Introduction
As discussed in lab 1, there are several issues associated with frac sand mining. This lab will focus on the increased traffic issue. Most roads in western Wisconsin were built for rural, low-freight economies and must now accommodate high volumes of heavy trucks (Hart 8-9). This is because there is an lack of pipelines and rail terminals which leads to an increase number of trucks transporting sand on the roads. The objective of this lab is to calculate a hypothetical dollar amount by county based on the wear and tear the sand trucks put on the roads between their route from the mine to the nearest rail terminal. This will then displayed and discussed using maps, charts, and tables. Because not all sand mines cause wear and tear on the road, there are three criterion which a mine must meet to be included in this analysis:
1. The mine must be active
1. The mine must be active
2. The mine must not have a rail loading station on-site
3. The mine must not be within 1.5 km of a rail line
The mines which met this criterion were queried out by creating a python script which can be found in the python script post.
Methods
Model builder in ArcMap was used to keep track of the workflow for this project. This is shown below in figure 4.0. The model starts in the upper left and then snakes its way around to the bottom. Each chunk of steps is described following the model. |
| Fig 4.0: Model Builder Flow |
First, The Make Closest Facility Layer tool was used to create a network analysis layer which can be used to calculate a constraint such as time or distance. In this case, the constraint is set to time. Then, the mines which met the above criterion in the introduction and the rail terminals were added to the streets network layer so they could be used for network analysis. If there were any barriers such as road closures, this is where they would have been added. Then, the solve tool was used to find the shortest route based on time from the mines to the rail terminals using the streets. These routes were then selected and exported as a new line feature class called Export_Routes.
Step 2: Calculate the Length of the Route by County
Next, the WisconsinCounties feature class was intersected with the Export_Routes. Using the Intersect tool keeps the attributes of both feature classes which will be necessary for the road cost calculation later. Then, because the routes weren't projected they were projected using the Project tool to a state plane Wisconsin coordinate system which has a linear unit of feet. This creates a default field which gives the road length used by the routes for all counties in Wisconsin. Because not all counties in Wisconsin have a route, the Summary Statistics tool was used to organize the data so that the road length by county and county name can be displayed. This was based on the default road length field and county name field. Next, a new field was created to display the road length in miles. The road miles by county were calculated by multiplying the default road length field by 5,280.
Step 3: Calculate the Cost of the Route by County
A new field was created titled CountyCostInDollars. The Calculate Field tool was then used to calculate the road cost. The cost is based off of a hypothetical assumption that for each sand mine there are 50 trucks trips to and from the rail terminal each year, and that the cost incurred by the county for using the roads is 2.2 cents per mile. Using these inputs, the calculation used to determine the cost of the trucks on the roads is the road length multiplied by 100 and then multiplied by .022. This can be seen below in figure 4.1.
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| Fig 4.1: Calculated Road Cost by County per Year |
Step 4: Get the Data Ready to Map the Cost by County
The summarized table was then joined to the Wisconsin Counties feature class so that it could be mapped. The common key used was county name. This was then exported as a new feature class.
Results / Discussion
A chart was created in Excel using the Table to Excel tool. In Excel, the table was simplified so that only the important fields were displayed. The counties are listed in alphabetical order. |
| Fig 4.2: Excel Chart |
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| Fig 4.3: Route Length and Cost Statistics |
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| Fig 4.3: Road Length and Cost Chart |
Lastly, a map was made to show the routes, the sand mines, the rail terminals, the main roads, and the incurred road cost by county. The three counties which had the highest incurred road cost are all located next to each other. Interesting enough, there is only one mine located in Eau Claire county. However, there is a rail terminal there which is the destination for 8 different mines stretching from Jackson to St. Croix county. Compare this to Burnett county, where there is only one mine, and the route in the county totals only .856 miles. The rest of the route extends into Minnesota.
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| Fig 4.4: Road Route and Cost Map |
Based off this map, the most most common rail terminals and the routes which the sand trucks take can be seen. The three most used rail terminals are located in Chippewa, Eau Claire, and Trempealeau counties. This rail terminals should expect a bit more traffic, even if the trucks aren't traveling quite as far as to some other terminals to get there.
Conclusion
Sand trucks have a large impact on the roads in Wisconsin counties. Although, the variables in the calculated road cost equation is hypothetical, the data still provides useful information such as the projected routes sand trucks take from mine to rail terminals which could be used by local governments.It is important to note that some of the road types used in the routes range from county dirt roads to interstates. The interstates are more equipped to handle the increased traffic than the smaller county roads are. If this project was going to be done over again, perhaps the cost incurred by the county should vary depending on the road type.
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