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4.1 Conclusions

The improvements being implemented by FHWA as part of the continuous improvement model have significant positive impacts on the fuel tax attribution process. These impacts include standardizing and streamlining the data reporting by the states to reduce risk associated with poor quality data. For example, the use of the Smart Input Tool for collecting fuel tax data from the states improves the quality of data (fewer errors, more complete, and timely), saves time and costs, and reduces the risks associated with inaccurate and incomplete data. These improvements greatly enhance the quality and reliability of the entire fuel tax attribution process. A procedure for assessing reliability and consistency of the analytical process was developed based on the stability of the ratios of the attributions to fuel consumption. The use of simple regression analysis and statistical ranges to assess consistency and reliability of the attribution process is suggested.

It can be concluded from the evaluation results that the analytical procedures are fairly reliable in generating consistent HTF attributions to the states.

4.2 Recommendations

The following are recommendations to help improve the fuel tax data attribution process:

• Conduct periodic evaluations. This evaluation was conducted at a time when the full effects of some of the changes being implemented have not been felt (e.g., Smart Input Tool). In addition, some improvements have not been fully implemented (e.g., oversight). Thus, the initial indications of the impacts may not be the same in the long term. It is, therefore, recommended that as part of the continuous improvement model, periodic evaluations be conducted to assess the success of the improvements, identify shortcomings, and design corrective measures. It is only when the problems have been identified that policies and practices can be implemented and/or modified to address them. This approach is consistent with standard risk management procedures.
• Convert the spreadsheets into a simple model. The analysis procedures are currently in a number of spreadsheets that are linked. While the use of spreadsheets provides transparency in the analytical procedure, it can sometimes be cumbersome to execute.
• Action items for further improvements: It is recommended that several actions identified to further improve the fuel tax attribution process be reviewed and implemented. These include:
  • Development of a structured stand-alone Instructions Manual devoted to providing specific guidance on reporting fuel tax data.
  • Introduction of data assessment process tool that enables states to assess the quality of their own prior to submission to FHWA.
  • Use Information Exchange website to address issues related to fuel tax data and also serve as a feedback mechanism.
  • Establishment of mentoring program to assist states that experience consistent data quality problems.
  • Review, enhance, and update of estimation models. Data and assumptions used in developing some of the original models need to be reviewed to determine their validity.
  • Enhancement of analytical framework including EVAL, GTA, MF-20 analyses to adapt to improvements in quality of fuel tax data and estimation models.
  • Development of a framework for systematic monitoring and evaluation of FHWA's continuous improvement programs.