01337549

Component

https://doi.org/10.3141/2256-22

http://scholar.google.com...bath&publication_year=2011

Performance measures are used to communicate the operation of the transportation infrastructure, to build and enhance credibility and accountability, to guide investment, and to support budget and program proposals. Although considerable data are collected on a regular basis, these data have significant limitations and pose challenges for performance management. Experience in assembling the data for an infrastructure index for the U.S. Chamber of Commerce is drawn on to describe the data publicly available to serve as indicators of transportation infrastructure performance, address the shortcomings in the available data, and present the challenges encountered in using the existing data and likely to be encountered in developing new data sources and resources. The transportation index, part of the infrastructure index project (which includes broadband, water, and energy), aims to develop a rigorous, quantitative, and repeatable methodology to capture the performance of infrastructure in order to connect this performance with economic health and prosperity. Four barriers to developing comprehensive performance measures are focused on—varying levels of data aggregation, missing and erroneous data, prediction, and institutional issues—and a summary of data needs is given.

01362484

11-2260

English

pp 191-201

2011

Transportation Research Record: Journal of the Transportation Research Board

9780309222921

Print

Figures (4) ; References (22) ; Tables (3)

Data files; Data quality; Errors; Forecasting; Infrastructure; Institutional issues; Performance measurement; Transportation policy

Data aggregation

Transportation Index

Data needs; Missing data

Data and Information Technology; Highways; I70: Traffic and Transport

TRIS, TRB

Feb 17 2011 6:07PM

• Algorithm for Finding Optimal Paths in a Public Transit Network with Real-Time Data
• Analysis of Weigh-in-Motion Data for Truck Weight Grouping in "Mechanistic–Empirical Pavement Design Guide"
• Automated Quality Assurance Methodology for Archived Transit Data from Automatic Vehicle Location and Passenger Counting Systems
• Axle Load Distribution for Mechanistic–Empirical Pavement Design in North Carolina: Multidimensional Clustering Approach and Decision Tree Development
• Centrality Characteristics of Road Network Patterns of Traffic Analysis Zones
• Counting Motorcycles and Estimating Motorcycle Vehicle Miles of Travel
• Detection and Correction of Inductive Loop Detector Sensitivity Errors by Using Gaussian Mixture Models
• Development of Traffic Inputs for "Mechanistic–Empirical Pavement Design Guide" in Michigan
• Development of Traffic Inputs for New "Mechanistic–Empirical Pavement Design Guide": Case Study
• Discriminant Analysis for Assigning Short-Term Counts to Seasonal Adjustment Factor Groupings
• Estimating Route Choice and Travel Time Reliability with Field Observations of Bluetooth Probe Vehicles
• Estimation of Dynamic Traffic Densities for Official Statistics: Combined Use of Data from Global Positioning System and Loop Detectors
• Evaluation of Nonintrusive Technologies for Traffic Detection, Phase 3
• Forecasting Traffic Loads for Mechanistic–Empirical Pavement Design
• Future Scenarios for Traffic Information and Management
• Identifying and Correcting Pulse-Breakup Errors from Freeway Loop Detectors
• Innovative Real-Time Methodology for Detecting Travel Time Outliers on Interstate Highways and Urban Arterials
• Is Usual Share of Commuting Mode Always Greater Than Its Actual Share?
• License Plate Image Binarization Based on Chromatic Information and Fuzzy C-Mean Approach
• Network Stratification Method by Travel Time Variation
• Travel Time Prediction Using k Nearest Neighbor Method with Combined Data from Vehicle Detector System and Automatic Toll Collection System