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<br />! 1 "L'1[jil j:;-K': ('4' 1 't~ "r11J <br />~ J. .... I -', l. ~ '-'..' J. Vi <br /> <br />.. <br /> <br />- - <br />is necessary because we must know both the existing and new truck traffic on <br />each highway segment in order to project highwa.y improvement neeOB. <br />4. Develop a Tnick Traffic Forecasting Model for the Primary Production <br />Region (UGPTI). Grain 1roek 1rips will be predicted for each highway using the' <br />GIS crop.database (T~ 1), historical elevator denlands, results from pr~o-us <br />1roek SUIVeyS, and a Cube prototype model which predicts flows from production <br />zones to elevators and plants. The prototype model-which is develOped in Cube <br />BaseIVayager-will be tailored to the specific objectives of this study. The <br />outcome of this task: will be a detailed prediction of existing grain tru.ck <br />movements within the region and grain tIUck trips on individual highway <br />scgmcm:ts. This baseline flow pattern will be used in all subsequent steps of the <br />analysis. <br />5. Develop DetaRed Historical Tl1lck Trame Data (Shared). UGPTI will utilize <br />d~ed NDDOT vehicle classification data to estimate ttuck ADT for individual <br />types of trucks using state highways. Actual track distributions will be used fur <br />classification stations located Within .the. p~ .produotion region.: J"or state <br />highway segments without classification stations, average vehicle classification <br />data will be used. to factor truck. ADT counts from RIMS into detailed truck . <br />configurations. 'The amsultant will collect similar da.ta fol' county roads. UGPTI <br />will th&m. :integra.te the county and state traffic. data into a comprehensive "database. <br />6. Collect Detalled. HIghway Stnlctural and Geometric: Data for State and <br />County Roads (Shared). Using NDnOT data. UGPTI will develop a'database <br />.containing the current surlace type, strUctural number, and highwa.y geometry for <br />state highways. Similar data will be collected for county roads by the consultant <br />team.. Once the county road data are collected. they will be integrated with state <br />highway data to form a comprehensive database. <br />- 7. Run Model for RelevBIlt Scenarios (UGPTI). Once the highway and traffic <br />inventories are complete and integrated. the Cube truck flow model will be run for <br />all relevant plant output and production scenarios. The prflfClicted incteDlental trips <br />will be assigned to individual segments and added to the baseline trips. Baseline <br />and incremental ESALs will be computed, baSed on. the 1J:UCk-type dis1nbutions <br />from Task 5. The key outcome of this task will be annual ESAL projections for <br />indtvidual segments. . <br />8. Analyze Improvement Alternatives and COSh (Consultant). The possible <br />methods of improving the affected highway segments will be enumerated and the <br />costs and benefits of ea~ will be computed. <br />9. Prepare Report with List of IDpway ;Needs and Benefits/Costs (Shared). A <br />project report will be prepared which includes detailed estimates of: (1) predicted <br />ESALs on affected segments, (2) the costs and bcmefits of improvements. and (3) <br />a deScription of improvemcmt options. UGPTI will provide the consultant with <br />sections of the report related to UGPTI efforts. as descnoed above. The consultant <br />will prepare the conclusions and reoommendations. . <br /> <br />:pJ:"oject TealD. Denver Tolliver. Mark Berwick, and Kurt Johnson will serve as project <br />directors. Alan Dybing will be the primary resea:rch~. Several graduate and <br />undergraduate students will be involved including Subhro Mitra. <br /> <br />F. 4 <br /> <br />. , <br />