Journal of Transportation and StatisticsVolume 4 Number 1
Wednesday, March 22, 2017
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Journal of Transportation and Statistics
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NOTES: 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.
The Journal of Transportation and Statistics has been discontinued by BTS because of budget constraints. Papers are therefore no longer being accepted or considered for publication. BTS hopes to bring the Journal back as a virtual publication in the future, and will post any news about the status of JTS on this page.
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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 to the Editor |
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| Paper 1 - Assessing the Impact of Speed-Limit Increases on Fatal Interstate Crashes by Sandy Balkin and J. Keith Ord |
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| Table 1 - Significant Changes in Predicted Accident Rates Attributed to the Speed-Limit Increases on Rural Interstates |
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| Table 2 - Significant Changes in Predicted Accident Rates Attributed to the Speed-Limit Increases on Urban Interstates |
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| Table 3 - Predicted Number of Fatal Crashes Attributed to the Speed-Limit Increase on Rural Interstates |
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| Table 4 - Predicted Number of Fatal Crashes Attributed to the Speed-Limit Increase on Urban Interstates |
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| Figure 1 - Rural Arizona Crashes (Actual series and level component, Seasonal component, Irregular component) |
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| Figure 2 - Aggregate Fatal Crashes: Rural, Urban, and All |
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| Figure 3 - Significance Levels of Responses to the First Speed-Limit Change (1987) on Rural Interstates |
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| Figure 4 - Significance Levels of Responses to the Second Speed-Limit Change (1996) on Rural Interstates |
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| Figure 5 - Significance Levels of Responses to the Speed-Limit Change (1996) on Urban Interstates |
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| Figure 6 - Significance Levels of Seasonal Components for Fatal Accidents on Rural Interstates |
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| Figure 7 - Significance Levels of Seasonal Components for Fatal Accidents on Urban Interstates |
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| Discussion: Johannes Ledolter |
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| Discussion: Michael Fontaine, Tongbin Teresa Qu, Karl Zimmerman, and Clifford Spiegelman |
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| Figure 1 - Injury Crashes per Month on Rural Interstate Highways in Texas, 1992-1999 |
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| Figure 2 - Injury Crashes per Month and Injury Crashes during Icy or Snowy Weather on Rural Interstate Highways in Texas, 1992-1999 |
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| Discussion: Andrew Harvey |
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| Figure 1 - Series for Rural and Urban Arizona with Levels Estimated by a Bivariate Model |
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| Rejoinder: Sandy Balkin and J. Keith Ord |
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| Paper 2 - Accounting for Uncertainty in Estimates of Total Traffic Volume: An Empirical Bayes Approach by Gary Davis and Shimin Yang |
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| Table 1 - Evaluation of 90% Prediction Intervals: First Factor Group |
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| Table 2 - Evaluation of 90% Prediction Intervals: Second Factor Group |
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| Table 3 - Evaluation of 90% Prediction Intervals: Third Factor Group |
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| Paper 3 - Creating Land-Use Scenarios by Cluster Analysis for Regional Land-Use and Transportation Sketch Planning by Joshua Smith and Mitsuru Saito |
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| Table 1 - GeoProcessing® Steps Used in Preparing Land-Use Data for Cluster Analysis |
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| Table 2 - Input Parameters for 38 Trial Cluster Analyses on Land-Use Distress |
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| Table 3 - Land-Use Distribution in Percent of the Clustered Land-Use Scenarios for 343 Wasatch Front Region Planning Districts |
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| Sub Table 1 |
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| Sub Table 2 |
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| Sub Table 3 |
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| Sub Table 4 |
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| Figure 1 - Map of the Study Area and 343 Districts for the Largely Developable Areas of the Wasatch Front Region |
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| Figure 2 - Land Use in the Wasatch Front Region |
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| Figure 3 - Classification of Land-Use Scenarios for 343 Districts Using Single Linkage |
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| Figure 4 - Final Classification of Land-Use Scenarios for 343 Districts Using Cluster Analysis |
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| Paper 4 - Three Faces of Eve: How Engineers, Economists, and Planners Variously View Congestion Control, Demand Management, and Mobility Enhancement Strategies by Erik Ferguson |
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| Table 1 - Performance Measures |
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| Table 2 - Authors and Data |
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| Table 3 - Semantic Scales |
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| Table 4 - Regression Results |
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| Table 5 - Variable Correlations |
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| Table 6 - Outliers |
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| Table A-1 Arnold Data |
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| Table A-2 Downs Data |
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| Table A-3 Zupan Data |
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| Table B-1 Arnold Model |
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| Table B-2 Downs Model |
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| Table B-3 Zupan Model |
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| Table C-1 Arnold Output |
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| Table C-2 Downs Output |
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| Table C-3 Zupan Output |
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| Paper 5 - Loglinear Models and Goodness-of-Fit Statistics for Train Waybill Data by Herbert Lee and Kert Viele |
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| Table 1 - Cross Validation Goodness-of-Fit Statistics for the Top Models |
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| Paper 6 - Estimation and Evaluation of Full Marginal Costs of Highway Transportation in New Jersey by Kaan Ozbay, Bekir Bartin, and Joseph Berechman |
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| Table 1 - Major Cost Categories and Data Sources |
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| Table 2 - Results of Regression of Depreciation Cost |
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| Table 3 - Operating Costs |
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| Table 4 - Contribution Percentages of Operating Cost Categories |
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| Table 5 - Accident Occurrence Rate Regression Analyses Results |
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| Table 6 - Accident Costs by Type |
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| Table 7 - Cost of Each Pollutant Type |
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| Table 8 - Housing Value in New Jersey |
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| Table 9 - Data Fitting for ORMC Values Given for Peak and Off-Peak Hours with Different VOT Values |
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| Table 10 - Full Marginal Cost by Categories for a Trip Distance Range of 9 to 15 Miles |
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| Table 11 - Fuel Prices and Percent Taxes in European Countries |
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| Figure 1 - Hypothetical Marginal and Average Costs of Highway Transportation |
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| Figure 2 - ORMC Calculation Process |
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| Figure 3 - User Cost Categories |
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| Figure 4 - ORMC Distribution with Respect to Trip Distance for Peak and Off-Peak Hours (VOT = $7.6) |
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| Figure 5 - ORMC Distribution with Respect to Trip Distance for Peak and Off-Peak Hours (VOT = $32.3) |
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| Figure 6 - ORMC Distribution with Respect to Highway Functional Type Percentage During Peak Hours for a Trip Distance of Two Miles (VOT = $7.6): Marginal Cost vs. Local-Collector Highway |
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| Figure 7 - ORMC Distribution with Respect to Highway Functional Type Percentage During Off-Peak Hours for a Trip Distance of Two Miles (VOT = $7.6): Marginal Cost vs. Local-Collector Highway |
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| Figure 8 - ORMC Distribution with Respect to Highway Functional Type Percentage During Peak Hours for a Trip Distance of Two Miles (VOT = $7.6): Marginal Cost vs. Minor Arterial Highway |
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| Figure 9 - ORMC Distribution with Respect to Highway Functional Type Percentage During Off-Peak Hours for a Trip Distance of Two Miles (VOT = $7.6): Marginal Cost vs. Minor Arterial Highway |
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| Figure 10 - ORMC Distribution with Respect to Highway Functional Type Percentage During Peak Hours for a Trip Distance of Seven Miles (VOT = $7.6): Marginal Cost vs. Principal Arterial |
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| Figure 11 - ORMC Distribution with Respect to Highway Functional Type Percentage During Peak Hours for a Trip Distance of 25 Miles (VOT = $7.6): Marginal Cost vs. Interstate-Freeway-Expressway |
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| Figure 12 - ORMC Distribution with Respect to Highway Functional Type Percentage During Peak Hours for a Trip Distance of 25 Miles (VOT = $7.6): Marginal Cost vs. Principal Arterial Highway |
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| Figure 13 - ORMC Distribution with Respect to Degree of Urbanization During Peak Hours for a Trip Distance of 40 Miles (VOT = $7.6): Marginal Cost vs. Degree of Urbanization |
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| Guidelines for Manuscript Submission |
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| Back Cover |
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