Journal of Transportation and Statistics Volume 9 Number 1
Thursday, March 23, 2017
Journal of Transportation and Statistics
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NOTE: The views presented in the articles in this journal are those of the authors and not necessarily the views of the Bureau of Transportation Statistics. All material contained in this journal is in the public domain and may be used and reprinted without special permission; citation as to sources is required.
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Entire Report |
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Editorial Board |
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Contributors |
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Front Matter |
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Letter from the Editor-in-Chief |
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Paper 1 - Impacts of Productivity Changes in Air Transportation on Profits, Prices, and Labor Compensation: 1990-2001 by Anthony D. Apostolides |
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Table 1 - Growth Rates of Labor Productivity in Transportation |
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Table 2 - Productivity and Profits in Air Transportation |
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Table 3 - Productivity and Prices in Air Transportation |
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Table 4 - Labor Compensation per Employee |
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Figure 1 - Labor Productivity in Air Transportation |
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Figure 2 - Labor Productivity in Transportation and the U.S. Business Sector |
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Figure 3 - Multifactor Productivity in Air Transportation and U.S. Business Sector |
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Appendix 1 - Growth of Multifactor Productivity in Air Transportation |
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Appendix 2 - Spearman Rank Correlation Coefficients |
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Appendix 3 - Prices in Air Transportation |
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Appendix 4 |
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Appendix 5 |
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Paper 2 - Speed as a Risk Factor in Serious Run-off-Road Crashes: Bayesian Case-Control Analysis with Case Speed Uncertainty by Gary A. Davis, Sujay Davuluri, and Jianping Pei |
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Table 1 - Posterior Means and Standard Deviations for the Speeds of the Case Vehicles, and the Measured Speeds for the Control Vehicles, for 10 Road Accident Research Unit Run-off Road Crashes |
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Table 2 - Posterior Means and Standard Deviations for the Speeds of the Case Vehicles, and Measured Speeds of the Control Vehicles, for 10 Minnesota Run-off Road Crashes |
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Table 3 - Bayesian Parameter Estimates and Goodness of Fit Measures for the Linear and Constrained Quadratic Models Applied to the Minnesota Data |
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Table 4 - Bayesian Parameter Estimates and Goodness of Fit Measures for Linear and Quadratic Models Applied to the RARU Data |
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Figure 1 - Contour Plot of Log Likelihood Function of Quadratic Model Fit to the Minnesota Data |
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Figure 2 - Matched Case Control Log Likelihood as a Function of b1 for Linear Model Fit to Road Accident Research Unit Data |
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Paper 3 - Speed Estimation for Air Quality Analysis by Huafeng Gong, Mei Chen, Jesse Mayes, and Rob Bostrom |
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Table 1 - Sample Data Summary |
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Table 2 - Speed Comparison Based on the 1997 Speed Study |
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Table 3 - Speed Comparison Based on the 2004 Christian Country Survey |
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Table 4 - Christian Country Speeds Comparison |
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Table 5 - Speed Comparison with Changes in Traffic Control Devices |
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Table 6 - Statewide Average Speeds by Area Type and Functional Class |
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Figure 1 - Estimating Average Effective Speed |
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Paper 4 - Measurement Errors in Poisson Regressions: A Simulation Study Based on Travel Frequency Data by Erling Häggström Lundevaller |
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Table 1 - Maximum Likelihood Estimates of the Model Assuming No Random Effects |
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Table 2 - The Bias Measure when Measurement Errors Exist in the Petrol Price Index |
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Table 3 - The Bias Measure when Measurement Error Exists in the Income Variable |
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Table 4 - Percentage of Rejected Replications with Different Levels of Effects and Measurement Error |
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Table 5 - Percentage of Rejected under Negatively Correlated Individual Effects and Different Levels of Individual Effects and Measurement Errors |
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Paper 5 - Measuring Variability in Urban Traffic Flow by Use of Principal Component Analysis by Theodore Tsekeris and Antony Stathopoulos |
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Table 1 - Values of MRE Resulting from the Reconstruction of a Typical Traffic Flow |
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Figure 1 - Illustration of the Greater Athens Area (GAA) Network and Configuration of the Location of Loop Detectors |
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Figure 2 - Graphical Representation of an Eigenflow and its Corresponding Principal Axis |
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Figure 3 - Number of Significant Eigenflows, in Terms of the Cumulative Density Function (CDF) of the Number of Entries in Each Row of the Principal Matrix that Exceed the Threshold and Histogram of Significant Eigenflows |
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Figure 4 - The Number of Significant Eigenflows with Respect to the Monthly Average Daily Traffic Flow Rate |
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Figure 5 - Plot of Singular Values for Traffic Flows and Normalized Traffic Flows |
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Figure 6 - Reconstruction of a Typical Traffic Flow Using 5 Principal Components and Statistical Analysis of the Reconstructed Traffic Flow |
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Figure 7 - Decomposition of a Typical Eigenflow by Use of Temporal Trend Thresholding and t-test Statistical Analysis of Structural Breaks and Outliers |
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Paper 6 - Frequency and Severity of Belgian Road Traffic Accidents Studied by State-Space Methods by Elke Hermans, Geert Wets, and Filip van den Bossche |
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Table 1 - Dependent and Independent Variables |
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Table 2 - Overview of the Significant Explanatory and Correction Variables for Each Dependent Variable |
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Figure 1 - Actual Monthly Accident and Casualty Observations: 1974-1999 |
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Figure 2 - Residuals of the Model with Stochastic Trend, Deterministic Seasonal, and Explanatory Variables for the Four Dependent Variables |
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Figure 3 - Monthly Two-Year Ahead Predictions for the Four Dependent Variables |
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Reviewers for 2006 |
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Index to Volume 9 |
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Back Cover |
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