4. Summary of Findings
Using the methodology developed under the national analysis of highway performance and demand for freight movement, this study assesses the reorganization impact of performance improvements at a regional level. Panel data analysis indicates that demand for shipping services varies with the expected speed of delivery and that differing levels of variance can be expected in different geographic areas. As highway performance improves, demand for freight movement increases in each region. The impact of improvement is strongest in the Central region. This is possibly due to the greater than average volume of finished and semi-finished goods moved through the Central region.
Table 27 provides a summary of findings regarding the impact of changes in highway performance on freight demand for the three studied regions.
| Region | Coefficient on Delay | Implied Elasticity | Interpretation |
|---|---|---|---|
| East | -0.005117 | -0.0076 | Other things being equal, a 10% increase in delay per mile reduces freight demand by 0.076%. |
| Central | -0.069076 | -0.0175 | Other things being equal, a 10% increase in delay per mile reduces freight demand by 0.175%. |
| West | -0.015586 | -0.0070 | Other things being equal, a 10% increase in delay per mile reduces freight demand by 0.07%. |
Overall, the estimated impact ranges from 0.07% to 0.175% reduction in freight demand (measured by average daily truck traffic) for every 10% increase in measured congestion.
These results should be interpreted with some caution, however, given the important difficulties and data limitations encountered in the course of this project. In particular, a relatively short period of eleven years and various problems with publicly available data (on highway performance, truck rates, and corridor-specific control variables) affected the reliability of regression results.
This study estimates total benefits associated with highway investment by establishing a relationship between highway performance measures and freight demand. An illustrative additive freight reorganization benefit factor was estimated to capture the reorganization impact of highway investments for a typical corridor in each region. This factor is used to estimate the total benefits where the existing data allows estimation of only the direct effects. The additive benefit factor is sensitive to the level of demand and the level of delay. As such, a small rural road without predicted demand post-improvement would have a negligible additional benefit. Moreover, as the additive benefit factor is applied to AADTT travel time savings, non-freight-trafficked roads could be expected to apply a negligible benefit factor to a minimal AADTT savings. Table 28 summarizes the range of the implied elasticities and example additive benefit factors for each region.
| Region | East | Central | West |
|---|---|---|---|
| Additive Benefit Factor | 16.7% | 14.8% | 12.7% |
These results suggest there will be 13% to 17% of indirect benefits in addition to estimated direct freight benefits of highway improvements for a typical corridor, depending on the region of interest.
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