In recent years, transportation systems have been judged on performance-based outcomes, thus, quantitative methods have become increasingly important to such assessments. Transportation Statistics brings together the work of outstanding international experts on the latest methods and their applications to the many modes of transportation statistics. This one-of-a-kind, definitive reference will equip practitioners, policy makers, and academic researchers with state-of-the-art statistical tools used in transportation modeling, how to interpret results and how to analyze the implications of those results.

Brian W. Sloboda was a former economist at the Bureau of Transportation Statistics in the U.S. Department of Transportation and the Bureau of Economic Analysis in the U.S. Department of Commerce. While at the Bureau of Transportation Statistics, he did research in examining the relationship of transportation and the economy, productivity in the various modes of transportation; and tourism and transportation. Currently, he is a pricing economist at the US Postal Service. He is also teaching economics and statistics as an adjunct faculty for the University of Phoenix, University of Maryland, Park University, and the USDA Graduate School.

Contributors	p. ix
Preface	p. xiii
About the Author	p. xxi
Macroscopic Road Safety Modeling: A State Space Approach Applied to Three Belgian Regions	p. 1
Introduction	p. 1
Exposure and Risk in Road Safety Research	p. 3
Oppe and Its Extensions	p. 3
Layered Structure of the DRAG Family	p. 5
Unobserved Components Models	p. 5
Model Development	p. 7
Tri-variate State Space Model	p. 7
Estimation	p. 9
DATA	p. 11
RESULTS	p. 12
Model Estimation	p. 12
Regression Parameters	p. 13
Model Components	p. 14
Conclusions	p. 16
Traffic Safety Study: Empirical Bayes or Hierarchical Bayes?	p. 21
Introduction	p. 21
Bayesian Analysis for Safety Studies	p. 23
Crash Frequency Modeling	p. 24
Negative Binomial Model	p. 24
Extensions of the Negative Binomial Model	p. 25
Hierarchical Poisson Models	p. 26
Extensions of Hierarchical Models	p. 26
Posterior Analysis	p. 27
Simulation Study	p. 28
Simulation Design	p. 29
Performance Evaluation Criteria	p. 31
Data Sets	p. 32
Computational Details	p. 34
Results	p. 34
Summary	p. 38
Utilizing Data Warehouse to Develop Freeway Travel Time Reliability Stochastic Models	p. 43
Introduction	p. 43
Literature Review	p. 45
Percent Variation	p. 45
California Reliability Method	p. 45
Florida Reliability Method	p. 46
Buffer Time	p. 46
Research Framework	p. 47
Research Conceptual Plan	p. 47
I-4 Traffic Data Warehouse	p. 49
Data Preparation	p. 50
Potential Independent Variables	p. 51
ANOVA Test of Significance	p. 53
Segment Travel Time Reliability Stochastic Models	p. 54
Weibull Stochastic Model	p. 54
Exponential Stochastic Model	p. 54
Lognormal Stochastic Model	p. 55
Normal Stochastic Model	p. 55
Goodness-of-Fit Tests	p. 55
System Travel Time Reliability	p. 56
Model Application	p. 56
Segment Travel Time Distribution	p. 57
Spatial Correlation in Travel Times	p. 57
Travel Time Reliability Stochastic Models	p. 59
Theoretical Versus Empirical Reliability Distributions	p. 60
Segment Travel Time Reliability Models	p. 62
Travel Time Reliability and Departure Time	p. 62
Benefits of the New Method to Practitioners and Travelers	p. 64
Segment Travel Time Reliability	p. 64
Corridor Travel Time Reliability	p. 66
Comparison Between Reliability Methods	p. 68
Summary	p. 73
Mixed Logit Modeling of Parking-Type Choice Behavior	p. 77
Introduction	p. 77
Review of Parking-Type Choice Literature	p. 79
Data	p. 79
Description of Data and the Stated Choice Survey	p. 79
Methodology	p. 82
Data Rearrangement	p. 82
Choice of Model	p. 82
Model Specification and Estimation	p. 84
Choice of Random Distributions	p. 86
Results	p. 87
Overall Data Set	p. 89
Grouping by Location	p. 91
Grouping by Activity	p. 95
Summary	p. 98
Modeling Daily Traffic Counts: Analyzing the Effects of Holidays	p. 103
Introduction	p. 103
Data	p. 106
Daily Traffic	p. 106
Holiday and Day-of-Week Effects	p. 109
Methodology	p. 110
Spectral Analysis	p. 110
Exponential Smoothing	p. 111
ARMA Modeling	p. 112
Regression Modeling	p. 112
Box-Tiao Modeling	p. 113
Model Evaluation	p. 113
Results	p. 114
Spectral Analysis	p. 114
Holt-Winters Multiplicative Exponential Smoothing	p. 114
ARMA Modeling	p. 116
Box-Tiao Modeling	p. 117
Model Comparison	p. 117
Summary	p. 120
Issues With Small Samples in Trip Generation Estimation	p. 123
Introduction	p. 123
Study Area and Data	p. 125
Small Samples in Trip Generation	p. 126
Identification of Outliers	p. 127
Improving the Reliability of Trip Generation Rates With CART Procedures	p. 130
Background Information	p. 130
CART Demonstration Using Two Independent Variables	p. 131
Comparison Between CUUATS and CART Models	p. 134
CART Demonstration Using Three Independent Variables	p. 134
Row-Column Decomposition Analysis as an Imputation Method	p. 144
Summary	p. 148
Recent Progress on Activity-Based Microsimulation Models of Travel Demand and Future Prospects	p. 151
Introduction	p. 151
Background	p. 152
Activity-Based Modeling	p. 153
Econometric Activity-Based Modeling	p. 154
Bowman and Ben-Akiva Model	p. 155
PB Consult Models	p. 156
CEMDAP	p. 156
Rule-Based Activity Scheduling Models	p. 156
Scheduler	p. 157
TASHA	p. 158
Albatross	p. 159
Dynamic Scheduling Models	p. 159
Microsimulation in Activity-Based Modeling	p. 160
Population Synthesis	p. 162
Activity Generation and Planning	p. 163
Activity Travel Execution	p. 165
Summary	p. 166
Future of Rule-Based Activity Scheduling Models	p. 166
Maximum Simulated Likelihood Estimation With Spatially Correlated Observations: A Comparison of Simulation Techniques	p. 173
Introduction	p. 173
Mixed Logit Model	p. 176
Simulation Techniques	p. 178
Standard Halton Sequence	p. 178
Scrambled Halton Sequence	p. 180
Shuffled Halton Sequence	p. 183
Long Shuffled Halton Sequence	p. 183
Randomized (Shifted) Shuffled Halton Sequence	p. 184
Correlated Synthetic Data	p. 184
Results Analysis	p. 186
Summary	p. 189
Analyzing the Impact of Land Transportation on Regional Tourism: The Case of the Closure of the Glion Tunnel in the Valais, Switzerland	p. 195
Introduction	p. 195
Data Description and Estimation of Missing Values	p. 198
Endogenous Variables	p. 198
Exogenous Variables	p. 199
Analyzing the Tourism and Transportation Dynamic (Nowcasting)	p. 201
Forecast of Glion (A9) and Lausanne-Vevey (K9)	p. 205
Forecasting the Endogenous Tourism Variables	p. 206
Estimates of Figures Lost	p. 207
Summary	p. 209
Appendix 1	p. 210
Quasi-Likelihood Generalized Linear Regression Analysis of Fatality Risk Data	p. 215
Introduction	p. 215
Generalized Linear Models	p. 216
Parameter Estimation and Statistical Inference	p. 219
Illustrative Application	p. 222
Summary	p. 229
Developing Statewide Weekend Travel-Demand Forecast and Mode-Choice Models for New Jersey	p. 231
Introduction	p. 231
Research Problem and Background	p. 233
Current State of Practices in Weekend Models	p. 234
TMIP Discussions	p. 234
MPO Survey	p. 236
Existing Travel-Demand Forecast Models in New Jersey	p. 239
Model Structures of Various MPOs	p. 240
Expectations of Statewide Weekend Model	p. 242
Specifications for a Statewide Weekend Travel-Demand Model	p. 243
Development of Weekend Total Demand for Transit Travel and Trans-Hudson Automobile Travel	p. 245
Development of Weekend Transit Level-of-Service Matrices	p. 246
Summary	p. 246
Transferability of Time-of-Day Choice Modeling for Long-Distance Trips	p. 249
Introduction	p. 249
Long-Distance Trips	p. 251
Data Description	p. 251
MNL Modeling Analysis and Results	p. 255
MNL Modeling	p. 255
Model Specifications	p. 256
Model Parameter Coefficients	p. 258
Summary	p. 260
Univariate Sensitivity and Uncertainty Analysis of Statewide Travel Demand and Land Use Models for Indiana	p. 263
Introduction	p. 263
Background	p. 265
Literature Review	p. 265
Calculating Univariate Uncertainty	p. 266
Model Description	p. 267
Data Description	p. 267
LUCI2 Urban Simulation Model Estimation	p. 268
Indiana Statewide Travel-Demand Model	p. 270
Simulation Results	p. 273
Summary	p. 277
Index	p. 281
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