM: 21.62 min :5.21% Middletown

  41. Model Validation PM Northbound Travel Times GPS: 6.55 min TSM: 6.99 min : 6.45% GPS: 15.02 min TSM: 16.58 min :9.88% Boundary GPS: 13.12 min TSM: 15.03 min :13.53% Upper Lake Lucerne SR-53 GPS: 17.31 min TSM: 16.54 min : 4.57% Kelseyville GPS: 10.14 min TSM: 9.21 min :9.60% GPS: 16.82 min TSM: 18.55 min :9.81% Lower Lake GPS: 19.96 min TSM: 22.42 min :11.60% Middletown

  42. Model Validation

  43. Model Validation Lower and Upper Bounds calculated by bootstrapping sample

  44. Model Validation Create bootstrapped sample of the set of simulation runs For each run in bootstrapped sample, create bootstrapped sample of point-to-point travel times Calculate expected travel time for each simulation run Lower and Upper Bounds calculated by bootstrapping sample

  45. Model Validation Which is to say this is NOT an average of all of the point-to-point travel times Create bootstrapped sample of the set of simulation runs For each run in bootstrapped sample, create bootstrapped sample of point-to-point travel times Calculate expected travel time for each simulation run Lower and Upper Bounds calculated by bootstrapping sample

  46. Model Validation

  47. Outline • Project Background • Model Scope • Model Preparation • Model Minutia • Curvature • Grade • Lane Width • Two-lane Highway Passing

  48. Outline • Project Background • Model Scope • Model Preparation • Model Minutia • Curvature • Grade • Lane Width • Two-lane Highway Passing

  49. Curvature

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