U.S. Department of Transportation
Federal Highway Administration
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Federal Highway Administration Research and Technology
Coordinating, Developing, and Delivering Highway Transportation Innovations
| REPORT |
| This report is an archived publication and may contain dated technical, contact, and link information |
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| Publication Number: FHWA-HRT-13-097 Date: September 2014 |
Publication Number: FHWA-HRT-13-097 Date: September 2014 |
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Travelers’ choices are central to the performance of a transportation system, but little is known about what influences such choices or the impact they have on system performance. When selecting a transportation management strategy, a transportation management center operator must understand and anticipate how travelers will respond (i.e., will they stay on the same routes or divert; will they decide to walk, bike, or take a bus or train instead of driving; will they leave earlier or later, etc.).
The operator must know the potential benefits of alternative overall strategies (e.g., variable pricing or information on dynamic message signs) as well as how to handle day-to-day operations by implementing strategies to provide effective responses to particular events. The operator must also account for non-network, predisposing factors that influence travelers’ choices. Such factors, including land use, population density, and walkability, are generally out of the control of the network manager, and their influence may not be intuitively obvious.
This report addresses the current state of the practice, identifies gaps in knowledge regarding traveler choices, and provides six case studies on how to improve current models. This report provides a comprehensive conceptual framework that incorporates traveler behavior and the impact on network performance for demand-side and supply-side measures. This report will be a resource for both traveler choice researchers and organizations considering transportation management strategies that influence traveler choice.
Joseph I. Peters
Director, Office of Operations
Research and Development
Notice
This document is disseminated under the sponsorship of the U.S. Department of Transportation in the interest of information exchange. The U.S. Government assumes no liability for the use of the information contained in this document. This report does not constitute a standard, specification, or regulation.
The U.S. Government does not endorse products or manufacturers. Trademarks or manufacturers’ names appear in this report only because they are considered essential to the objective of the document.
Quality Assurance Statement
The Federal Highway Administration (FHWA) provides high-quality information to serve Government, industry, and the public in a manner that promotes public understanding. Standards and policies are used to ensure and maximize the quality, objectivity, utility, and integrity of its information. FHWA periodically reviews quality issues and adjusts its programs and processes to ensure continuous quality improvement.
Technical Report Documentation Page
| 1. Report No.
FHWA-HRT-13-097 |
2. Government Accession No. | 3 Recipient's Catalog No. | ||
| 4. Title and Subtitle
Analysis of Network and Non-Network Factors on Traveler Choice Toward Improving Modeling Accuracy for Better Transportation Decisionmaking |
5. Report Date September 2014 |
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| 6. Performing Organization Code | ||||
| 7. Author(s)
Hani S. Mahmassani, Charlotte Frei, Andreas Frei, Joseph Story, Lewison Lem, Alireza Talebpour, Ying Chen, Ali Zockaie, Meead Saberi, Hooram Halat, and Robert Haas |
8. Performing Organization Report No.
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9. Performing Organization Name and Address SAIC, M/S E-12-3,
Northwestern University |
10. Work Unit No. (TRAIS) |
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| 11. Contract or Grant No. DTFH61-06-D-00005, Task Order T-11-013 |
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| 12. Sponsoring Agency Name and Address
U.S. Department of Transportation |
13. Type of Report and Period Covered
Final Report; July 2011–April 2013 |
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14. Sponsoring Agency Code
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| 15. Supplementary Notes The Contracting Officer’s Technical Representative (COTR) was Taylor Lochrane, HRDO-20. |
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| 16. Abstract
The need to reduce congestion, enhance safety, and make the U.S. transportation system and cities more sustainable has given rise to various programs, technologies, and policies. The effectiveness of these interventions depends on how users eventually respond and, in some instances, modify their travel behavior. While significant advances have taken place over the past 50 years in the field of travel behavior research and travel demand forecasting, the ability to reliably predict the direction and magnitude of behavioral responses to various network and non-network factors and interventions remains limited. Many experts have called for better data collection and analysis methods and better integration of behavior models with supply analysis tools. This report provides a synthesis of the state of knowledge in travel behavior research and showcases how to improve current models with relevant behavior realism through six case studies. These case studies range from long-term policy interventions (e.g., urban design policy affecting land use and neighborhood walkability), to short-term en-route interventions (e.g., traveler information systems for weather-responsive system management). The case studies also include interventions aimed at environmental as well as congestion avoidance objectives. The applications provide an enhanced capability to capture traveler choices in both the main evaluation tools as well as in supporting the design process actively. This multifaceted research initiative cuts across several Federal Highway Administration (FHWA) programs such as the Office of Planning, Environment, and Realty; Office of Operations; Office of Safety; and Office of Research, Development, and Technology. This study will facilitate implementation of a balanced, cross-cutting effort to better understand the topic of traveler choice, and builds on current activities related to modeling and analysis across FHWA, professional associations, and academia. |
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| 17. Key Words
Behavior models, Demand modeling, Weather-responsive traffic management (WRTM), Traffic estimation and prediction, Weather and traffic analysis, Dynamic traffic assignment, Microsimulation |
18. Distribution Statement
No restrictions. This document is available to the public through the National Technical Information Service, Springfield, VA 22161 |
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19. Security Classification Unclassified |
20. Security Classification Unclassified |
21. No. of Pages 224 |
22. Price | |
| Form DOT F 1700.7 (8-72) | Reproduction of completed page authorized |
SI* (Modern Metric) Conversion Factors
CHAPTER 2. TRAVELER BEHAVIOR OVERVIEW
CHAPTER 3. SCOPE DELINEATION AND CONCEPTUAL FRAMEWORKS
CHAPTER 4. URBAN POLICY AND NON-NETWORK INTERVENTIONS CASE STUDY
CHAPTER 6. AERIS CASE STUDY I: SOCIAL NETWORKS AND GREEN BEHAVIORS
CHAPTER 7. AERIS CASE STUDY II: INFLO AND SPEED COMPLIANCE
CHAPTER 9. CONCLUSIONS AND RECOMMENDATIONS
APPENDIX. COMPLETE LIST OF EXAMINED VARIABLES FOR NON-NETWORK MODEL
Figure 1. Illustration. High-level conceptual framework
Figure 2. Illustration. Expanding sphere with fuzzy boundaries
Figure 3. Illustration. Direct and indirect influences of non-network factors over time
Figure 4. Equation. Land use effect
Figure 5. Equation. Utility function (mixed logit)
Figure 6. Equation. Choice probability (multinomial logit)
Figure 7. Equation. Choice probability (mixed logit)
Figure 8. Equation. Log-likelihood function (mixed logit)
Figure 9. Equation. Choice indicator
Figure 10. Illustration. Hybrid choice model with latent variables
Figure 11. Illustration. Integration of multiple data sources
Figure 12. Illustration. Chicago, IL, crime count at the TAZ level in 2008
Figure 13. Illustration. Map of approximate locations of all origins and destinations recorded in the CMAP Household Travel Survey data
Figure 14. Equation. Auto cost estimation
Figure 15. Equation. CTA bus/train cost estimation
Figure 16. Equation. Metra train cost estimation
Figure 17. Equation. Land use mix diversity index
Figure 18. Illustration. Population density in the Chicago, IL, metro region at the TAZ level from Census data
Figure 19. Illustration. Geo-coded locations of CTA bus stops
Figure 20. Illustration. Geo-coded locations of Pace bus stops
Figure 21. Illustration. Geo-coded locations of CTA train stops
Figure 22. Illustration. Geo-coded locations of Metra train stops
Figure 23. Illustration. Modeling framework for analyzing ATDM policies
Figure 24. Graph. Increase in hourly bicycle counts on specific street corridor from 2004–2012 in Washington, DC.
Figure 25. Graph. Increase in peak hour bicycle counts on specific street corridor from 2004–2012 in Washington, DC
Figure 26. Graph. Average peak hour bicycle counts on specific street corridor per mile of bicycle lanes from 2004–2012 in Washington, DC
Figure 27. Illustration. Attitudes and information dissemination impact modeling framework
Figure 28. Illusttration. Social network attitude diffusion influence process integrated into utility-based choice models
Figure 29. Equation. Dissimilarity function
Figure 30. Equation. Interaction threshold function
Figure 31. Illustration. Rogers’ bell curve of the innovation adoption life-cycle distribution
Figure 32. Equation. Communication confidence parameter
Figure 33. Equation. Type-dependent memory coefficient
Figure 34. Graph. Forgetting curve
Figure 35. Equation. Dissimilarity inequality
Figure 36. Equation. Opinion change function
Figure 37. Equation. Opinion value function
Figure 38. Equation. Impendance function
Figure 39. Equation. Class type similarity indicator
Figure 40. Equation. Opinion leader mechanism
Figure 41. Equation. Opinion follower mechanism
Figure 42. Equation. Status quo mechanism
Figure 43. Graph. Social class distribution in neighborhood 1
Figure 44. Graph. Social class distribution in neighborhood 2
Figure 45. Graph. Social class distribution in neighborhood 3
Figure 46. Graph. Social class distribution in neighborhood 4
Figure 47. Illustration. Agent locations and social classes for a network before simulation stabilization
Figure 48. Illustration. Agent locations and social classes for a network after simulation stabilization
Figure 49. Screenshot. Netlogo model interface
Figure 50. Graph. Number of agents with different attitudes in neighborhood 1
Figure 51. Graph. Number of agents with different attitudes in neighborhood 2
Figure 52. Graph. Number of agents with different attitudes in neighborhood 3
Figure 53. Graph. Number of agents with different attitudes in neighborhood 4
Figure 54. Graph. Attitude distribution in neighborhoods 1–4 of base scenario
Figure 55. Illustration. Simulation results for scenario 1: random targeting simulation results
Figure 56. Graph. Simulation results for scenario 1: random targeting percentage change of attitudes
Figure 57. Graph. Percentage change of attitudes aggregated in positive and negative attitude bins
Figure 58. Graph. Percentage change of attitudes in the different neighorhoods under scenario 2: targeting opinion leaders
Figure 59. Graph. Percentage change of attitude aggregated in positive and negative attitude bins
Figure 60. Illustration. USDOT DMA bundles
Figure 61. Illustration. USDOT AERIS application bundles
Figure 62. Illustration. Framework for evaluation of speed harmonization and related en-route interventions
Figure 63. Illustration. Geometric characteristics of the hypothetical two-lane highway
Figure 64. Illustration. Geographic characterization of the selected segment in Chicago, IL
Figure 65. Illustration. Geometric characterization of the selected segment in Chicago, IL
Figure 66. Equation. Hazard equation for time t
Figure 67. Equation. Acceleration value function
Figure 68. Equation. Crash disutility
Figure 69. Equation. Stochastic nature of the acceleration
Figure 70. Equation. CWT function
Figure 71. Equation. Mother wavelet function
Figure 72. Equation. CWT function (simplified)
Figure 73. Equation. Average wavelet energy function
Figure 74. Graph. Wavelet energy calculation—actual speed of a vehicle
Figure 75. Graph. Wavelet energy calculation—CWT of the actual speed data
Figure 76. Graph. Wavelet energy calculation—absolute values of the CWT coefficient across scales
Figure 77. Graph. Wavelet energy calculation—average wavelet energy across scales
Figure 78. Flowchart. Speed harmonization decision tree
Figure 79. Equation. Ramp metering rate
Figure 80. Equation. Wavelet energy rate
Figure 81. Equation. Average hazard
Figure 82. Graph. Fundamental diagram and hazard value for simulation with no active speed harmonization
Figure 83. Graph. Fundamental diagram and hazard value for simulation with active speed harmonization
Figure 84. Graph. Flow and speed evolution over time for simulation with no active speed harmonization
Figure 85. Graph. Flow and speed evolution over time for simulation with active speed harmonization
Figure 86. Graph. Emission and moving average evolution over time for simulation with no active speed harmonization
Figure 87. Graph. Emission and moving average evolution over time for simulation with active speed harmonization
Figure 88. Graph. Fundamental diagram and hazard value for simulation with active speed harmonization and 0 percent compliance
Figure 89. Graph. Fundamental diagram and hazard value for simulation with active speed harmonization and 10 percent compliance
Figure 90. Graph. Fundamental diagram and hazard value for simulation with active speed harmonization and 90 percent compliance
Figure 91. Graph. Flow and speed evolution over time for simulation with active speed harmonization and 0 percent compliance
Figure 92. Graph. Flow and speed evolution over time for simulation with active speed harmonization and 10 percent compliance
Figure 93. Graph. Flow and speed evolution over time for simulation with active speed harmonization and 90 percent compliance
Figure 94. Graph. Emission and moving average evolution over time for simulation with active speed harmonization and 0 percent compliance
Figure 95. Graph. Emission and moving average evolution over time for simulation with active speed harmonization and 10 percent compliance
Figure 96. Graph. Emission and moving average evolution over time for simulation with active speed harmonization and 90 percent compliance
Figure 97. Graph. Smoothed speed variations in time-space diagram for simulation without active speed harmonization
Figure 98. Graph. Flow-time diagram for simulation without active speed harmonization
Figure 99. Graph. Smoothed speed variations in time-space diagram for simulation with active speed harmonization
Figure 100. Graph. Flow-time diagram for simulation with active speed harmonization
Figure 101. Graph. Fundamental diagram and hazard value for simulation with active speed harmonization without active ramp metering at 0 percent compliance
Figure 102. Graph. Flow and speed evolution over time for simulation with active speed harmonization without active ramp metering at 0 percent compliance
Figure 103. Graph. Emission and moving average evolution over time for simulation with active speed harmonization and without active ramp metering at 0 percent compliance
Figure 104. Graph. Fundamental diagram and hazard value for simulation with active speed harmonization without active ramp metering at 10 percent compliance
Figure 105. Graph. Flow and speed evolution over time for simulation with active speed harmonization without active ramp metering at 10 percent compliance
Figure 106. Graph. Emission and moving average evolution over time for simulation with active speed harmonization and without active ramp metering at 10 percent compliance
Figure 107. Graph. Fundamental diagram and hazard value for simulation with active speed harmonization without active ramp metering at 20 percent compliance
Figure 108. Graph. Flow and speed evolution over time for simulation with active speed harmonization without active ramp metering at 20 percent compliance
Figure 109. Graph. Emission and moving average evolution over time for simulation with active speed harmonization and without active ramp metering at 20 percent compliance
Figure 110. Graph. Fundamental diagram and hazard value for simulation with active speed harmonization without active ramp metering at 40 percent compliance
Figure 111. Graph. Flow and speed evolution over time for simulation with active speed harmonization without active ramp metering at 40 percent compliance
Figure 112. Graph. Emission and moving average evolution over time for simulation with active speed harmonization and without active ramp metering at 40 percent compliance
Figure 113. Graph. Fundamental diagram and hazard value for simulation with active speed harmonization without active ramp metering at 90 percent compliance
Figure 114. Graph. Flow and speed evolution over time for simulation with active speed harmonization without active ramp metering at 90 percent compliance
Figure 115. Graph. Emission and moving average evolution over time for simulation with active speed harmonization and without active ramp metering at 90 percent compliance
Figure 116. Graph. Fundamental diagram and hazard value for simulation with active speed harmonization and active ramp metering at 0 percent compliance
Figure 117. Graph. Flow and speed evolution over time for simulation with active speed harmonization and active ramp metering at 0 percent compliance
Figure 118. Graph. Emission and moving average evolution over time for simulation with active speed harmonization and active ramp metering at 0 percent compliance
Figure 119. Graph. Fundamental diagram and hazard value for simulation with active speed harmonization and active ramp metering at 10 percent compliance
Figure 120. Graph. Flow and speed evolution over time for simulation with active speed harmonization and active ramp metering at 10 percent compliance
Figure 121. Graph. Emission and moving average evolution over time for simulation with active speed harmonization and active ramp metering at 10 percent compliance
Figure 122. Graph. Fundamental diagram and hazard value for simulation with active speed harmonization and active ramp metering at 20 percent compliance
Figure 123. Graph. Flow and speed evolution over time for simulation with active speed harmonization and active ramp metering at 20 percent compliance
Figure 124. Graph. Emission and moving average evolution over time for simulation with active speed harmonization and active ramp metering at 20 percent compliance
Figure 125. Graph. Fundamental diagram and hazard value for simulation with active speed harmonization and active ramp metering at 40 percent compliance
Figure 126. Graph. Flow and speed evolution over time for simulation with active speed harmonization and active ramp metering at 40 percent compliance
Figure 127. Graph. Emission and moving average evolution over time for simulation with active speed harmonization and active ramp metering at 40 percent compliance
Figure 128. Graph. Fundamental diagram and hazard value for simulation with active ?speed harmonization and active ramp metering at 90 percent compliance
Figure 129. Graph. Flow and speed evolution over time for simulation with active speed harmonization and active ramp metering at 90 percent compliance
Figure 130. Emission and moving average evolution over time for simulation with active speed harmonization and active ramp metering at 90 percent compliance
Figure 131. Illustration. Integrated DTA model with weather measures/forecast
Figure 132. Illustration. WRTM modeling framework
Figure 133. Equation. WAF
Figure 134. Illustration. Network configuration and description for Chicago, IL, network
Figure 135. Illustration. Chicago, IL, study area and adjacent ASOS stations
Figure 136. Illustration. Selected detector locations in Chicago, IL
Figure 137. Equation. Auto travel cost estimation
Figure 138. Equation. Transit travel cost estimation
Figure 139. Equation. Park & ride travel cost estimation
Figure 140. Illustration. Nested and non-nested mode choice model structure—structure for trips to and from the CBD
Figure 141. Illustration. Nested and non-nested mode choice model structure—structure for trips outside the CBD
Figure 142. Equation. Random utility model
Figure 143. Equation. Utility of park & ride and transit
Figure 144. Equation. Distribution of nested logit error terms
Figure 145. Equation. Conditional choice probability (nested logit)
Figure 146. Equation. Marginal choice probabilities (nested logit)
Figure 147. Equation. Log-likelihood function (nested logit)
Figure 148. Equation. Choice indicator function
Figure 149. Equation. Choice probability
Figure 150. Equation. Scheduling cost function
Figure 151. Equation. Definition of schedule delay early and late
Figure 152. Graph. Traffic detector volumes on clear weather days and a median snow day in Chicago, IL
Figure 153. Graph. Morning peak departure time distribution for school trips and work trips
Figure 154. Equation. Accumulated percentage of out vehicles
Figure 155. Equation. Percentage change in average travel time
Figure 156. Equation. Percentage change in average stop time
Figure 157. Illustration. Simulated network density for scenarios 1 and 2 with 100 percent demand—clear weather condition
Figure 158. Illustration. Simulated network density for scenarios 1 and 2 with 100 percent demand—median snow day
Figure 159. Illustration. Simulated link speed distribution for scenarios 1 and 2 for the Kennedy Expressway between Pulaski Road and North Cicero Avenue westbound—clear weather condition
Figure 160. Illustration. Simulated link speed distribution for scenarios 1 and 2 for the Kennedy Expressway between Pulaski Road and North Cicero Avenue westbound—median snow day
Figure 161. Illustration. Traffic volume distribution for scenarios 1 and 2 for the Kennedy Expressway between Pulaski Road and North Cicero Avenue westbound—clear weather condition
Figure 162. Illustration. Traffic volume distribution for scenarios 1 and 2 for the Kennedy Expressway between Pulaski Road and North Cicero Avenue westbound—median snow day
Figure 163. Illustration. Simulated network density for scenario 2 at 9:30 a.m
Figure 164. Illustration. Simulated network density for scenario 3 at 9:30 a.m
Figure 165. Illustration. Simulated link speed distribution for scenario 2 for the Kennedy Expressway between Pulaski Road and North Cicero Avenue westbound
Figure 166. Illustration. Simulated link speed for scenario 3 for the Kennedy Expressway between Pulaski Road and North Cicero Avenue westbound
Figure 167. Illustration. Traffic volume distribution for scenario 2 for the Kennedy Expressway between Pulaski Road and North Cicero Avenue westbound
Figure 168. Illustration. Traffic volume distribution for scenario 3 for the Kennedy Expressway between Pulaski Road and North Cicero Avenue westbound
Figure 169. Illustration. Simulated network density for scenario 4.1.2 at 9:30 a.m
Figure 170. Illustration. Simulated link speed distribution for scenario 4.1.2 for the Kennedy Expressway between Pulaski Road and North Cicero Avenue westbound
Figure 171. Illustration. Traffic volume distribution for scenario 4.1.2 for the Kennedy Expressway between Pulaski Road and North Cicero Avenue westbound
Figure 172. Illustration. Simulated network density for scenario 5 at 9:30 a.m
Figure 173. Illustration. Simulated link speed distribution for scenario 5 for the Kennedy Expressway between Pulaski Road and North Cicero Avenue westbound
Figure 174. Illustration. Traffic volume distribution for scenario 5 for the Kennedy Expressway between Pulaski Road and North Cicero Avenue westbound
Figure 175. Illustration. Simulated network density for scenario 6.1 at 9:30 a.m
Figure 176. Illustration. Simulated link speed distribution for scenario 6.1 for the Kennedy Expressway between Pulaski Road and North Cicero Avenue westbound
Figure 177. Illustration. Traffic volume distribution for scenario 6.1 for the Kennedy Expressway between Pulaski Road and North Cicero Avenue westbound
Figure 178. Graph. Accumulated percentage of out vehicle for different scenarios
Figure 179. Graph. Changes in average travel time and average stop time relative to the benchmark
Figure 180. Graph. Summary of daily demand
Figure 181. Graph. Comparison of transit usage to Interstate 5 northbound traffic
Figure 182. Graph. Median dry day volumes by time of day—south of Boeing Access Road off-ramp
Figure 183. Graph. Median drizzle day volumes by time of day—south of Boeing Access Road off-ramp.
Figure 184. Graph. Median rainy day volumes by time of day—south of Boeing Access Road off-ramp
Figure 185. Graph. Percentage of off-ramp traffic comparing median and study drizzle day
Table 1. Summary of the effects of land use on travel behavior
Table 2. Point elasticity estimates imputed from mode choice models: percentage change in probability of choosing mode with a 1 percent increase in built environment factor
Table 3. Crime categories tested in model
Table 4. Mode-specific average speeds from Chicago, IL, Household Travel Survey data
Table 5. Distance-based Metra fares
Table 6. Mixed logit mode choice model
Table 7. Basic non-motorized use data element: intersection non-motorized counts in Arlington County, VA
Table 8. Number of Metrorail stations where bicyclists boarded or alighted, by rail line; LAC MTA
Table 9. Bicycle boardings data at transit stations: LAC MTA blue line
Table 10. LAC MTA bicycle transit survey responses to motor vehicle access question, “How often do you have access to a motor vehicle?”
Table 11. LAC MTA bicycle transit survey responses to motor vehicle access question, “If you did not have your bike, how would you get from your origin to the first station?”
Table 12. Example of comparison data from other cities: mode shift to bikeshare
Table 13. Example of bicycle use data linked to other traveler attributes: bicycle trip purpose data from Los Angeles County
Table 14. Summary of Cleveland, OH, BoBB data
Table 15. Example of bicycle use data linked to other traveler attributes: bicycle trip purpose data from Los Angeles County
Table 16. Variables and values used in the simulation experiment
Table 17. Neigborhood description with social class (household income) related to attitude
Table 18. Social type distribution in all neighborhoods
Table 19. External covariates and their definition
Table 20. Descriptive statistics of calibrated parameters
Table 21. Pearson correlation coefficients and p-values (in parentheses)
Table 22. Supply-side properties related with weather impact in DYNASMART
Table 23. Airports with ASOS stations and available time periods for data
Table 24. Mode-specific average speeds from the 2011 NTD
Table 25. Nested logit model estimation results for trips from and to the CBD
Table 26. Logit model estimation results for trips outside of the CBD
Table 27. Departure time choice model parameter estimates
Table 28. Demand scenario overview and description
Table 29. Boeing Field weather data
Table 30. Summary of daily demand (October 10, 2012, to December 6, 2012)
Table 31. Variables and their definitions
