@article{Khachatryan:207929,
      recid = {207929},
      author = {Khachatryan, Hayk and Jessup, Eric L.},
      title = {Spatial Investigation of Mineral Transportation  Characteristics in the State of Washington},
      address = {2007-03},
      number = {1428-2016-118572},
      pages = {17},
      year = {2007},
      abstract = {Highway 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.},
      url = {http://ageconsearch.umn.edu/record/207929},
      doi = {https://doi.org/10.22004/ag.econ.207929},
}