Abstract
This study proceeds from the belief that an examination of railroads and railcar flows provides important insights into economic geography and to the economics of transportation. The flows of wheat in the United States are examined using rail carload waybill survey data in order to model the network routes likely taken from origin to destination, a characterization of the flows themselves, and modeling the flows through the comparison of standard gravity models with two relatively new gravity estimation methodologies suggested in the network analysis literature. The analysis covers Wheat flows through the U.S. for the years 2013 through 2015.
Rail flows are highly conducive to the emergence of high volume corridors, a primary hypothetical relationship described by Taaffe, Morrill and Gould (1963). Hubs are expected to develop in market centers with sufficient traffic, as in a Christaller (1966) system, where market centers above a certain size have terminal facilities, or serve as primary interchange points. Centers of different sizes may also have more than one load center thereby concentrating and attracting more flows (o’Kelly, 1998). Implications are for commodity production and processing locations to develop concentrations of flows and the development of identifiable commodity-specific components of the national rail network. As the railroad nodes are structured in relation to the railroad network, the origin and destination nodes take the form of an irregular lattice (Besag 1974). One implication of this irregular lattice network is the probable presence of network autocorrelation in the flows: the constraints of network connectivity assure flows are not open between the terminals, but travel to and from distinct neighbors on the network. In addition, the network terminal structure can be expected to follow a hierarchical central place ordering. Evidence of such an ordering may be determined by examining the clustering and concentration of flows at nodes and, by association, particular network links serving those nodes (Fujita and Thisse 2002; Hesse and Rodrigue 2004; Nierat 1997). The presence of hierarchies implies economies of scale are being realized which leads to a centralizing or agglomerating effect (Fujita and Thisse, 2002 and Brakman et al, 2001). Thus the transportation sector will also develop an associated central place hierarchy with its own associated network of flows and flow concentrations.
