ANÁLISIS DE LA DEMANDA

This chapter uses the results of the previous chapter and explains four methods that can be used to forecast trucks:

• Trend Program (NCDOT Data) • Trend Program (VTRIS Data)

• Average Growth Factor (VTRIS Data and Statistical Checks) • Growth Factor Ratio (VTRIS Data and Statistical Checks) Defining Model Structure

After analyzing and testing the available datasets discussed in the previous chapter, the next step of this research involves defining the methodology for forecasting truck traffic. A subsequent chapter will test the methodology with available data sources, however, the general procedure will be to locate the study area for the forecast, gather available historic traffic data, and supplement the available data with traffic counts that may need to be annualized.

Depending on the location for the highway project study area, data may be available on GIS shape files that can be queried with GIS techniques. A GIS approach may prove advantageous to provide the organization and efficiency of data retrieval to researchers and engineers. The motivation for the methodology with a GIS interface is to facilitate access to truck and other traffic data such as growth factors and nearby regional data.

Trend Program (NCDOT)

The most common approach for traffic forecasts by NCDOT is performed using the Trend Program spreadsheet as described in Chapter Two of this report. The Trend Program update is called the Traffic Forecasting Utility. This method utilizes available data from ADT datasets provided by the Traffic Survey Unit (TSU). Also, the spreadsheet accepts a user defined growth rate, which is usually based on the engineer’s experience and judgment. Figure 4-1 describes the usual process for a traffic forecast conducted at NCDOT using Trend Program.

Percent Duals and percent TTSTs are obtained from classified traffic counts done once every three years or so at selected ATR locations. In most NCDOT traffic forecasts, percent Duals and percent TTSTs are assumed to remain the same from the base year to the future year. In the research methodology, the percentage Duals and TTSTs are not assumed constant. Truck traffic volumes are calculated directly from NCDOT truck counts, VTRIS, and other data. Then truck percentages can be determined based on the usual future year forecast of AADT. A statistical comparison of the forecasted truck values at the project location is also performed after the forecast to determine if they are reasonable.

Trend Program with VTRIS Data

VTRIS data contain consistent annualized truck traffic information for the period 1997-2004. VTRIS data are recorded at Weigh in Motion (WIM) stations and include truck weight information as well as

not fall nearby, then forecasting follows either the Average Growth Factor Method (Figure 4-3) or Growth Factor Ratio Method (Figure 4-4).

Figure 4-1. NCDOT Trend Program Forecasting Procedure (Traffic Forecasting Utility)

Average Growth Factor (AGF) Method

There are five steps involved in the Average Growth Factor (AGF) methodology. They follow a structure similar to that used to forecast traffic using the traditional growth factor strategy of using first and last traffic items in an historical sequence of data. The steps include:

• Selection of project location using GIS • Data collection, analysis, and GIS attributes • Selection of growth rates and percentage trucks • Developing future traffic volumes

• Check for Accuracy

Appendices I to K demonstrate this methodology for several case studies along Interstate, NC, and State Routes in North Carolina. Appendix L presents an interesting case that uses Average Growth Factor to forecast trucks on I-95 in order to locate an “electrified truck stop” to reduce idling and emissions. The results of the case studies demonstrate that the average growth factor method can provide good traffic forecasts.

Following the initial location selection and analysis of the average growth factor, the next step involves locating the station on an existing GIS map and database. This process uses ArcMap to

Figure 4-2. Traffic Forecasting Procedures

Figure 4-4. Growth Factor Ratio Method of Traffic Forecasting

The steps for applying an existing GIS network model or developing one are described below.

Step 1: Selection of project location using GIS

Project location can be easily identified using the geographical capability of GIS. From the GIS shapefile, identify the site using the county shapefile and DOT roads layer. This can be effectively performed by using the “select by attribute” capabilities’ of GIS. Once the project is located, using the VTRIS shapefile, nearby VTRIS/WIM stations can be identified.

For model development the initial step involves collecting appropriate datasets at the project location from NCDOT Traffic Survey Unit. The data available at the project location may or may not consist of historical trends. In cases where historical counts are unavailable, the latest count at the specific location is used and chosen as base year data.

In cases where vehicle classified counts are unavailable, percent Duals and TTSTs should be assumed to be equal to the mean or median value of the data analysis as described in Chapter 3. Depending on the project location, similar locations along the same facility type may be chosen and appropriate growth factors for the facility type can be applied along the same route.

If there are no VTRIS/WIM stations in the county, a similar county with similar socioeconomic characteristics should be chosen. In order to choose that, the GIS display can help in selecting a similar county based on population, transportation warehouses, and income. From a neighboring county VTRIS station, the pattern of growth of Duals and TTSTs can be determined. For a county which has VTRIS station the station growth factors may apply directly if the facility type, facility location and surrounding areas are similar.

The values corresponding to a similar VTRIS station are checked with the mean and confidence intervals obtained for the facility type as explained in Chapter 3 and Appendix G.