Approaches to Presenting External Factors with Operations Performance Measures

Printable version [PDF 2.9 MB]
You may need the Adobe® Reader® to view the PDFs on this page.
Contact Information: Operations Feedback at OperationsFeedback@dot.gov

U.S. Department of Transportation
Federal Highway Administration
Office of Operations
1200 New Jersey Avenue, SE
Washington, DC 20590
www.ops.fhwa.dot.gov

FHWA-HOP-18-0002

February 2018

---

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.

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 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-HOP-18-002		2. Government Accession No.	3. Recipient's Catalog No.
4. Title and Subtitle Approaches to Presenting External Factors with Operations Performance Measures			5. Report Date February 2018
			6. Performing Organization Code
7. Author(s) Bahar Dadashova, Phil Lasley, Pete Koeneman, and Shawn Turner			8. Performing Organization Report No.
9. Performing Organization Name and Address Texas A&M Transportation Institute, College Station, Texas 77843 and Battelle, 505 King Avenue, Columbus, Ohio 43201			10. Work Unit No. (TRAIS)
			11. Contract or Grant No. DTFH61-12-D-00046
12. Sponsoring Agency Name and Address U.S. Department of Transportation Federal Highway Administration Office of Operations 1200 New Jersey Avenue, SE Washington, D.C. 20590			13. Type of Report and Period Covered Final: Oct. 2016 to Nov. 2017
			14. Sponsoring Agency Code HOP
15. Supplementary Notes Government Task Manager: Rich Taylor, Operations Performance Measurement Program
16. Abstract External factors (e.g., fuel prices, economic conditions, employment levels) are those influences that affect transportation system performance but are typically outside the control of transportation agencies. Transportation agencies that wish to effectively manage system performance should understand the nature and extent of this influence of external factors on system performance. Understanding the nature and extent of external factors helps in several elements of performance management, particularly in communication and target setting. This project identified key external factors that can impact performance, and developed recommendations for including external factors in performance reporting. Based on a statistical analysis of available data, the following external factors were identified as being highly correlated with system performance: 1) traffic volume levels; 2) number of employed persons; 3) number of building permits; 4) rental vacancy rate; 5) fuel price index; and 6) economic conditions index. The report also includes recommendations for including external factors in performance reporting. The recommendations include communication and chart design techniques that highlight and reinforce the connection between system performance and external factors. For example, one should illustrate the system performance and external factors on the same time scale on a single chart, preferably with seasonal adjustments applied to better visualize overall trends. The intended message from the chart should be clearly spelled so that readers are not forced to interpret complex graphics. Colors should be chosen that are complementary and easily viewable, even by those with visual impairments.
17. Key Words External factors, transportation system performance management		18. Distribution Statement
19. Security Classif. (of this report) Unclassified	20. Security Classif. (of this page) Unclassified	21. No. of Pages 54	22. Price

SI* (MODERN METRIC) CONVERSION FACTORS

TABLE OF CONTENTS

• LIST OF FIGURES
• LIST OF TABLES
• LIST OF ABBREVIATIONS AND SYMBOLS
• CHAPTER 1. INTRODUCTION
  • FACTORS IMPACTING SYSTEM PERFORMANCE
  • WHAT ARE EXTERNAL FACTORS?
  • WHY SHOULD YOU CARE?
  • PROJECT OBJECTIVES AND APPROACH
  • OVERVIEW OF REPORT
• CHAPTER 2. ANALYSIS OF EXTERNAL FACTORS
  • POSSIBLY INFLUENTIAL EXTERNAL FACTORS FOR ANALYSIS
  • OVERALL ANALYSIS APPROACH
  • ANALYSIS RESULTS WITH HIGHLY CORRELATED EXTERNAL FACTORS
• CHAPTER 3. RECOMMENDATIONS FOR PRESENTING EXTERNAL FACTORS
  • WHAT OTHERS HAVE DONE ALREADY
  • CONSIDERATIONS FOR DISPLAYING EXTERNAL FACTORS
  • OTHER CONSIDERATIONS IN VISUALIZING EXTERNAL FACTORS
  • TYING IT ALL TOGETHER
  • REFERENCES FOR CHAPTER 3
• APPENDIX. IDENTIFYING AND PRESENTING EXTERNAL FACTORS
  • STATE-OF-THE-PRACTICE REVIEW
  • STATISTICAL ANALYSIS OF POSSIBLE EXTERNAL FACTORS
  • PRESENTATION AND CHART BEST PRACTICES
  • REFERENCES FOR APPENDIX

LIST OF FIGURES

• Figure 1. Graphic. TMAS Stations located at the selected 51 MSAs.
• Figure 2. Chart. San Francisco MTA’s Vital Signs Dashboard Segment.
• Figure 3. Chart. San Francisco MTA’s Vital Signs Deep Dive Page.
• Figure 4. Graphic. Washington DOT’s Grey Notebook.
• Figure 5. Graphic. Florida DOT’s Fast Facts Examples.
• Figure 6. Chart. Default Excel Chart Formatting.
• Figure 7. Chart. Same Data as Figure 6, but Improved Visual.
• Figure 8. Chart. Detracting Elements from a Bad Example.
• Figure 9. Chart. Elements of What Makes a Good Visualization.
• Figure 10. Chart. Statistical Analysis Methodology.
• Figure 11. Chart. Time Series and Scatter Plots of Performance Measures.
• Figure 12. Chart. Trend and Seasonality Decomposition of Performance Measures (Austin).
• Figure 13. Chart. Principal Component Analysis of Performance Measures.
• Figure 14. Graphic. PCA for GMP.
• Figure 15. Chart. PCA Plots of MADT
• Figure 16. Graphic. Preattentive Attributes of Visual Perception.
• Figure 17. Graphic. Gestalt Principles of Grouping.
• Figure 18: Graphic. Example of Preattentive Attributes and Gestalt Principles in Action
• Figure 19. Graphic. Sample Complementary Color Wheel.
• Figure 20. Graphic. Ordered and Dual-Ordered Color Palettes.

LIST OF TABLES

• Table 1. List of Possibly Influential External Factors.
• Table 2. Frequency of Available CPI data (2007-2017).
• Table 3. Substitution of CPI Data for Missing MSAs.
• Table 4. Source of Data for Highly Correlated External Factors
• Table 5. Summary Table of Factors Being Recorded by Different Agencies.
• Table 6. List of Selected External Factors.

LIST OF ABBREVIATIONS AND SYMBOLS

AR

Autoregressive Process

ARIMA

Autoregressive integrated moving average

BEA

Bureau of Economic Analysis

BLS

Bureau of Labor Statistics

BP

Building Permits

CEDS

Comprehensive Economic Development Strategy

CHR

Congested Hours

CMAP

Chicago Metropolitan Agency for Planning

CMSA

Consolidated Metropolitan Statistical Area

CPI

Consumer Price Index

DOT

Department of Transportation

DPD

Dynamic Panel Data

DVRPC

Delaware Valley Regional Planning Commission

ECI

Economic Conditions Indices

EF

External Factors

ESS

European Statistical System

FHFA

Federal Housing Finance Agency

FHWA

Federal Highway Agency

FRED

Federal Reserve Bank

GDP

Gross Domestic Product

GMP

Gross Metropolitan Product

HPI

Home Price Index

MADT

Monthly Average Daily Traffic

MPO

Metropolitan Planning Organization

MSA

Metropolitan Statistical Area

MTC

Metropolitan Transportation Commission

NOAA

National Oceanic and Atmospheric Administration

PCA

Principal Component Analysis

PI

Personal Income

PM

Performance Measures

PTI

Planning Time Index

SCAG

Southern California Association of Governments

SEATS

Signal Extraction in ARIMA Time Series

SF

Single Family

TMAS

Travel Monitoring Analysis System

TRAMO

Time Series Regression with ARIMA Noise, Missing Observations, and Outliers

TTI

Travel Time Index

UCR

Urban Congestion Report