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dc.contributor.authorForsythe, Shane
dc.contributor.authorStephens, Jerry
dc.contributor.authorWang, Yiyi
dc.date.accessioned2015-12-28T15:40:22Z
dc.date.available2015-12-28T15:40:22Z
dc.date.issued2015-06
dc.identifier.citationForsythe, Shane, Jerry Stephens, and Yiyi Wang. "Estimation of Seasonal Daily Traffic Flow of Agricultural Products and Implications for Implementation of Automatic Traffic Recorders." Transportation Research Record (June 2015): 18-26. DOI:https://dx.doi.org/10.3141/2477-03.en_US
dc.identifier.issn0361-1981
dc.identifier.urihttps://scholarworks.montana.edu/xmlui/handle/1/9458
dc.description.abstractReliable traffic counts on a highway system are critical for sound decision making about the maintenance, operation, and expansion of the system. Portable short-term automatic traffic recorders (ATRs) are a cost-efficient way to complement traffic counts from permanent ATR sites by performing temporary traffic counts on the highway system. Complicating the collection of traffic data with these short-term devices is the seasonal variation in vehicle operations throughout the year. This work focused on predicting the spatial distribution of seasonal traffic resulting from agricultural activities by using a new method that combines geographic information system spatial functions and the four-step travel demand model. This research collected information about township grids for Montana (as proxies for trip origins), grain elevators (trip destinations), agricultural ground cover, and crop yield estimates to estimate flows in tonnage at the grid level on the road network. Results suggest that the proposed method using the location of major crops and the locations of grain elevators can be used to predict tonnage of product that will be added to individual routes. The predicted values can then be compared with reported heavy-truck traffic to locate sites that may have underrepresented traffic flows. Although this work considered specifically three crops, the method can be applied to any resource flow that has known origin and destination information. The method can be enhanced by refining assumptions of the composition of heavy trucks transporting agricultural products and by field measurements of vehicle flows to better test the validity of the model.en_US
dc.titleEstimation of Seasonal Daily Traffic Flow of Agricultural Products and Implications for Implementation of Automatic Traffic Recordersen_US
dc.typeArticleen_US
mus.citation.extentfirstpage18en_US
mus.citation.extentlastpage26en_US
mus.citation.journaltitleTransportation Research Record: Journal of the Transportation Research Boarden_US
mus.citation.volume2477en_US
mus.identifier.categoryEngineering & Computer Scienceen_US
mus.identifier.categoryLife Sciences & Earth Sciencesen_US
mus.identifier.doi10.3141/2477-03en_US
mus.relation.collegeCollege of Engineeringen_US
mus.relation.departmentCivil Engineering.en_US
mus.relation.universityMontana State University - Bozemanen_US
mus.relation.researchgroupWestern Transportation Institute (WTI).en_US


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