Condiciones Generales de Acceso desde Colombia a los Estados Unidos Los flujos de comercio entre Colombia y Estados Unidos y la importante

Truck traffic forecasting is critical for highway and pavement design, but insufficient historical classified truck data makes truck traffic forecasting difficult for highway improvement projects. The traditional method of assuming a constant percentage of truck in base year traffic and future year traffic for Duals and TTST causes concern for design engineers because the constant truck percentage assumption does not reflect the reality of increasing truck volumes. To help rectify this problem this project develops project-level truck forecasting methods that use historical data and replace subjective estimates of truck growth rates with statistically based ranges of expected truck growth rates depending on facility type. The method uses available data from VTRIS and a general GIS shapefile for easy retrieval of data and locations of viable data recording stations.

The growth of Dual and TTST truck traffic on Interstates has been tested using t-tests and proven to be statistically significant at the 90% confidence interval and larger than the growth of light vehicles (Cars). While the findings are based on limited data, they suggest that future traffic forecasts should include and separately forecast Duals and TTSTs.

Findings Methodology

The methodology developed in this project provides a selection of truck forecasting tools and a range of growth factor values that an engineer can use with confidence. It illustrates the importance of choosing a verified growth factor rather than a value solely based on prior experience. Another advantage of the statistical analysis lies in the opportunity to check proposed growth factors.

The proposed methodology for developing truck traffic growth factors and forecasts provides consistent, reproducible, defensible results. The analytical methods incorporate a set of commonly used data sources and techniques to forecast AADT and vehicle traffic by class and facility type. Statistically based growth factors guide the selection of reliable growth factors and percentages of Duals and TTSTs depending on highway type.

The results are based on limited available data. They should be updated as more WIM stations are built throughout the state and more on-line VTRIS data becomes available. The current results reflect 1997-2004 VTRIS data for only 19 rural stations along NC Interstates and 32 rural stations along NC Arterials (other) – US, NC and some SR routes. Also, the VTRIS stations are only located in rural areas. Urban or suburban locations are desirable to expand the applicability of the proposed confidence intervals on expected truck growth rates by facility type.

North Carolina Traffic Trends Based on 1997-2004 VTRIS Data

• Mean AGF TTST is greater than the mean AGF Duals on NC Interstates. • Mean AGF TTST is about the same as the mean AGF Duals on NC Arterials. • The mean AGF for light vehicles (Cars) on Interstates shows a decline.

• At 95% confidence level, there is no significant difference between Dual and TTST truck growth factors and light vehicle growth factors for NC Arterials and NC Interstates.

• According to regression analysis truck growth factors in North Carolina are not a statistically significant function of geographic location, facility type, population distribution and proximity to warehouses.

Products

As a result of the methodology and data used for the project, the following products are available for use at NCDOT.

• Two new traffic forecasting methods (AGF and GFR).

• Statistical tests to check growth factors for traffic by class on rural Interstates, US and NC routes in North Carolina.

• Default growth factors to use where data is sparse.

• A GIS shapefile for Statewide VTRIS (Weigh in Motion) stations (51 WIM and LTPP stations).

• A statewide 1997-2004 spreadsheet and database for the VTRIS archive including classified counts for Cars, Duals and TTSTs. A methodology for integrating tools and data for traffic forecasts.

• A GIS methodology for integrating growth factor methods and data from VTRIS, land use files, the Census and other sources that describe economic trends for traffic forecasts.

• Four case study demonstrations of the growth factor methods.

• A sensitivity study of highway design for lanes and level of service versus truck volumes. • A pavement design sensitivity study of cost per linear foot versus truck volumes.

• An application of GIS and average growth factor methods to locate an electrified truck stop an Interstate.

Conclusions

There are four major conclusions from this research:

• There is statistical evidence that trucks are a significant percentage of vehicles on North Carolina highways, and growth rates for heavy trucks exceed those for light vehicles, especially on Interstates.

• Traffic forecasts should not be based on trends that consider only the first and last years in a sequence of historical traffic data. Rather, each year’s historical traffic data should be included in the calculation of the growth factor.

• Dual and TTST truck volumes should be forecast independently of lighter vehicles for highway projects. Truck percentages should not be assumed equal at the base and future years for the forecast, rather vehicle volumes by class should be calculated or estimated using available data and valid statistical guidelines. Truck percentages should be determined after the total AADT is found by adding the individual vehicle volume forecasts.

• Statistically based guidelines (confidence interval tables) should guide the calculation and selection of growth factors.

Recommendations

- Traffic forecasts for total traffic or any vehicle class should not be based on trends that consider only the first and last years in a sequence of historical traffic data. Rather, each year’s historical traffic data should be included in the calculation of the growth factor. - Dual and TTST truck volumes should be forecast independently of lighter vehicles for

highway projects. Truck percentages should not be assumed equal at the base and future years for the forecast, rather vehicle volumes by class should be calculated or estimated using available data and valid statistical guidelines. Truck percentages should be determined after the total AADT is found by adding the individual vehicle volume forecasts.

- Statistically based guidelines (confidence interval tables) should guide the calculation and selection of growth factors.

• The results of this study, in particular the historical vehicle class data and confidence interval tables, should be updated as more WIM stations are installed throughout North Carolina and as more VTRIS data become available.

• As more traffic data collection resources become available at NCDOT, special attention should be given to secondary highways (NC and SR routes) that are not currently included in the VTRIS database used in this study and that are particularly attractive to permitted and non-permitted over-weight trucks.

• The prototype GIS methodology for processing VTRIS vehicle class data, calculating vehicle class growth rates, and displaying the results with land use and other map images should be more fully developed and demonstrated operationally.

Future Research

This research project has demonstrated the importance of including trucks in traffic forecasts. However, the results, though state of the practice, are limited to traffic forecasts on “isolated” highway links. Highway network effects are not addressed; hence, policy level questions regarding alternative highway improvements and traffic divergence to alternate routes cannot be tested. Also, the methods are simple trend forecasts of past history. There is no accounting for causal factors such as economic, land use, technology or political forces. Furthermore, all forecasts are uncertain, an element ignored in this effort. All these issues are fruitful areas for future research.

Specific future research topics are:

- Develop a program to use annual traffic counts from VTRIS stations to update the confidence intervals and related growth factors for traffic.

- Extend the statistical analysis developed in this study from rural VTRIS stations to urban VTRIS stations and non-VTRIS NCDOT automatic traffic recorder (ATR) stations throughout the state.

- Rewrite the NCDOT Trend Program (Traffic Forecasting Utility) to include the Average Growth Factor and Growth Factor Ratio methods developed in this study. Most importantly include the Confidence Interval guidelines for selecting and checking growth factors.

- Use the recently developed NCDOT Redbook Wizard to integrate traffic forecasts (based on growth factor guidelines developed in this research) and benefit-cost estimates in the feasibility evaluation of highway projects.

- Develop a prototype truck network model for North Carolina. This model would be similar in function to regional or city travel demand models. It could forecast deficiencies in the state truck network at truck and general traffic volumes increase, and it could test alternative improvements.

such as overweight and oversize. Relate the profiles to pavement maintenance, structure design, and funding issues.