1. Microsimulation for Rural and Exurban Regions: Lake County, California David Gerstle (presenting) & Zheng Wei 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 TransModeler microsimulation 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

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

9. 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

12. Dominant route for through traffic passes through populated areas

13. Outline Project Background Model Scope Model Preparation Model Minutia

14. Outline Project Background Model Scope –Geography –Time Periods & Vehicle Population Model Preparation Model Minutia

15. Lake County

16. Lake County 450 square miles of Lake County, from Middletown (Napa border) to Upper Lake (Mendocino border)

17. Lake County

18. 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

19. Lake County High level of detail for local streets Nice

20. Lake County Intersection geometry accurately reproduced

21. Outline Project Background Model Scope –Geography –Time Periods & Vehicle Population Model Preparation Model Minutia

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. 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

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

25. Outline Project Background Model Scope Model Preparation –Data Collection –Model Calibration –Model Validation Model Minutia

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

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

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

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

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

31. Outline Project Background Model Scope Model Preparation –Data Collection –Model Calibration –Model Validation Model Minutia

32. 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

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 Calibrated Travel Demand Model Match Counts (ODME) Match Times (DTA) Calibrated Micro-simulation Model

34. 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

35. Outline Project Background Model Scope Model Preparation –Data Collection –Model Calibration –Model Validation Model Minutia

36. 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

37. 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 Calibrated Traffic Simulation Model Revisit Calibration (ODME/DTA) Match Times (Simulation) Validated Micro-simulation Model

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

39. Boundary Upper Lake Lucerne SR-53 Lower Lake Kelseyville Middletown Model Validation AM Southbound Travel Times

40. Boundary Upper Lake Lucerne SR-53 Lower Lake Kelseyville Middletown Model Validation AM Northbound Travel Times

41. Boundary Upper Lake Lucerne SR-53 Lower Lake Kelseyville Middletown Model Validation PM Southbound Travel Times

42. Boundary Upper Lake Lucerne SR-53 Lower Lake Kelseyville Middletown Model Validation PM Northbound Travel Times

43. Model Validation

44. Lower and Upper Bounds calculated by bootstrapping sample

45. Model Validation Lower and Upper Bounds calculated by bootstrapping sample 1.Create bootstrapped sample of the set of simulation runs 2.For each run in bootstrapped sample, create bootstrapped sample of point-to-point travel times 3.Calculate expected travel time for each simulation run

46. Model Validation Lower and Upper Bounds calculated by bootstrapping sample 1.Create bootstrapped sample of the set of simulation runs 2.For each run in bootstrapped sample, create bootstrapped sample of point-to-point travel times 3.Calculate expected travel time for each simulation run Which is to say this is NOT an average of all of the point-to- point travel times

47. Model Validation

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

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

50. Curvature

51. Radius of 20ft, curvature of (1/20ft)*1000ft = 50 in Segment layer

52. Curvature

53. Maximum speed is constrained by the radius

54. Curvature

55. Curvature at which maximum speed 55 mph

56. Curvature Reduction in Travel Time for two pairs with most curvature

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

58. Grade

59. 30 ft of elevation gain, from USGS DEM Grade 1,000 ft long

60. Grade 3% Grade

61. Grade 3% Grade Effect on Acceleration Effect on Max. Speed

62. Grade

63. Now looking at statistics across all point-to-point Travel Times (not at simulation run level)

64. Grade Effect is opposite for uphill vs. downhill

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

66. Lane Width

67. 12 ft lane 11 ft lane 10 ft lane

68. Lane Width 12 ft lane 11 ft lane 10 ft lane

69. Lane Width

70. Back to Expected Travel Time

71. Lane Width Travel Time drops without Lane Width restriction

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

73. Two-lane Highway Passing

74. Two-Lane Highway Passing

75. Now looking at statistics across all point-to-point Travel Times (not at simulation run level)

76. Two-Lane Highway Passing Generally increases travel time, as expected Exceptions are due to network effects

77. Conclusion Lane level detail is essential for accurate modeling of rural and exurban regions

78. Conclusion Lane level detail is essential for accurate modeling of rural and exurban regions, and, as a corollary, microsimulation is essential for accurate modeling of rural and exurban regions

79. Thank you david@caliper.com