Market-Share Models

Heartland Corridor’s Inspired Scenarios

Scenario #1: Impacts of Shortening Rail Travel Distance on Rail Market Share by Commodity Type

The objective of this scenario is to illustrate the use of the freight mode choice market-share models—discussed in Chapter 8—to assess the impacts of a shortening of the travel distance by rail. To this effect, the team assumed hypothetical flows of cargo that travel from Norfolk, Virginia, to Chicago, Illinois. The scenario assumes that the reduction in distance traveled is similar to the one produced by the Heartland Corridor, which shortened the distance between these cities to 1,031 miles; from 1,342 miles via the Knoxville route, or 1,264 miles to via the Harrisburg route. The analyses focused on the following commodities: electronic, machinery, textiles, miscellaneous, plastics and rubber, meat, alcoholic beverages, animal feed, articles of base metal, printed products, wood products and mixed freight. These commodity types were selected, in part, because they are the commodities with the largest amounts of tonnage being transported in the corridor, and also according to the models that are available to estimate the rail rate that is charged for the shipment which is based on origin and destination state and commodity type. The scenario considers two different cases that differ in the way the average shipment sizes are obtained.

Results: Using the Shipment Size Models

In this scenario, the team used shipment size models derived from CFS microdata in the computation of the freight rates. The impacts on rail market share are shown in Figure 49. The figure shows that the reduction of the rail travel distance leads to increases in rail market share. In the case of wood products (SCTG 26), for instance, the rail market share assuming that all shipments use the Knoxville route is 6.0% and 6.3% if the Harrisburg route is used. Using the Heartland route, the estimated rail market share increases to 7.6% due to the shorter rail travel distances. In general, all commodities exhibited similar behavior though there are differences.

Figure 49

Results: Using the Shipment Size from PUMS

In this scenario, the team used the average shipment sizes from the PUMS data to compute the freight rates. As shown in Figure 50, the reduction in distance traveled lead to an increase in rail market share. However, the rail market shares for some commodities are very different from the ones estimated using shipment size models. The commodities with the largest difference are wood products (SCTG 26) and mixed freight (SCTG 43). Wood products can be explained, in part, due to the small amount of data in PUMS for that commodity (only 35 observations). Mixed freight, being a category with a heterogeneous composition of goods is bound to exhibit different behaviors depending on the actual mix of commodities in the given shipment.

Table 58 summarizes the expected changes in rail market shares between the original routes (i.e., Knoxville and Harrisburg) and the new Heartland Corridor. As shown, the market share increases by a range between 0.1% and 1.6%. This indicates that the shorter travel distance between the two points, due to the Heartland Corridor, resulted in an increased rail market share for the commodities in the analysis. As explained in the introduction to this chapter, the differences between the two sets of results are related to the number of observations in the PUMS data for each corridor.

Figure 50 Table 58

Crescent Corridor’s Inspired Scenarios

Scenario #2: Impacts of Travel Distance on Truck Market Share by Commodity Type

The Crescent Corridor connects through rail network the Southeast with the Northeast region of United States. The objective of this scenario is to quantify the impacts of travel distance and transit time on mode split. Keeping this in mind, an origin and a set of destinations were selected along Crescent corridor. For the analysis, the origin selected was Metairie, Louisiana.

The destination cities were selected based on the rail rate models that were available. The destination cities are organized in order of increasing distance.

As with the Heartland Corridor inspired analysis, the commodity selection for the analysis was based on the top state-to-state commodity flows along the corridor and the available rail rate models. The selected commodities are plastics and rubber (SCTG 24), wood products (SCTG 26), textiles/leather (SCTG 30), base metal (SCTG 32), machinery (SCTG 34), and electronics (SCTG 35). The resulting truck market share—P(T)—for the seven commodities are presented in three ways:

1.      in relation to distance (miles), using great circle distance (GCD);

2.      in relation to truck transit time (hours) which accounts for truck driver hours of service regulations; and

3.      in relation to truck rate (USD) which accounts for number of trucks required to fulfill the shipment.

As previously stated, two methods for estimating shipment size—shipment size models and PUMS data—were used for comparative purposes. The results are presented for both methods in the following sections.

Results: Using the Average Shipment Size Obtained from Shipment Size Models

The following graphs displays the results for the analysis using the shipment size models. Figure 51 illustrates the truck market share for all the commodities included in the analysis. In general terms and as expected, the truck market is relatively high with the exception of mixed freight (SCTG 43). In all cases, there is a slight decrease in truck market shares as GCD, truck transit time, and truck rate increases. However, this drop in truck market share is more noticeable in the case of mixed freight products, which were found by the estimated models to be more sensitive than other commodities to changes in the values of these variables.

Figure 51

To facilitate interpretation of results, the team decided to take out the results for mixed freight as shown in Figure 52. As shown in the figure, plastics and rubber (SCTG 24) is the most sensitive commodity among this group, which starts with a truck market share of 98% (Daphne, AL) and drops to 95% (Dorchester Center, MA). The figures also show that, although the truck market share decreases with these variables, there are segments where it does the opposite, as in the case of P(T) for plastics and rubber for GCD equal to 1,100 miles. The reason is that at these locations, for reasons unknown, the truck rates are relatively higher than at other locations. In summary, the analyses show that the truck market share decreases with GCD, truck transit time, and truck rate. The results for specific commodities are shown next.

Figure 52

Figures 53-59, display each of the commodities individually in order to get a closer look at the market share patterns. Figure 53 presents the truck market share estimated for plastics and rubber (SCTG 24). The origin is Metairie, LA as it is for all the commodities. The destinations along the Crescent Corridor for this commodity, indicated by the markers of on the graphs, are: Daphne, AL; Wetumpka, AL; Birmingham, AL; Atlanta, GA; McDonough, GA; Hinesville, GA; Greenville, SC; Richburg, SC; High Point, NC; Barboursville, VA; Virginia Beach, VA; Baltimore, MD; Monkton, MD; Lincoln, DE; Philadelphia, PA; and Dorchester Center, MA.

Figure 53

Figure 54 shows the truck market share patterns for textiles (SCTG 30). The destinations along the Crescent Corridor used for this commodity, indicated by the markers of on the graphs, are: Daphne, AL; Wetumpka, AL; Birmingham, AL; Mcdonough, GA; Greenville, SC; Richburg, SC; Charleston, SC; Charlotte, NC; High Point, NC; Durham, NC; Roxobel, NC; Richmond, VA; Virginia Beach, VA; Arlington, VA; College Park, MD; Baltimore, MD; Snow Shoe, PA; Newark, NJ; Conway, MA; and Dorchester Center, MA.

Figure 54

Figure 55 shows the truck market share patterns for mixed freight (SCTG 43). The destinations along the Crescent Corridor used for the analysis of this commodity, indicated by the markers of on the graphs, are: Daphne, AL; Wetumpka, AL; Birmingham, AL; Memphis, TN; Antioch, TN; Knoxville, TN; Charleston, SC; Roxobel, NC; Barbousville, VA; and Monkton, MD.

Figure 55

Figure 56 shows the truck market share patterns for electronic and other electrical equipment and components (SCTG 35). The destinations along the Crescent Corridor used for the analysis of this commodity, indicated by the markers of on the graphs, are: Daphne, AL; Wetumpka, AL; Atlanta, GA;, Greenville, SC; Richburg, SC; Charlotte, NC;  Durham, NC;  Roxobel, NC; Richmond, VA; Pittsburgh, PA Newark, NJ; and Dorchester Center, MA.

Figure 56

Figure 57 shows the truck market share patterns for machinery (SCTG 34). The destinations along the Crescent Corridor used for the analysis of this commodity, indicated by the markers of on the graphs, are: Daphne (AL), Wetumpka, AL; Birmingham, AL; McDonough, GA; Durham, NC; Virginia Beach, VA; Camden, NJ; Newark, NJ; and Dorchester Center, MA.

Figure 57

Figure 58 shows the truck market share patterns for base metal in primary or semi-finished forms and in finished basic shapes products (SCTG 32). The destinations along the Crescent Corridor used for the analysis of this commodity, indicated by the markers of on the graphs, are: Daphne, AL; Wetumpka, AL; Birmingham, AL; McDonough, GA; Hinesville, GA; High Point, NC; Virginia Beach, VA; Baltimore, MD; Monkton, MD; Snow Shoe, PA; and Newark, NJ.

Figure 58

Figure 59 shows the truck market share patterns for wood products (SCTG 26). The destinations along the Crescent Corridor used for the analysis of this commodity, indicated by the markers of on the graphs, are: Daphne, AL; Wetumpka, AL; Birmingham, AL; McDonough, GA; Richburg, SC;  Roxobel, NC; Richmond, VA; Virginia Beach, VA; Baltimore, MD; and Philadelphia, PA.

Figure 59

Results: Using the Average Shipment Size Obtained from the PUMS

The results in this section were estimated using a process similar to the one in the previous section, with the difference that the shipment sizes are obtained using the PUMS data instead of shipment size models. As before, the truck market share is presented as a function of great circle distance (GCD), transit time, and truck rate for seven commodities. The results are shown in Figure 60.  In this version of the analysis, the truck market share for plastics and rubber (SCTG 24) is the most sensitive of the commodities to distance, transit time and truck rate.

Figure 60

In order to facilitate interpretation of the results, plastics and rubber (SCTG 24) was removed to get a closer look at the other commodity types, as shown in Figure 61. As expected, mixed freight (SCTG 43 is one of the more sensitivity commodity types towards changes in the presented variables. The results for specific commodities are shown next.

Figure 61

Figures 62 – 68, display each of the commodities individually in order to get a better look at the market share patterns. Figure 62 presents the truck market share estimated for plastics and rubber (SCTG 24). The destinations along the Crescent Corridor for this commodity, as indicated by the markers of on the graphs, are: Daphne, AL; Wetumpka, AL; Birmingham, AL; Atlanta, GA; McDonough, GA; Hinesville, GA; Greenville, SC; Richburg, SC; High Point, NC; Barboursville, VA; Virginia Beach, VA; Baltimore, MD; Monkton, MD; Lincoln, DE; Philadelphia, PA; and Dorchester Center, MA.

Figure 62

Figure 63 presents the truck market share estimated for textiles (SCTG 30). The destinations along the Crescent Corridor for this commodity, as indicated by the markers of on the graphs, are: Daphne, AL; Wetumpka, AL; Birmingham, AL; McDonough, GA; Greenville, SC; Richburg, SC; Charleston, SC; Charlotte, NC; High Point, NC; Durham, NC; Roxobel, NC; Richmond, VA; Virginia Beach, VA; Arlington, VA; College Park, MD; Baltimore, MD; Snow Shoe, PA; Newark, NJ; Conway, MA; and Dorchester Center, MA.

Figure 63

Figure 64 presents the truck market share estimated for mixed freight (SCTG 43). The destinations along the Crescent Corridor for this commodity, as indicated by the markers of on the graphs, are: Daphne, AL; Wetumpka, AL; Birmingham, AL; Memphis, TN; Antioch, TN; Knoxville, TN; Charleston, SC; Roxobel, NC; Barbousville, VA; and Monkton, MD.

Figure 64

Figure 65 presents the truck market share estimated for electronics (SCTG 35). The destinations along the Crescent Corridor for this commodity, as indicated by the markers of on the graphs, are: Daphne, AL; Wetumpka, AL; Atlanta, GA;, Greenville, SC; Richburg, SC; Charlotte, NC;  Durham, NC;  Roxobel, NC; Richmond, VA; Pittsburgh, PA Newark, NJ; and Dorchester Center, MA.

Figure 65

Figure 66 presents the truck market share estimated for machinery (SCTG 34). The destinations along the Crescent Corridor for this commodity, as indicated by the markers of on the graphs, are: Daphne (AL), Wetumpka, AL; Birmingham, AL; Mcdonough, GA; Durham, NC; Virginia Beach, VA; Camden, NJ; Newark, NJ; and Dorchester Center, MA.

Figure 66

Figure 67 presents the truck market share estimated for base metal (SCTG 32). The destinations along the Crescent Corridor for this commodity, as indicated by the markers of on the graphs, are: Daphne, AL; Wetumpka, AL; Birmingham, AL; McDonough, GA; Hinesville, GA; High Point, NC; Virginia Beach, VA; Baltimore, MD; Monkton, MD; Snow Shoe, PA; and Newark, NJ.

Figure 67

Figure 68 presents the truck market share estimated for wood products (SCTG 26). The destinations along the Crescent Corridor for this commodity, as indicated by the markers of on the graphs, are: Daphne, AL; Wetumpka, AL; Birmingham, AL; McDonough, GA; Richburg, SC; Roxobel, NC; Richmond, VA; Virginia Beach, VA; Baltimore, MD; and Philadelphia, PA.

Figure 68

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