Transit Modeling Guidance by Rosella Picado and ... - FSUTMSOnline

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Destination Choice Estimation Optionally, explode the sample • Reproduce each trip record k times, and choose a different destination set for each ‘exploded’ record Approximately statistically equivalent to choosing a destination set of 10 x k for each original trip record Apply weights (1/k) and sampling correction factor during estimation U = U + C '' ijm ijm jm
Destination Choice Estimation Size variable coefficients may be estimated simultaneously, or pre-calculated Size variable pre-calculation options: • Use representative employment − HBW - total employment − HBSH - retail employment − HBU – college enrollment • Estimate coefficients using linear regression, similar to attraction model estimation • Tabulate workers by household income and industry (provides HBW income stratification)
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Transit Modeling Update presented t
Page 3 and 4:
Transit Working Group Vidya Mysore,
Page 5 and 6:
Project Overview On-Board Rider Sur
Page 7 and 8:
Principles of Model Calibration and
Page 9 and 10:
Case for the Project Workshop Pilot
Page 11 and 12:
New PT Features in Cube Voyager 6.0
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Examples of Drive-Access Coding Aer
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Examples of Drive-Access Coding Net
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Drive-Access Generation in 5.x Hist
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Drive-Access Generation Examples in
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Drive-Access Generation Examples in
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Drive-Access Generation in 6.x Cube
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Enhanced Drive-Access Generation Ex
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Output Files by PT AUTOCON Process
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Enhanced Fares along with BestPathO
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Fare Process in 5.x Output example
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Enhanced Fare Process in 6.x Exampl
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Enhanced Fare Process in 6.x Exampl
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Enhanced Transit Line Keywords in 6
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Page 43 and 44:
Transit Modeling Update TRIP MARKET
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Proposed New Trip Purposes HB Colle
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Proposed New Trip Purposes NHB Work
Page 49 and 50: Key Travel Market Attributes Model
Page 51 and 52: Key Travel Market Attributes FTA Re
Page 53 and 54: Building the Household Classificati
Page 55 and 56: Building the Household Classificati
Page 57 and 58: Household Workers Classification Mo
Page 59 and 60: Transit Modeling Update AUTO OWNERS
Page 61 and 62: Proposed Auto Ownership Model Multi
Page 63 and 64: Mixed Household / Employment Densit
Page 65 and 66: Sample Auto Ownership Coefficients
Page 67 and 68: Trip Production Model Home-Based Wo
Page 69 and 70: Transit Modeling Update TRIP ATTRAC
Page 71 and 72: Trip Attraction Model Linear regres
Page 73 and 74: Transit Modeling Update SPECIAL TRI
Page 75 and 76: Example 1: Portland Air Passenger T
Page 77 and 78: Transit Modeling Update TIME OF DAY
Page 79 and 80: FSUTMS Approach to Time of Day Effe
Page 81 and 82: Implications for other model compon
Page 83 and 84: FSUTMS Diurnal Factors Developed fr
Page 85 and 86: Travel Time Feedback: Why? Ensure c
Page 87 and 88: Transit Modeling Update TRIP DISTRI
Page 89 and 90: Destination Choice Advantages Sensi
Page 91 and 92: Destination Choice Model Multinomia
Page 93 and 94: Trip Market Stratification At least
Page 95 and 96: Attraction Balancing Whether to sha
Page 97 and 98: Destination Choice Estimation Requi
Page 99: Destination Choice Estimation Sampl
Page 103 and 104: Destination Choice The mode choice
Page 105 and 106: Transit Modeling Update TRANSIT NET
Page 107 and 108: Good transit coding practices … R
Page 109 and 110: Good transit coding practices … R
Page 111 and 112: Transit network coding Access Conne
Page 113 and 114: Path building and skimming Path Bui
Page 115 and 116: Transit Modeling Update MODE CHOICE
Page 117 and 118: Trip market segmentation Household
Page 119 and 120: Tier B/C Cities Mode Choice Nesting
Page 121 and 122: FSUTMS Nesting Coefficients 0.8 0.5
Page 123 and 124: Nested Logit Model: How? Choice Aut
Page 125 and 126: Logit Model Curve 1 Auto Probabilit
Page 127 and 128: Coordinating DC and MC Calibration
Page 129 and 130: Trip Generation EE Trips Visitor &
Page 131 and 132: Build Hwy Networks & Skims Build Pe
Page 133 and 134: Trip Generation EE Trips HH Classif
Page 135 and 136: Travel time feedback
Page 137 and 138: Trip Distribution Destination choic
Page 139 and 140: Model Inputs: What’s New? Zonal D
Page 141 and 142: Model Parameters: What’s New? Pea
Page 143 and 144: Catalog Keys: What’s New? Auto oc
Page 145 and 146: Deliverables Technical Memoranda
Page 147: Deliverables Workshops • Case for
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