000207929 001__ 207929
000207929 005__ 20250401105607.0
000207929 0247_ $$2doi$$a10.22004/ag.econ.207929
000207929 037__ $$a1428-2016-118572
000207929 041__ $$aeng
000207929 245__ $$aSpatial Investigation of Mineral Transportation Characteristics in the State of Washington
000207929 260__ $$c2007-03
000207929 269__ $$a2007-03
000207929 270__ $$mhkhachatryan@wsu.edu$$pKhachatryan,   Hayk
000207929 270__ $$meric_jessup@wsu.edu$$pJessup,   Eric L.
000207929 300__ $$a17
000207929 336__ $$aConference Paper/ Presentation
000207929 520__ $$aHighway construction and maintenance relies heavily upon mined aggregates as a core
ingredient. The proximity of aggregate mine sites to highway or other construction locations is
an important issue since the total project costs are highly affected by transportation
cost/efficiency and also deterioration of the existing highway infrastructure as influenced by
frequent, heavy shipments traveling long distances. Likewise, the transportation costs for
hauling mined aggregates are minimized when shipments are loaded to capacity payload
weights.
This is the first attempt in a series of forthcoming studies to explore mineral shipment
characteristics with a spatial regression model. A comprehensive survey was conducted to
determine both the location and type of need for road improvements. This study investigates the
spatial relationships between construction aggregate shipments and the hauling trucks’ payload
weights as it pertains to highway deterioration in the State of Washington. Many studies have
examined the relationship between transportation cost and construction unit productivity but
there’s minimal information available pertaining to the relationship between payload weights,
shipment distances and highway deterioration.
To identify impacted highway segments resulting from aggregates shipments, mine locations and
shipment distances in cooperation with payload weights are examined. Naturally, spatial nonstationarity
of the data is possible whenever any process takes place over many different
geographical locations. As such, it’s appropriate and necessary to test the mining industry data
for spatial dependences. As a result, the paper employs a spatial error regression model with
distance based weights matrix to address spatial autocorrelation, to capture the interaction
between spatial units and to predict the incremental change in payload weights resulting from
increasing hauling distance. Results show a highly significant positive relationship between
payload weights and increasing shipment distances.
000207929 650__ $$aPublic Economics
000207929 650__ $$aResearch and Development/Tech Change/Emerging Technologies
000207929 650__ $$aResearch Methods/Statistical Methods
000207929 700__ $$aKhachatryan, Hayk
000207929 700__ $$aJessup, Eric L.
000207929 773__ $$d2007
000207929 8564_ $$97f09a998-20d1-4390-a9dd-482bd3ebf512$$s91755$$uhttps://ageconsearch.umn.edu/record/207929/files/2007_7B_SpatialAttributes_paper.pdf
000207929 887__ $$ahttp://purl.umn.edu/207929
000207929 909CO $$ooai:ageconsearch.umn.edu:207929$$pGLOBAL_SET
000207929 912__ $$nSubmitted by Lisa Vang (vang1490@umn.edu) on 2015-08-20T14:40:30Z
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  Previous issue date: 2007-03
000207929 913__ $$aLicense granted by Lisa Vang (vang1490@umn.edu) on 2015-08-20T14:38:26Z (GMT):

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000207929 980__ $$a1428
000207929 982__ $$gTransportation Research Forum>48th Annual Transportation Research Forum, Boston, Massachusetts, March 15-17, 2007