Cover Image

Not for Sale



View/Hide Left Panel
Click for next page ( 45


The National Academies | 500 Fifth St. N.W. | Washington, D.C. 20001
Copyright © National Academy of Sciences. All rights reserved.
Terms of Use and Privacy Statement



Below are the first 10 and last 10 pages of uncorrected machine-read text (when available) of this chapter, followed by the top 30 algorithmically extracted key phrases from the chapter as a whole.
Intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text on the opening pages of each chapter. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.

Do not use for reproduction, copying, pasting, or reading; exclusively for search engines.

OCR for page 44
44 qualitatively based on a market segmentation approach? Modal Assignment Will a logit or other choice model be used? If so, what will If there are modal assignment components, will they be be the form of that model and how will its parameters and validated? If so, how will they be validated? coefficients be developed? Flow Unit and Time Period Conversion Model Application Will the model include a component to covert trip table What are the specific applications of the model? What out- flow units and time periods prior to assigning those trip ta- puts will be obtained and how will they be used and evaluated? bles to modal networks, such as converting annual ton flows to daily truck flows? If so, what will be the form of this con- Performance Measures and Evaluation version and where will the conversion factors be developed or obtained? Will the model be used to support performance measures? What performance measures are being supported? How will they be developed? How will they be used? How will Assignment performance standards or thresholds be established? Will Will the model include the ability to assign modal trip performance measures be developed that are not supported tables to modal networks? What assignment process will by the forecasting model? be used? Will other vehicles using the modal network be included? If there are modal assignment components, will they be 8.2 Case Study Minnesota Trunk validated? If so, how will they be validated? Highway 10 Truck Trip Forecasting Model Model Application Background What are the specific applications of the model? What out- Context puts will be obtained and how will they be used and evaluated? The Minnesota Department of Transportation (Mn/DOT) has identified a system of major highways connecting regional Performance Measures and Evaluation activity centers within the state and designated those highways Will the model be used to support performance measures? as the Interregional Corridor System (IRC). Initially, Mn/DOT What performance measures are being supported? How will chose seven highway corridors to be the focus of an Interre- they be developed? How will they be used? How will perform- gional Corridor Management Plan. One of those seven is ance standards or thresholds be established? Will performance Trunk Highway 10 (TH 10) from TH 24 (Clear Lake) to I-35W measures be developed that are not supported by the forecast- (Mounds View).16 The TH 10 corridor is shown in Figure 8.1. ing model? highway with trucks? How will these additional The IRC Management Plan process included a comprehen- users be assigned in conjunction with freight vehicles? sive technical analysis and public involvement process in order to evaluate existing and future travel conditions, identify defi- Model Validation ciencies, and weigh the various improvement alternatives. Current and future truck activity in the TH 10 corridor was Trip Generation studied through analysis of historical truck data and develop- ment of a truck traffic forecasting methodology that utilized his- If there is a trip generation component, will it be validated? torical truck count data, regional employment data, FHWA If so, how will it be validated? truck trip generation rates, and local truck trip-making activity. The TH 10 study utilized direct flow factoring by applying Trip Distribution economic activity indicators to project future truck volumes. If there is a trip distribution component, will it be vali- This methodology is relatively straightforward and readily dated? If so, how will it be validated? adaptable to other corridors in the Minnesota IRC system. Mode Choice Objective and Purpose of the Model If there is a mode choice component, will it be validated? Modal activity assessment is required under Mn/DOT's If so, how will it be validated? Interregional Corridor Plans. The TH 10 Truck Trip Forecast-

OCR for page 44
45 Source: Minnesota Department of Transportation, TH 10 Corridor Management Plan. Figure 8.1. Trunk Highway 10 in Minnesota. ing Model was developed specifically to assess current and fu- alternatives. Current and future truck activity in the TH 10 ture truck travel demand in the TH 10 corridor, but the corridor was studied through analysis of historical truck data process is applicable to other Minnesota IRC corridors. and development of a truck traffic forecasting methodology that utilized historical truck count data, regional employment General Approach data, FHWA truck trip generation rates, and local truck trip- making activity. This method is appropriate for corridors Model Class where no network-based truck forecasting models exist. The TH-10 model is a direct facility flow factoring class of model. It uses economic variables and existing truck flows to Flow Units directly factor those flows and produce future truck volumes. The TH 10 Truck Trip Forecasting Model estimates daily A detailed description of the direct facility flow factoring class truck trips in the corridor. of model is provided in Sections 4.1 and 6.1. Data Modes Forecasting Data The TH 10 model estimates only truck volumes on the TH 10 highway corridor. BASE AND FORECAST YEAR SOCIOECONOMIC DATA Historical truck traffic data from 1992 through 1999 were Markets obtained to estimate the growth trend in truck traffic along The TH 10 model was specifically built for the TH 10 cor- the TH 10 corridor. ridor, but the methodology is applicable to other corridors in Socioeconomic data included: Minnesota. Industrial employment projections (19962006) for Cen- tral Minnesota and the Twin Cities Metropolitan Area Framework from the Minnesota Department of Economic Security; The IRC Management Plan process included a compre- and hensive technical analysis and public involvement process Labor projections (19902020) for counties within Central designed to evaluate existing and future travel conditions, Minnesota and the Twin Cities Metropolitan Area ob- identify deficiencies, and weight the various improvement tained from the Minnesota Department of Planning.

OCR for page 44
46 The economic forecasts were used to project the number of 2020 truck volumes. Because 2025 was the desired study of future employees by industrial sector within the corridor year, the 2020 projections were extrapolated to 2025. study area. By applying the appropriate truck trip generation Using data from private vendors, businesses along or near rate by sector (truck trips per employee), the associated num- the corridor that generate truck trips were identified and the ber of trucks was estimated. associated number of future truck trips was estimated. Based on future employment at these businesses and the adjusted FHWA truck trip generation rates, the number of truck trips EXTERNAL MARKETS associated with each employer were estimated. By geocoding No external market data was provided. the employment locations and the associated truck trips, high- way segments with high truck volumes could be identified. Modal Networks Software FREIGHT MODAL NETWORKS The methodology developed for the TH 10 corridor relied No travel demand models were used in the TH 10 Truck primarily on spreadsheet calculation (such as Microsoft Trip Forecasting Model. Excel), GIS software such as Business Map by ESRI, and the HarrisInfo database of manufacturers. INTERMODAL TERMINAL DATA Commodity Groups/Truck Types No intermodal terminal data was provided. Trip demand analysis was based on trip generation rates from the Quick Response Freight Manual for 12 industrial Model Development Data sectors. No specific commodity groups or truck types were No model coefficients or parameters were necessary in the specified. TH 10 model. The economic forecasts were applied directly to the existing truck volumes. Trip Generation Trip generation is not included in the direct flow forecast- Conversion Data ing model class. However, the TH-10 model used the Quick No conversion data were necessary in the TH 10 model. All Response Freight Manual trip generation equations to develop truck data are presented and estimated in daily truck trips. the growth rates to be applied to the truck volumes. As shown in Table 8.1, appropriate daily truck trip rates per employee (by sector) were identified using the Manual. Validation Data To estimate truck trips generated within a county, these The model uses existing truck counts directly therefore truck trip generation rates were applied to base and future those truck counts could not also be used for validation. No county employment forecasts by sector. other independent validation data was available. Trip Distribution Model Development Trip distribution is not included in the direct flow forecast- ing model class. The TH-10 model geocoded the manufactur- The model process was to gather and review historical truck ing employment along the corridor and applied the Quick counts in the TH 10 corridor and develop a growth trend pro- Response Freight Manual rates to that location-specific em- file. Projections of future truck trips were developed based on ployment to develop growth factors for individual sections regional employment forecasts (year 2020) applied to the of the corridor. truck trip generation rates from the Federal Highway Admin- istration's Quick Response Freight Manual. The FHWA's truck trip generation rates were applied to existing county employ- Commodity Trip Table ment data to estimate existing truck trips in the corridor. This No commodity trip table was acquired or needed. estimate was compared to observed truck counts, and the trip generation rates were adjusted for use in future year trip esti- Mode Split mation. The adjusted forecast truck factors were applied to 2020 county employment projections to develop an estimate A mode split model is included in this class of models.