CHAPTER 1. INTRODUCTION

BACKGROUND

Annual average daily traffic (AADT) represents traffic on a typical day of the year. It is used by transportation agencies for reporting requirements, allocating resources, informing decision-making, and supporting various agency functions. Transportation agencies use different methods to derive AADT estimates from short-duration counts (SDCs) of traffic data from permanent and portable traffic counting equipment installed at selected locations.

Commonly used methods for estimating AADT do not adequately address how SDCs should be assigned to adjustment factor groups. Also, there are concerns about the inherent errors in these methods, their applicability to roadways with insufficient traffic data, and the accuracy of the derived AADT estimates. There is a need to improve existing methods and develop new methods for functional classes of roadways where insufficient continuous counting exists to improve accuracy of AADT estimates. These methods will help transportation agencies improve the quality of traffic information and support the decisions regarding capital investment programs and budgets as well as design and maintenance programs.

OBJECTIVE

The objective of this research was to develop rational methods for assigning short-duration traffic volume counts to adjustment factor groups for estimating AADT. The research was concerned with all functional classes of roadways and traffic volumes. This research addressed several questions that practitioners frequently ask:

• What are the most (and least) effective assignment methods?
• What are the most (and least) important assignment attributes?
• Is it better to use functional classification or volume factor groups?
• What is the anticipated improvement in AADT accuracy if we use cluster analysis?
• What is the expected increase/reduction in AADT accuracy if we combine two or more assignment methods?
• Can clusters be easily defined from a practical/assignment standpoint?
• Which assignment characteristics should be incorporated in cluster analysis?
• Are there other data-driven methods to assign counts to existing clusters or individual continuous count stations (CCSs)?
• In the case of low-volume roads, what is the expected AADT accuracy of counts factored using adjustment factors from higher functional classes?
• Is it worth factoring counts? If yes, which assignment method should be used and what is the anticipated level of improvement in AADT accuracy?
• Which assignment method should be used to factor counts on low-volume roads if there is no CCS on these roads?
• Can temporal adjustment and axle-correction factors be developed from raw probe data?
• What is the accuracy of AADT estimates derived from SDCs that have been annualized using probe-based factors?

• Can probe-based adjustment factors be used to factor counts taken on lower roadway functional classes?
• What is the relationship between the accuracy of probe-based adjustment factors and the penetration rate of raw probe data?

RESEARCH APPROACH

This research was divided into two phases. The first phase (Phase I) of the project included the following tasks:

• Task 1—Review literature and identify relevant assignment methods. The purpose of this task was to collect and review domestic and foreign literature for research findings and information relative to methods for assigning traffic volume counts to adjustment factor groups for estimating AADT.
• Task 2—Evaluate and identify candidate methods for further consideration. The purpose of this task was to assess the methods identified in Task 1 and select methods for further consideration/investigation in Phase II to (a) improve existing methods and (b) develop new methods for functional classes of roadways where insufficient continuous count data exist.
• Task 3—Prepare Phase II work plan. The purpose of this task was to prepare a detailed Phase II work plan.
• Task 4—Prepare Phase I interim report. The purpose of this task was to document the research performed in Tasks 1 through 3.

Phase II of the project included two tasks:

• Task 5. Develop, validate, and compare existing, improved, and new assignment methods. The purpose of this task was to develop and validate several assignment methods and propose improved and new methods for all functional classes and volume ranges.
• Task 6. Prepare project deliverable. The purpose of this task was to develop the final project deliverable.

REPORT ORGANIZATION

The remaining chapters and appendices of this report include the following:

• Chapter 2—Literature Review. This chapter summarizes findings from the literature review (Task 1), discusses strengths and weaknesses of various assignment methods (Task 2), and provides a general assessment of these methods with respect to several key elements (Task 2).
• Chapter 3—State of Practice. This chapter presents the results of a national survey conducted in Phase I.
• Chapter 4—Methodology. This chapter describes the various assignment methods validated in this project.
• Chapter 5—Study Data. This chapter describes the data used in this study to validate the assignment methods presented in Chapter 4.
• Chapter 6—Results. This chapter presents and discusses the results of the analysis.

• Chapter 7—Conclusions and Suggested Research. This chapter summarizes the answers to the research questions, presents other key findings, and offers suggestions for future research.
• Appendix A—Survey Transmittal Letter and Questionnaire. This appendix includes the survey transmittal letter that was distributed to Transportation Research Board (TRB) state representatives and all 31 questions of the online survey.
• Appendix B—Survey Responses. This appendix summarizes the individual agency responses to each question of the survey.
• Appendix C—Holiday Periods Excluded from Validation. This appendix provides a list of all holiday periods that were excluded from the validation.
• Appendix D— Performance Metric of Traditional Methods for Various Day-of-Week Groups. This appendix shows the grand average results obtained from traditional methods for four day-of-week groups from which sample counts were extracted and validated. The results are aggregated across all states and years.
• Appendix E—Performance Metrics of Traditional Methods and Clustering by State. This appendix shows the main results obtained from traditional methods and cluster analysis. The results are aggregated by state.