Developing the Red River: Facilitating the decision utilizing design of experiment methodology

Developing the Red River: Facilitating the decision utilizing design of experiment methodology | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Developing the Red River: Facilitating the decision utilizing design of experiment methodology Gary Stading, Maryam Kheirandish, Fred Norton This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7236082/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract The Red River is one of the last great rivers in the United States that is only partially developed for transportation and recreational purposes. The continued development of the Red River, however, remains a policy question. The impact of Red River navigability offers opportunities for regional economic development, and the creation of shipping related businesses have the potential to drive capital investment. This paper utilizes design of experiment (DOE) methodology on data from a database provided by the National Waterways Conference to provide information of which decision makers, policy makers, and congressional members can use to evaluate the decision to continue further developing the Red River. The rivers present a good experimental comparison because they are geographically relatively close, they carry similar types of cargo, and they serve similar client bases, including significant agricultural regions of the United States. The results from this experiment show significance in various factors that support the development of the Red River. This study reinforces the importance of heavy cargo loads contributing to decision factors for developing the Red River. This study supports the objectives of the “Marine Highways” Congressional Act with results encouraging the final stages of development for the Red River highway system. Economic development Red River marine water highway system Design of experiments Marine Highways Congressional Act Figures Figure 1 Figure 2 Figure 3 1 Introduction The Red River is one of the last great rivers in the United States that remains not fully developed for transportation and recreational purposes (Kheirandish and Stading, 2024). The National Waterways Conference divides the benefits of waterway development into six major categories: ecosystem restoration, flood control, hydropower, navigation, recreation, and water supply (National Waterways Conference, 2024). Kheirandish and Stading (2024) argue that additional justification for development includes national security and safety. At present, the Red River is only developed from the Mississippi River to Shreveport, Louisiana. This developed portion of the Red River has shown and ratifies the benefits advocated by the National Waterways Conference. The continued development of the Red River, however, remains in question. It runs from Shreveport, Louisiana, into Arkansas, and then follows the borders of Texas and Arkansas before bordering Texas and Oklahoma. Entities presently lobbying for the river development propose that the Red River terminates just north of Dallas, Texas, at Denison, Texas (Stone, 2023 ). Stone presents the benefits of such development, including cargo movement from Dallas with water transportation systems to the Gulf of America ports (e.g., New Orleans, Houston, etc.) for overseas international business shipment. The impact of Red River navigability offers opportunities for regional economic development and the strengthening of national security. The creation of shipping-related businesses and a growing workforce will drive capital investment and an increased wage base. National security is supported and enhanced when military equipment and supplies, which are manufactured and serviced near Texarkana at Red River Army Depot and Lone Star Army Ammunitions Plant, are more easily and efficiently distributed intermodally using developed waterways. Similarly, military equipment manufactured in the Dallas/Fort Worth area from manufacturers like Lockheed Martin and Bell Textron may be transported globally more expediently. Additionally, waterborne freight traffic represents a significant level of opportunity. Currently, freight moves by water at a rate of about 6% of all domestic tonnage (Frittelli, 2020 ). The opportunity to increase the use of transporting freight over the water highway system can off-load the costly transporting of freight using rail or trucking. Trucking, especially, while it has the advantage of point-to-point delivery, also has a costly maintenance structure that only increases when trucks have to deliver heavy cargo loads over the roads. Heavy truck loads tend to break down roads at a faster rate, which increases the cost to rebuild this system. 2 Literature Review The Red River is naturally a shallow river with tight, hair-pinned curves throughout in undeveloped portions of the river. It terminates into the Mississippi River at Simmesport, Louisiana, just north of Vicksburg, Mississippi. In the developed parts of the Red River in Louisiana, the curves have been straightened, and the river has been dredged to make it navigable throughout that state. The same process of development in the lower river can be used to make the river navigable all the way to Denison, Texas. Such a development of the river would effectively provide a water port for the Dallas-Fort Worth metroplex. The Dallas-Fort Worth (DFW) is currently “landlocked” without a waterway access. It is posited in this research that the economic development opportunities of developing the Red River would benefit the entire four state region of Oklahoma, Arkansas, Louisiana, and Texas (Stone, 2023 ). The nature of the shallow draft and the tight curves in the Red River can be partially attributed to the unique natural history of the Red River. The Red River had a very large natural dam form in what is now the Shreveport area. This natural dam is known as the great Red River Raft (Humphreys, 1971 ). This natural dam, some speculate, was the largest in size and the longest lasting known of its kind in the history of the United States and possibly the world. It is speculated that it could have existed from the 12th century to the mid 1830’s, and as long in length as more than 160 miles. It was cleared by the US Army Corps of Engineers from 1829 to 1838 by Captain Henry Miller Shreve, but the raft soon reformed and was cleared a second time in 1873 by Lieutenant Eugene Woodruff (Holbrook, 1946 ). All this is significant because this natural dam formed lakes, bayous and swamps that caused the nature and formation of this river that still exists to this day. 2.1 River Development Developing the Red River and waterways, in general, are evaluated with metrics from a typical perspective of utilizing benefits in such areas as ecosystem restoration, flood control, hydropower, navigation, recreation, and water supply (National Waterways Conference, 2024). However, evaluating waterway development, in general, and the Red River, specifically, researching benefits from a tax perspective is limited (Khaddage-Soboh, et al., 2023 ). This paper explores the question of whether or not the Red River should be developed using these criteria. Khaddage-Soboh, et al. ( 2023 ) develop a case of costs in terms of taxes and rents to the environment and a carbon footprint. Each of these areas (ecosystem restoration, flood control, hydropower, navigation, recreation, and water supply) all contribute to improving the environment by reducing pollution and reducing the carbon footprint. These authors argue that the federal tax perspective should also be relevant for evaluating the benefits of developing the Red River. The tax perspective, environmental tax or financial tax, all result in financial gains as well as environmental gains, but as argued by these authors, these benefits extend to security aspects as well. Kheirandish and Stading (2024) support the environmental aspects of developing the Red River, but also, they begin to explore security as benefit for developing waterway transportation. Despite all the various reasons for developing the Red River, economic development remains the main reason for developing a river system. Bhurtyal, et al. ( 2024 ), in presenting a strong algorithm for identifying best locations for ports to be placed along the Arkansas River, also presented a strong case for developing river freight transportation in general. Jung ( 2011 ) argues that the typical academic debate breaks down into two separate perspectives on economic development. One perspective is that the development of ports on a river system leads to economic development. The second perspective is that economic development in a river system creates environmental, hydropower, recreational and water supply benefits as well as contributing to port growth of existing ports and the need for additional ports. Both types of development, river system development and port development, have a relationship with economic development. Development of ports on a river system leads ultimately to employment, revenue, and social benefits for not only ports but also for geographical areas and the states where the development occurs (Edih et al., 2022 ). Each perspective supports the argument for continued development of the Red River. Funding is, of course, always the main concern of all transportation systems (water, air, trucking and rail). Logistics and transportation costs of all freight movements is always a large cost of the burden placed on the consumer, but these service costs are also necessary for providing the product to the consumer. Transportation costs, particularly maintenance costs, are subsidized by the U.S. government (e.g. trucking is subsidized by roads being build and maintained). In particular, the water transport business is supported by the government in two significant ways. Each provides about 50% of the support. The first half of support is provided by the Inland Waterway Trust Fund, where the funds are provided by a barge fuel tax; the second half of support is provided by the General Treasury fund (Frittelli, 2020 ). River water systems have long been studied for their contribution to economic development as can be seen in seminal work for their ability to carry oil and petroleum products, ores, agriculture products, social and security processes (Hirsch, 1961). Inter Waterways System (IWS), which is the system of inland waterways in the United States supported by a tax structure (Frittelli, 2013 ). The tax structure is used to feed the inland waterways trust fund (IWTF) to maintain the waterway infrastructure and is used to pay half the constructions, which includes maintaining and building new locks and dams. The maintenance and construction of the river system is managed by the Army of Corp of Engineers. The barge carriers in turn support the inland waterways trust fund with a per gallon fuel tax (Frittelli, 2013 ). Lockage fees are also being considered as a source of income. Congress set this system up originally to primarily generate revenue, but also, create healthy competition for transporting products with the rail system. In the development of natural resources Khaddage-Soboh, et al., ( 2023 ) make the case that in, many different ways, taxes make a big difference in justifying the development of those resources. While Khaddage-Soboh, et al., do not necessarily address the taxes in the traditional sense of income to the government, they do essentially make the case that taxes can reveal themselves as costs, especially when tax dollars are spent to clean up the environment. In the case of waterway development, this type of waterway development leads to a cleaner environmental footprint. Khaddage-Soboh, et al., make the case that type of cost is an imperative in public policy. There is a strong propensity in the literature to support both economic development and to develop natural resources, especially in developing countries (Li, et al., 2021 ). In developing countries, Li, et al., discussed the difficulty of developing natural resources with the purpose of building a strong financial economy. Khaddage-Soboh, et al., ( 2023 ) make the case that renewable energies improve the environment and encourage sustainable practices. Kheirandish and Stading (2024) argue that developing waterways result in the types of environmentally sound growth that is desired including providing expanded natural habitats, especially when compared to other forms of transportation modes. While it is generally acknowledged that oil and gas production from a major oil field like the Permian basin is dependent and best transported by pipelines, the materials to produce that oil do not flow through pipelines (Burns and Sirisomboonsuk, 2022). A single well takes 500 tons of steel, 365 truckloads of sand, over 35 Olympic sized swimming pools worth of water (Collins, 2018 ). Collins states that moving oil crude away from a well head requires over 2,700 truck loads, which is over 500,000 barrels of crude. Transporting these types of goods by truck destroys roads and makes them not only dangerous, but also costly to maintain. The civic tax on this type of work is high. These products are not pipeline products. These types of products are good examples of products that should be moved as close to the Permian Basin and other commodities and oil producing areas via water transportation as possible (Asborno, et al., 2020). Red River development is a natural consideration for the oil fields of West Texas and Oklahoma, especially if supported by the data as proposed by the research question addressed in this paper. The inland waterway supports the transportation of heavy raw materials. These include items like grain, coal, sand, petroleum, and construction materials (Frettelli, 2018). These are exactly the types of materials that are transported in and out of the oil fields that populate Oklahoma and Texas. These are also the materials that serve the agriculture industry of Oklahoma and Texas. These are also materials that are heavy and cause degradation on the road systems. If a port were developed on the Texas-Oklahoma border just north of Dallas, the return on investment (ROI) should support the costs of maintaining the river transportation system. One example is provided by Xu, Lu, and Song (2024). Xu, et. al., apply a tripartite evolutionary game model to a liquid natural gas (LNG) problem to evaluate a government strategy to attain a carbon tax benefit. While transporting LNG is generally relative to deep water shipping, the application is relative to the decision of developing the Red River. Xu, et al., evaluate the strategies of the government, the shipping company, and the energy company using the carbon tax benefit as the measurement. The carbon tax mechanism has been used in various applications and is a widely used practice in the following applications: pollution levies, subsidies, and emissions trading (Sheng, et al., 2021; Li, et al., 2022). 2.2 Research Contribution The factors that drive economic development, especially in water port regions, continue to be studied (Bhurtyal, et al., 2024 ). The factors, which have shown evidence of support for port development include marketing, information and data analysis, manpower labor, industrial structure, regional innovation capability, the degree of how a region is “opened up,” and finally financial support from both governmental sources and economic foundations (Kong and He, 2020). Kong and He make the point that to calculate the comprehensive growth factor rate, labor input index information and capital input index information, and output index information must be documented and measured. The main point is that growth is achieved through the economic development process, which results in increased employment opportunities, increased revenues into a region, and increased regional product output (GDP) as the result of the economic development. Burtyal, et al. (2024) clearly demonstrate the benefits of economic development in the case of the Arkansas River. This paper extends this research to measure the benefits of extending the Red River by using Design of Experiment (DOE) methodology to compare the fully developed Arkansas River with the portion of the Red River, which is partially developed, to measure the potential results of completing the development of a Red River extension. 3 Methodology This study is an exploratory study on the data from the National Waterways Conference ( 2024 ). This study compares data from two rivers: The Red River (known as the J. Bennett Johnston Waterway or JBJ Waterway) and the Arkansas River (known as the McClellan-Kerr Arkansas River Navigation System or MKARNS). The development of the next section of the Red River is formally known as the Southwest Arkansas Red River Navigation (SARRN) Channel. Comparing rivers for their effect on economic development is not a new phenomenon. Stoeckl, et al. ( 2013 ) similarly studied two rivers in Australia for their potential impact on economic development. The data in this study is collected from actual shipping data as reported into a national database from the National Waterways Conference and explains the traffic on the Red River and Arkansas River. The Arkansas River Basin spans 1,469 miles from the Rockies to the Lower Mississippi, offering recreational activities and navigation via 18 locks and dams that also support power and flood control. In contrast, the Red River Basin, marking much of the Texas–Oklahoma border before emptying in Louisiana, is managed under the Red River Compact to balance agricultural, urban, industrial, and ecological water needs. The Red River navigation begins in Shreveport, Louisiana and terminates into the Mississippi River just north of Vicksburg, Mississippi. The first notable relevant point from the data is that the state of Oklahoma currently moves a little over $ 180B of various products by truck, and this mode of transportation is growing annually at a rate of over 7%. Rail is moving about $ 16B worth of product, and it is growing at a rate of a little over 9.5%. Water transportation does not appear as a category from Oklahoma, reflecting the state’s current lack of a fully developed, high-capacity port. Most of the leading products being transported include those types of bulk and heavy break-bulk items that extol a large environment tax, a large carbon tax, and a large civic tax (road repairs, etc.) Comparatively, Arkansas moves about $ 141B annually and is growing at a rate of about 8% annually. Rail moves about $ 7.5B annually and is growing at a little over 7.5%. Arkansas has pipeline traffic, and it moves a little over $ 26.5B worth of product annually. The pipeline traffic has grown, but it appears to be flattening out, indicating that it may be reaching capacity, and the construction of additional pipelines is limited. Product moving on the Arkansas river is a little over a half a billion dollars per year and is growing at almost 9% annually. While the Arkansas River supports efficient, year-round barge transport, the Red River plays a smaller role in commercial transportation. Figure 1 shows the 10-year traffic of both rivers from 2013–2022, illustrating consistent utilization of the Arkansas River and highlighting the Red River’s potential for expanded freight movement once infrastructure and navigational enhancements are in place. Figure 1 About Here This study employs a three-factor, two level DOE analysis to determine if further development of the Red River could offer a valuable alternative route for regional supply chains, reduce congestion on existing corridors, and lower transportation costs. The three-factor analysis compares Arkansas River and Red River traffic assessing the traffic volumes growth over a five-year span and evaluating differences across various voyage types. The three factors include Rivers, Traffic, and Years and the levels are included below next to the factors. River Factor Levels are Red vs. Arkansas (X axis) Traffic Factor : Levels are Voyage Types: Shipments (and through) vs. Receipts (and i ntraportal) (Y axis) Time Period Factor Levels are the 5 year period from 2018 through 2022 vs. the 5 year period from 2013 through 2017 (z axis) Designed experiment methodology utilizes regression analysis to assess the results of the data (Stading, et al, 2001 ). Similarly, Khaddage-Soboh, et al., ( 2023 ) use regression analysis will be used to analyze the designed experiment results. DOE methodology has the capability to identify the significant differences between the factors that will indicate if developing a river for traffic transportation is of the value necessary to justify the development for regional economic development (Stading, et al, 2001 ). Design of Experimentation (DOE) methodology has been used in a number of applications including to simulate data when a lack of data is present or when conditions are not safe to run designed experiments (Petsagkourakis and Galvanin, 2021). In such cases, Gaussian Processes (GP) are used to estimate constraints and expected values of objective functions. Nimmeegers, et al. (2020) utilize confidence regions estimated from parameters collected, which is more robust DOE. With data available from Inland Waterways, parameters are available from which to build estimates. This provides a robust analysis to predict the outcomes of developing a river system. 4 Data Analysis The data in this study is analyzed using design of experiment methodology. The data is provided by the National Waterways Conference ( 2024 ). The data is collected from actual shipping data as reported into a national database. These sources of data provide an excellent basis for exploring the advantage of developing the Red River. The model will be refined as new studies provide updated information. To analyze if there are advantages to additional development of a waterway, the DOE will lay out in the current configuration with levels mapped out in Fig. 2 below. Figure 2 About Here The levels for each factor are depicted in Fig. 2 . The river factor compares the currently partially developed Red River traffic with the fully developed Arkansas River traffic. The second factor compares shipping product upriver versus shipping product down river. Finally, the third factor compares two different 5-year periods to see if growth is occurring in the shipping patterns. The results of studying this data should provide insights to better understand the advantages of continuing the development of the Red River. The component which cannot be tested for, of course, is the volume of traffic which may occur when the Dallas-Ft. Worth (DFW) urban area gains access to a waterway. The reason this cannot be studied in parallel is because the Arkansas River just simply does not have access to the size of the market equal to the DFW urban region. Additionally, the Arkansas River also does not have access to the same oil field regions as a developed Red River would have. Design of experiments (DOE) is a common analysis technique using Gaussian process with a trust region when utilizing existing databases or to substitute missing data to compute the results in the absence of controlled experimental data (Petsagkourakis and Galvanin, 2021). Gaussian principles are used to quantify the uncertainty of the physical system and to calculate the optimal experimental design while accounting for the parametric uncertainty present in the model with existing data (Petsagkourakis and Galvanin, 2021). Ultimately, this analysis technique provides results with the probabilistic satisfaction of the constraints of the design of experiments model. 5 Results The results from the design of experiments analysis of the data provide insight into the significance of various decision factors referenced in evaluating the decision to develop the Red River. The model incorporates various inputs and data including volumes of products shipped in and out of river port destinations which decision makers (including congress members) use to evaluate the decision to develop the Red River. These results include business and economic data that drives the development of a river for the transportation and environmental needs of the people within a region and for the whole country. The first set of results are analyzed with the average and standard deviations of each level. Annual total shipping values are included as well for perspective. Figure 3 below shows these results: Figure 3 About Here The data is first examined by using a t-test pairwise comparison of the main effects. The results support the idea that growth over time is achieved as river shipping is further developed. Evaluating the data on the first individual axis, the river differences shows statistical differences in Table 1 below. Table 1 t-test: two-sample test between the Red River and the Arkansas River Red River Arkansas River Mean 1850678.9 2690906.275 Variance 3.17652E + 12 3.26385E + 12 Observations 40 40 Hypothesized Mean Difference 0 Df 78 t Stat -2.093975167 P(T < = t) one-tail 0.019755173 t Critical one-tail 1.664624645 P(T < = t) two-tail 0.039510347 t Critical two-tail 1.990847069 Table 1 About Here The Arkansas River is more fully developed, while the Red River is only partially developed. The p-statistic shows that the tonnage shipped on the Arkansas River is statistically larger than the tonnage that is shipped on the Red River. The next factor evaluated is the differences between five-year time periods. While, just simply looking at average tonnages by year, it looks like there may have been a decline in shipping. However, a significant difference between these groups is marginal on the pairwise comparison (t-stat is below the critical t). See Table 2 below. Table 2 t-test two-sample test between five year time periods 2013–2017 2018–2022 Mean 2441493.625 2100091.55 Variance 3.85573E + 12 2.8869E + 12 Observations 40 40 Hypothesized Mean Difference 0 Df 76 t Stat 0.831536412 P(T < = t) one-tail 0.204137849 t Critical one-tail 1.665151353 P(T < = t) two-tail 0.408275697 t Critical two-tail 1.99167261 Table 2 About Here The average amounts shipped in each time period is well withing the expected variance of normal amounts of shipping. It also supports the idea that river shipping is somewhat timeless. The need for river shipping of the types of products that use river shipping will continue to use that mode of transportation over time. The next main effect being measured is if there is a difference between product moving up or down the river. Product moving upriver is considered a receipt at river ports. If the product is being shipped downriver it is considered a shipment. The significance for this pairwise comparison effect is provided below in Table 3 . Table 3 t-test for the two-sample test for shipments vs. receipts Receipts Shipments Mean 2526991.45 2014593.725 Variance 2.75232E + 12 3.91545E + 12 Observations 40 40 Hypothesized Mean Difference 0 Df 76 t Stat 1.255009411 P(T < = t) one-tail 0.106660404 t Critical one-tail 1.665151353 P(T < = t) two-tail 0.213320809 t Critical two-tail 1.99167261 Table 3 About Here Table 3 challenges that there may be marginal significant differences of product being shipped or received. The p-value shows that there may only be marginal significant difference in these two directions of shipment. This makes sense because a shipping company would not want to move empty barges up or down river if at all possible, supporting the idea that backhauling is targeted as much as possible on river systems. Moving to the analysis of the experimental design, the significance of these data points does confirm statistical significance at each main effect. The full Anova shows the results in Table 4 below: Table 4 Anova results for the full factorial experiment Effect Sum of Squared Degree of Freedom F Statistics Probability(> F) River (Arkansas river Vs. Red river) 52.22e + 14 1 190.09 3.00e-15 Type of Shipment (Receipts in Vs. Shipments out) 26.18e + 14 1 95.30 2.94e-11 Year Span (2013–2017 Vs. 2018–2022) 4.01e + 14 1 14.61 5.55e-4 River_Type of Shipment 23.58e + 14 1 85.83 1.05e-10 River_Year Span 1.17e + 14 1 4.27 0.04 Type of Shipment_Year Span 20.92e + 11 1 0.07 0.78 Residual 9.06e + 14 33 Table 4 About Here Each of the main effects in Table 4 shows statistical significance. However, the main river effect showed the strongest effect supporting the argument that the fully developed river does result in significant differences in moving freight over a less developed river. The other main effects are showing some level significance but considerably less. Moving to the interaction effects, the interaction between the river and either cargo type or time period effect shows some limited significance. This indicates a strong contribution in effect from the differences in rivers, thereby, continuing to support the notion that the more developed river generates economic development. The results are provided in Table 5 below: Table 5 Regression of the full factorial interactions Linear Regression Model Summary: R-squared: 0.922 Adj. R-squared: 0.908 F-statistic: 65.03 Prob (F-statistic): 7.25e-17 Term coefficient t statistics P>|t| [0.025 0.975] Intercept 3.01e + 06 36.32 0 2.84e + 06 3.18e + 06 River (Arkansas river Vs. Red river) -1.14e + 06 -13.78 0 -1.31e + 06 -9.74e + 05 Type of Shipment (Receipts in Vs. Shipments out) -8.09e + 05 -9.76 0 -9.78e + 05 -6.40e + 05 Year Span (2013–2017 Vs. 2018–2022) -3.17e + 05 -3.82 0.001 -4.85e + 05 -1.48e + 05 River_Type of Shipment -7.68e + 05 -9.26 0 -9.36e + 05 -5.99e + 05 River_Year Span 1.71e + 05 2.06 0.047 2.68e + 3 3.4e + 05 Type of Shipment_Year Span -2.29e + 04 -0.27 0.784 -1.91e + 05 1.46e + 05 Table 5 About Here 5.1 Summary of Results The ANOVA results have shown significance with each of the main effects and some interaction effects. However, the strongest or most significant of the results is that a fully developed Arkansas River provides more freight transportation than a partially developed Red River. The interactions in the ANOVA also show the most significant interaction at the 0.05 level between the river along and the type of cargo shipment. The other two main effect interactions are also significant at a slightly weaker level. The regression results show an adjusted R-squared to be 0.99. This is a strong support that the regression model is a good predictor of the results. The specific interaction breakdowns showed decent adjusted R-squared results. After completing the regression, the t-test comparison of each of the main effects, the DOE main effects, and the interaction effects support further development of the Red River. In summary, the main effect of the river differences is significant. On this test, the type of shipments and the differences in years show that, while these main effects are significant from the ANOVA, more testing may likely be needed to firmly settle on these conclusions. Ultimately, the significance of each of the interactions may be explained by the size and strength of the numbers from the main effects, which could possibly be overpowering the measured interaction effects. More tests are required to determine if each of the interaction significances are real. However, due to the individual measured results of the main effects, in the end, these results support developing the Red River. 6 Discussion The results provide insight into the importance of the decision to develop the Red River. The model is useful for evaluating further development of the Red River by key decision makers who are primarily congressional representatives from the various constituencies representing the states of Louisiana, Arkansas, Texas, and Oklahoma. Since congressional representatives are not generally considered experts on river development, this model is a valuable aid by putting test results from relevant data into the hands of decision experts. The multiple regressive design of experiments analysis supports a decision to develop the Red River. The test results showed that developing the Arkansas river increased freight transport up and down the river to achieve higher levels of trade to ports and locations up and down the river. Similarly, the product transports up and down the Red River to the portion of the Red River that has been developed showing a productive level of traffic on those river ports. The freight moving up and down the Red River is about half the freight moving up and down the Arkansas River. This makes sense because approximately half the Red River has been developed. The analysis supports the idea that all three main effects are significant as seen in the p-value results. The main effect of the most significance is that river transport is greater on the more developed river especially since developing the Red River provides access to larger cities in the region (e.g. Dallas, Fort Worth, Oklahoma City). In the case of the Arkansas river, it provides freight transportation access to Little Rock, Arkansas. In the case of the Red River, developing it will provide river freight access to a much larger metropolitan area, specifically the Dallas-Fort Worth (DFW) metroplex. In addition, developing the Red River will provide river transportation to move oil and gas equipment and supplies access to the oil fields of Oklahoma and West Texas. Most of the heavy items being transported to the oil fields are currently being shipped by truck. Truck transport with these types of cargo and in these quantities result in significant costs, especially to the publicly maintained infrastructure. Heavy loads in these quantities require a significant amount of maintenance costs especially to road repair and maintenance. One important aspect of developing a river for navigation includes also the development of port operations. One of the key measures of port operations is the dwell time of cargo (Frittelli, 2020 ). Congress is concerned about these measures. There are many factors that affect dwell time, but one important point is that new ports also bring new and more efficient operations, especially since those ports place importance on new and efficient processes to improve the current cargo dwell time measures. The main key performance indicator includes the amount of time to load and unload the cargo. New operational methods will be available and provided for the new ports, which will be developed when the Red River navigability gets developed. These new ports will include new and improved efficiencies that help achieve the kinds of throughput velocity for loading river barges necessary to compete in today’s marketplace. Revenues are generated for river traffic through various ways. First, barge operators pay a fuel tax through the Inland Waterways Trust Fund (IWTF) that covers the cost of new projects and major rehabilitation projects (Frittelli, 2018 ). This fund covers about half the cost of operating the waterways. The other half is covered by general funds. This study provides strong evidence that that the development of this leg of the Red River can generate economic development and commercial cargo use of the Red River that will provide the return on investment (ROI) that will make this project a revenue generator. Kong and He (2020) select various financial criteria to measure the success of an investment for economic development of a waterway representing the regional GDP. These criteria include expenditure and budget from local expenditures, proportions of value or secondary and tertiary industries to GDP, year-end residential savings, and electricity consumption per unit of GDP. These criteria are relevant for the development of the regions measured in this study. While there is not necessarily a way to measure these items directly in this study, this study does produce results that support positive correlations in each of these categories, especially in evaluating the types of cargo loads that address these revenue sources. The designed experiment results provide evidence that the shipments of products that are statistically significant in increased volume including both agricultural goods and oil and gas support items producing the types of value that improve the GDP in each of these categories. Kong and He (2020) make a request to the research community for researchers to verify their results on ports driving economic development using data and designed experimental results. This research answers that call. Kong and He found through their theoretical research that factors contributing to economic development through ports and waterways development include labor input, capital input and a growth factor using GDP as the measurement. The research presented in these results supports the idea that heavy loads of goods are best transported through water transportation, and that, the transport of these goods using waterways does help the economic growth of the regions supported by these waterways, including those specific to the port cities. 7 Conclusion The Red River is one of the last rivers in the United States that is not fully developed for freight transportation. The research question is whether or not the Red River should be developed to a point just north of Dallas. The results showed that through an experimental test comparison of an existing database provided by the National Waterways Conference that a partially developed river like the Red River to a fully developed river like the Arkansas River results in shipping benefits for fully developing the Red River. The rivers are a good comparison because they are geographically relatively close, they carry similar types of cargo, and they serve similar client bases, including significant agricultural regions of the United States. The potential of developing the Red River is even greater than the fully developed Arkansas River because the Red River has the potential of serving at least one major metropolitan effort of the Dallas – Fort Worth region; serving a second major metropolitan effort of Oklahoma City; and, also, serving a major oil industry in Texas and Oklahoma with many products that may be used in oil fields. The results show significance in various factors that support the development of the Red River. This study reinforces the importance of heavy cargo loads contributing to decision factors for developing the Red River. This study also demonstrates how water development to carry heavy loads contributes with lesser studied tax and security factors. The results of this research and the model do provide encouraging evidence that Red River development should proceed. The Red River’s development will result in significant economic, environmental, and social benefit for the region. It will drive significant transportation development which will help with the economic development of the region. The Office of Management and Budget (OMB) will not request funding unless the economic benefit is 2.5 times the cost (Frittelli, 2018 ). The projects which are projected to have this kind of return are focused in three or four areas: the Ohio and Tennessee River Valleys, the Gulf Intercoastal Waterway with its petrochemical industry focus, and the agricultural heartland. The development of the Red River addresses the need for 2 of the top priorities for the OMB. Developing the Red River provides an artery directly into a major portion of the agricultural heartland providing access to Arkansas, the Oklahoma, and the Texas rich agricultural economy. Equally as important, developing the Red River to Denison, TX also provides an artery into the rich gas and oil economies of Oklahoma and West Texas. The need to develop the river highways is recognized in Washington D.C. The “Marine Highways” act from Congress in 2007 was passed acknowledging the need to divert rail and truck freight to the water highways (Frittelli, 2020 ). Rivers are one of the most efficient forms of freight movement (Bhutyal, et al., 2024). The “Marine Highways” act even provided funding for two of the largest costs in developing the marine highway. This funding is in the form of grants for upgrading terminal equipment and vessel upgrades. In 2012, Congress expanded the act to include all U.S. ports and marine loading and unloading operations of containers between large vessels and smaller vessels (Frittelli, 2020 ). This study supports the objectives of these Congressional acts in demonstrating the advantage of developing a river highway which results in significantly larger cargo shipments by utilizing the river highway for heavy cargo shipments. In summary, when each of these factors are fully considered, developing the Red River to the point just North of Dallas on the Texas – Oklahoma border will result in meeting the goals that are the criteria for economic development, including revenues generated through taxes from the transporting using river waterways. The heavy loads carried by this water traffic will address the regional needs of Oklahoma, Texas, and Arkansas including the important agricultural and oil and gas products necessary for this region in the country. Furthermore, the types of economic development which will occur will meet the criteria established by government initiatives including economics 2.5 times over the cost of development (Frittelli, 2018 ). 8 Limitations A pre-existing database was used to conduct the analysis (The National Waterways Conference, 2024 ). As with all pre-existing databases, the major limitation is that the researchers do not have control of the data collection. The collection of data always involves a certain amount variation in the collection of that data. Without being a part of the collection process, the sources of potential variation are unknown. The positive contribution for using existing databases, especially this database, is that it is a generally accepted and “official” database vetted by a trusted organization (The National Waterways Conference, 2024 ). The data represents data collected from traditional and generally accepted sources. This type of data, while it may originate from sources not considered “laboratory” controlled experimentally designed sources, the data does represent top lined summarized data that generally has uncontrolled variation “washed out.” Therefore, this data, while not ideal, produces reliable and trustworthy results. Declarations Author Contribution G.S. was primary authorM.K. was the primary data analyst and author of the results sectionF.N. was an editor Data Availability The data is resident at The National Waterways Conference (2024). Arlington, VA, https://waterways.org/. References Asborno, Magdalena I., Sarah Hernandez, and Taslima Akter. 2020. Multicommodity Port Throughput from Truck GPS and Lock Performance Data Fusion. Maritime Economics & Logistics , 22 (2), 196-217. https://doi.org/10.1057/s41278-020-00154-7. Bhurtyal, Sanjeev, Sarah Hernandez, Sandra Eksioglu, and Manzi Yves. 2024. A Two-Stage Stochastic Optimization Model for Port Infrastructure Planning. Maritime Economics & Logistics , 26,185-211. https://doi.org/10.1057/s41278-023-00262-0. Burns, James R. and Pinyarat Sirisomboonsuk. 2022. Applications of System Dynamics and Big Data Oil and Gas Dynamics in the Permian Basin. International Journal of Business Analytics , 9 (1), 1-22. Collins, Gabriel. 2018. Addressing the Impacts of Oil and Gas on Development on Texas Roads. Testimony to the Texas House of Representatives Transportation Committee, Rice University’s Baker Institute of Public Policy, https://www.bakerinstitute.org/research/addressing-impacts-oil-gas-development-texas-roads. Edih, University O., Fidelia Igemohia, and Nyanayon Faghawari. 2022. The Effect of Optimal Port Operations on Global Maritime Transportation: A study of Selected Ports in Nigeria. Journal of Money and Business , 2 (2), 173-185. Frittelli, John. 2020. Federal Freight Policy: In Brief. Congressional Research Service , R44367, (14), 1-13. https://crsreports.congress.gov. Frittelli, John. 2018. Prioritizing Waterway Lock Projects. Congressional Research Service, R45211, (3), 1-17. https://crsreports.congress.gov. Frittelli, John. 2013. Inland Waterways: Financing and Management Options and Federal Studies. Congressional Research Service, R43101, (5), 1-12. https://crsreports.congress.gov. Hirsch, Abraham, M. 1961. Some Aspects of River Utilization in Arid Areas: The Hydro-economics of Inadequate Supply. The American Journal of Economics and Sociology , April, 271-286. Holbrook, Stewart. 1946. Lost Men of American History. The Macmillan Company . Humphreys, Hubert. 1971. Photographic Views of Red River Raft, 1873. Louisiana History: The Journal of the Louisiana Historical Association , 12 (2), 101-108. Jung, Bong-min. 2011. Economic Contribution of Ports to the Local Economies in Korea. Asian Journal of Shipping Logistics, 27, (1), 1-30. Khaddage-Soboh, Nada, Adnan Safi, Muhammad F. Rasheed, and Amir Hasnaoui. 2023. Examining the Role of Natural Resource Rent, Environmental Regulations, and Environmental Taxes in Sustainable Development: Evidence from G-7 Economies . Resources Policy , 86, 1-9. Kheirandish Borujeni, Maryam and Gary Stading. 2024. A Transportation Decision: Developing the Red River . Decision Sciences Institute Proceedings, Phoenix, AZ. Kong, Qinghua and Jianfeng He. 2020. Controlling Methods of Driving Factors in Economic Development in Coastal Areas. Journal of Coastal Research , S1 (103), 129-133. Li Hui, Rou Li, Meng Shang, Yu Liu, and Dandan Su. 2022. Cooperative Decisions of Competitive Supply Chains Considering Carbon Trading Mechanism. International Journal of Low-Carbon Technology , 17, 102–117, https://doi.org/10.1093/ijlct/ctab085. Li, Zongyun, Syed Kumail Abbas Rizvi, Ghulame Rubbaniy, and Muhammad Umar. 2021. Understanding the Dynamics of Resource Curse in G7 Countries: The Role of Natural Resource Rents and the Three Facets of Financial Development. Resources Policy , 73, 102141, https://doi.org/10.1016/j. Petsagkourakis, Panagiotis and Federico Galvanin. 2021. Safe Model-based Design of Experiments Using Gaussian Processes. Computers and Chemical Engineering, 151, 1-20. The National Waterways Conference (2024). Arlington, VA, https://waterways.org/. Nimmegeers, Philippe, Satyajeet Bhonsale, Dries Telen, and Jan V. Impe. 2020. Optimal Experiment Design Under Parametric Uncertainty: A Comparison of a Sensitivities Approach Versus a Polynomial Chaos Based Stochastic Approach. Chemical Engineering Sciences, 221, 115651. Shang Meng, Hui Li, Yu-ping Wang, Yi-yan Qin, Yu Liu, and Yong Tan. 2021. Optimal decisions in a closed-loop supply chain under different policies of government intervention. Sustainable Energy Technologies and Assessments, 47, 101283. https://doi.org/10.1016/. Stading, Gary, Benito Flores, and David Olson. 2001. Understanding managerial preferences in selecting equipment. Journal of Operations Management , 19 (1), 23-37. Stoeckl, Natalie, Michelle Esparon, Marina Farr, Aurelie Delisle, and Owen Stanley. 2013. Distributional and Consumptive Water Demand Impacts of Different Types of Economic Growth in Two Northern Australian River Catchments. Austalasian Journal of Regional Studies , 19 (3), 396-432. Stone, John M. 2023. Student Participation from East Central Oklahoma University School of Business or Marketing Department. An email letter published to the Red River Valley Authority Organization, Pro bono consultant for the Red River Association. Xu, Changyan, Chan Lu, and Jingyao Song. 2024. Evolutionary Game of Inland Waterways LNG Construction Under Government Subsidy and Carbon Tax Policy Under Fuzzy Environment. International Journal of Low-Carbon Technologies, 19, 780-797. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 28 Aug, 2025 Reviews received at journal 27 Aug, 2025 Reviews received at journal 11 Aug, 2025 Reviewers agreed at journal 29 Jul, 2025 Reviewers agreed at journal 29 Jul, 2025 Reviewers invited by journal 29 Jul, 2025 Editor assigned by journal 29 Jul, 2025 Submission checks completed at journal 29 Jul, 2025 First submitted to journal 28 Jul, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7236082","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":492747706,"identity":"b47fd1ae-711d-4375-bc02-7e7eacb06ce6","order_by":0,"name":"Gary Stading","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9ElEQVRIiWNgGAWjYLACHgMGBn4Q4wFUQAK/emaIFsk2IDuBwYBYLUDK4BixWuT9zx/88Kbgjt3m+83PPiRU/JEzZ2A+eJsHjxbDG8nMknMMniVvO8ZmPCPhjIGxZQNbsjVeLTOYGaR5DA4nmx1jMGZIbDNI3HCAx0war5b+w8y/QVqM29g/MyT+A2nh/4ZXizxDMhvIFjsDNh6gLQ1gW9jwajGQSDaznGNwOEHiWE4xQ8IxY2ODw2zGlnPw2dJ/8PGNN38O2/M3H9/M8KFGTs7gePPDG2/w2XIAQic2wIWY8SgH2wJVak9A3SgYBaNgFIxkAAD3AknDlfV2sgAAAABJRU5ErkJggg==","orcid":"","institution":"Tarleton State University","correspondingAuthor":true,"prefix":"","firstName":"Gary","middleName":"","lastName":"Stading","suffix":""},{"id":492747707,"identity":"7beb7590-7af1-462a-945f-158c46061695","order_by":1,"name":"Maryam Kheirandish","email":"","orcid":"","institution":"Emory University","correspondingAuthor":false,"prefix":"","firstName":"Maryam","middleName":"","lastName":"Kheirandish","suffix":""},{"id":492747709,"identity":"ab40192a-6aca-4c73-aa12-2981920fde2b","order_by":2,"name":"Fred Norton","email":"","orcid":"","institution":"Texas A\u0026M University - Texarkana","correspondingAuthor":false,"prefix":"","firstName":"Fred","middleName":"","lastName":"Norton","suffix":""}],"badges":[],"createdAt":"2025-07-28 16:53:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7236082/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7236082/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87940615,"identity":"538469e9-e1b1-4d5a-a73f-eac00e57cd67","added_by":"auto","created_at":"2025-07-30 15:12:58","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":171250,"visible":true,"origin":"","legend":"\u003cp\u003eTen year traffic on Red River and Arkansas River in the form of receipts and shipments\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7236082/v1/cf8b27987b279c199f374846.png"},{"id":87940944,"identity":"9dd37be6-0ac5-4e0e-9438-93d587618e95","added_by":"auto","created_at":"2025-07-30 15:20:58","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":117849,"visible":true,"origin":"","legend":"\u003cp\u003eThe DOE corner points mapped out on each axis\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7236082/v1/2bd074a91aa95eb6c21eaf0d.png"},{"id":87940617,"identity":"82bafb26-ea13-47de-9ebe-ffef07d8d820","added_by":"auto","created_at":"2025-07-30 15:12:58","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":310402,"visible":true,"origin":"","legend":"\u003cp\u003eSummary statistics at each corner point\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7236082/v1/c94f3efb205bb2252de99691.png"},{"id":87943546,"identity":"1e3a92ae-66a1-4f64-a6a8-c1cde3eb6c32","added_by":"auto","created_at":"2025-07-30 15:44:58","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1280542,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7236082/v1/d8726e3e-60de-446e-b716-9e0c1afe6022.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Developing the Red River: Facilitating the decision utilizing design of experiment methodology","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eThe Red River is one of the last great rivers in the United States that remains not fully developed for transportation and recreational purposes (Kheirandish and Stading, 2024). The National Waterways Conference divides the benefits of waterway development into six major categories: ecosystem restoration, flood control, hydropower, navigation, recreation, and water supply (National Waterways Conference, 2024). Kheirandish and Stading (2024) argue that additional justification for development includes national security and safety. At present, the Red River is only developed from the Mississippi River to Shreveport, Louisiana. This developed portion of the Red River has shown and ratifies the benefits advocated by the National Waterways Conference.\u003c/p\u003e\u003cp\u003eThe continued development of the Red River, however, remains in question. It runs from Shreveport, Louisiana, into Arkansas, and then follows the borders of Texas and Arkansas before bordering Texas and Oklahoma. Entities presently lobbying for the river development propose that the Red River terminates just north of Dallas, Texas, at Denison, Texas (Stone, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Stone presents the benefits of such development, including cargo movement from Dallas with water transportation systems to the Gulf of America ports (e.g., New Orleans, Houston, etc.) for overseas international business shipment.\u003c/p\u003e\u003cp\u003eThe impact of Red River navigability offers opportunities for regional economic development and the strengthening of national security. The creation of shipping-related businesses and a growing workforce will drive capital investment and an increased wage base. National security is supported and enhanced when military equipment and supplies, which are manufactured and serviced near Texarkana at Red River Army Depot and Lone Star Army Ammunitions Plant, are more easily and efficiently distributed intermodally using developed waterways. Similarly, military equipment manufactured in the Dallas/Fort Worth area from manufacturers like Lockheed Martin and Bell Textron may be transported globally more expediently.\u003c/p\u003e\u003cp\u003eAdditionally, waterborne freight traffic represents a significant level of opportunity. Currently, freight moves by water at a rate of about 6% of all domestic tonnage (Frittelli, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The opportunity to increase the use of transporting freight over the water highway system can off-load the costly transporting of freight using rail or trucking. Trucking, especially, while it has the advantage of point-to-point delivery, also has a costly maintenance structure that only increases when trucks have to deliver heavy cargo loads over the roads. Heavy truck loads tend to break down roads at a faster rate, which increases the cost to rebuild this system.\u003c/p\u003e"},{"header":"2 Literature Review","content":"\u003cp\u003eThe Red River is naturally a shallow river with tight, hair-pinned curves throughout in undeveloped portions of the river. It terminates into the Mississippi River at Simmesport, Louisiana, just north of Vicksburg, Mississippi. In the developed parts of the Red River in Louisiana, the curves have been straightened, and the river has been dredged to make it navigable throughout that state. The same process of development in the lower river can be used to make the river navigable all the way to Denison, Texas. Such a development of the river would effectively provide a water port for the Dallas-Fort Worth metroplex. The Dallas-Fort Worth (DFW) is currently \u0026ldquo;landlocked\u0026rdquo; without a waterway access. It is posited in this research that the economic development opportunities of developing the Red River would benefit the entire four state region of Oklahoma, Arkansas, Louisiana, and Texas (Stone, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe nature of the shallow draft and the tight curves in the Red River can be partially attributed to the unique natural history of the Red River. The Red River had a very large natural dam form in what is now the Shreveport area. This natural dam is known as the great Red River Raft (Humphreys, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1971\u003c/span\u003e). This natural dam, some speculate, was the largest in size and the longest lasting known of its kind in the history of the United States and possibly the world. It is speculated that it could have existed from the 12th century to the mid 1830\u0026rsquo;s, and as long in length as more than 160 miles. It was cleared by the US Army Corps of Engineers from 1829 to 1838 by Captain Henry Miller Shreve, but the raft soon reformed and was cleared a second time in 1873 by Lieutenant Eugene Woodruff (Holbrook, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1946\u003c/span\u003e). All this is significant because this natural dam formed lakes, bayous and swamps that caused the nature and formation of this river that still exists to this day.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 River Development\u003c/h2\u003e\u003cp\u003eDeveloping the Red River and waterways, in general, are evaluated with metrics from a typical perspective of utilizing benefits in such areas as ecosystem restoration, flood control, hydropower, navigation, recreation, and water supply (National Waterways Conference, 2024). However, evaluating waterway development, in general, and the Red River, specifically, researching benefits from a tax perspective is limited (Khaddage-Soboh, et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This paper explores the question of whether or not the Red River should be developed using these criteria.\u003c/p\u003e\u003cp\u003eKhaddage-Soboh, et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) develop a case of costs in terms of taxes and rents to the environment and a carbon footprint. Each of these areas (ecosystem restoration, flood control, hydropower, navigation, recreation, and water supply) all contribute to improving the environment by reducing pollution and reducing the carbon footprint. These authors argue that the federal tax perspective should also be relevant for evaluating the benefits of developing the Red River. The tax perspective, environmental tax or financial tax, all result in financial gains as well as environmental gains, but as argued by these authors, these benefits extend to security aspects as well. Kheirandish and Stading (2024) support the environmental aspects of developing the Red River, but also, they begin to explore security as benefit for developing waterway transportation.\u003c/p\u003e\u003cp\u003eDespite all the various reasons for developing the Red River, economic development remains the main reason for developing a river system. Bhurtyal, et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), in presenting a strong algorithm for identifying best locations for ports to be placed along the Arkansas River, also presented a strong case for developing river freight transportation in general. Jung (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) argues that the typical academic debate breaks down into two separate perspectives on economic development. One perspective is that the development of ports on a river system leads to economic development.\u003c/p\u003e\u003cp\u003eThe second perspective is that economic development in a river system creates environmental, hydropower, recreational and water supply benefits as well as contributing to port growth of existing ports and the need for additional ports. Both types of development, river system development and port development, have a relationship with economic development. Development of ports on a river system leads ultimately to employment, revenue, and social benefits for not only ports but also for geographical areas and the states where the development occurs (Edih et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Each perspective supports the argument for continued development of the Red River.\u003c/p\u003e\u003cp\u003eFunding is, of course, always the main concern of all transportation systems (water, air, trucking and rail). Logistics and transportation costs of all freight movements is always a large cost of the burden placed on the consumer, but these service costs are also necessary for providing the product to the consumer. Transportation costs, particularly maintenance costs, are subsidized by the U.S. government (e.g. trucking is subsidized by roads being build and maintained). In particular, the water transport business is supported by the government in two significant ways. Each provides about 50% of the support. The first half of support is provided by the Inland Waterway Trust Fund, where the funds are provided by a barge fuel tax; the second half of support is provided by the General Treasury fund (Frittelli, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). River water systems have long been studied for their contribution to economic development as can be seen in seminal work for their ability to carry oil and petroleum products, ores, agriculture products, social and security processes (Hirsch, 1961).\u003c/p\u003e\u003cp\u003eInter Waterways System (IWS), which is the system of inland waterways in the United States supported by a tax structure (Frittelli, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The tax structure is used to feed the inland waterways trust fund (IWTF) to maintain the waterway infrastructure and is used to pay half the constructions, which includes maintaining and building new locks and dams. The maintenance and construction of the river system is managed by the Army of Corp of Engineers. The barge carriers in turn support the inland waterways trust fund with a per gallon fuel tax (Frittelli, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Lockage fees are also being considered as a source of income. Congress set this system up originally to primarily generate revenue, but also, create healthy competition for transporting products with the rail system.\u003c/p\u003e\u003cp\u003eIn the development of natural resources Khaddage-Soboh, et al., (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) make the case that in, many different ways, taxes make a big difference in justifying the development of those resources. While Khaddage-Soboh, et al., do not necessarily address the taxes in the traditional sense of income to the government, they do essentially make the case that taxes can reveal themselves as costs, especially when tax dollars are spent to clean up the environment. In the case of waterway development, this type of waterway development leads to a cleaner environmental footprint. Khaddage-Soboh, et al., make the case that type of cost is an imperative in public policy.\u003c/p\u003e\u003cp\u003eThere is a strong propensity in the literature to support both economic development and to develop natural resources, especially in developing countries (Li, et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In developing countries, Li, et al., discussed the difficulty of developing natural resources with the purpose of building a strong financial economy. Khaddage-Soboh, et al., (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) make the case that renewable energies improve the environment and encourage sustainable practices. Kheirandish and Stading (2024) argue that developing waterways result in the types of environmentally sound growth that is desired including providing expanded natural habitats, especially when compared to other forms of transportation modes.\u003c/p\u003e\u003cp\u003eWhile it is generally acknowledged that oil and gas production from a major oil field like the Permian basin is dependent and best transported by pipelines, the materials to produce that oil do not flow through pipelines (Burns and Sirisomboonsuk, 2022). A single well takes 500 tons of steel, 365 truckloads of sand, over 35 Olympic sized swimming pools worth of water (Collins, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Collins states that moving oil crude away from a well head requires over 2,700 truck loads, which is over 500,000 barrels of crude.\u003c/p\u003e\u003cp\u003eTransporting these types of goods by truck destroys roads and makes them not only dangerous, but also costly to maintain. The civic tax on this type of work is high. These products are not pipeline products. These types of products are good examples of products that should be moved as close to the Permian Basin and other commodities and oil producing areas via water transportation as possible (Asborno, et al., 2020). Red River development is a natural consideration for the oil fields of West Texas and Oklahoma, especially if supported by the data as proposed by the research question addressed in this paper.\u003c/p\u003e\u003cp\u003eThe inland waterway supports the transportation of heavy raw materials. These include items like grain, coal, sand, petroleum, and construction materials (Frettelli, 2018). These are exactly the types of materials that are transported in and out of the oil fields that populate Oklahoma and Texas. These are also the materials that serve the agriculture industry of Oklahoma and Texas. These are also materials that are heavy and cause degradation on the road systems. If a port were developed on the Texas-Oklahoma border just north of Dallas, the return on investment (ROI) should support the costs of maintaining the river transportation system.\u003c/p\u003e\u003cp\u003eOne example is provided by Xu, Lu, and Song (2024). Xu, et. al., apply a tripartite evolutionary game model to a liquid natural gas (LNG) problem to evaluate a government strategy to attain a carbon tax benefit. While transporting LNG is generally relative to deep water shipping, the application is relative to the decision of developing the Red River. Xu, et al., evaluate the strategies of the government, the shipping company, and the energy company using the carbon tax benefit as the measurement. The carbon tax mechanism has been used in various applications and is a widely used practice in the following applications: pollution levies, subsidies, and emissions trading (Sheng, et al., 2021; Li, et al., 2022).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Research Contribution\u003c/h2\u003e\u003cp\u003eThe factors that drive economic development, especially in water port regions, continue to be studied (Bhurtyal, et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The factors, which have shown evidence of support for port development include marketing, information and data analysis, manpower labor, industrial structure, regional innovation capability, the degree of how a region is \u0026ldquo;opened up,\u0026rdquo; and finally financial support from both governmental sources and economic foundations (Kong and He, 2020). Kong and He make the point that to calculate the comprehensive growth factor rate, labor input index information and capital input index information, and output index information must be documented and measured.\u003c/p\u003e\u003cp\u003eThe main point is that growth is achieved through the economic development process, which results in increased employment opportunities, increased revenues into a region, and increased regional product output (GDP) as the result of the economic development. Burtyal, et al. (2024) clearly demonstrate the benefits of economic development in the case of the Arkansas River. This paper extends this research to measure the benefits of extending the Red River by using Design of Experiment (DOE) methodology to compare the fully developed Arkansas River with the portion of the Red River, which is partially developed, to measure the potential results of completing the development of a Red River extension.\u003c/p\u003e\u003c/div\u003e"},{"header":"3 Methodology","content":"\u003cp\u003eThis study is an exploratory study on the data from the National Waterways Conference (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This study compares data from two rivers: The Red River (known as the J. Bennett Johnston Waterway or JBJ Waterway) and the Arkansas River (known as the McClellan-Kerr Arkansas River Navigation System or MKARNS). The development of the next section of the Red River is formally known as the Southwest Arkansas Red River Navigation (SARRN) Channel. Comparing rivers for their effect on economic development is not a new phenomenon. Stoeckl, et al. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) similarly studied two rivers in Australia for their potential impact on economic development. The data in this study is collected from actual shipping data as reported into a national database from the National Waterways Conference and explains the traffic on the Red River and Arkansas River.\u003c/p\u003e\u003cp\u003eThe Arkansas River Basin spans 1,469 miles from the Rockies to the Lower Mississippi, offering recreational activities and navigation via 18 locks and dams that also support power and flood control. In contrast, the Red River Basin, marking much of the Texas\u0026ndash;Oklahoma border before emptying in Louisiana, is managed under the Red River Compact to balance agricultural, urban, industrial, and ecological water needs. The Red River navigation begins in Shreveport, Louisiana and terminates into the Mississippi River just north of Vicksburg, Mississippi.\u003c/p\u003e\u003cp\u003eThe first notable relevant point from the data is that the state of Oklahoma currently moves a little over \u003cspan\u003e$\u003c/span\u003e180B of various products by truck, and this mode of transportation is growing annually at a rate of over 7%. Rail is moving about \u003cspan\u003e$\u003c/span\u003e16B worth of product, and it is growing at a rate of a little over 9.5%. Water transportation does not appear as a category from Oklahoma, reflecting the state\u0026rsquo;s current lack of a fully developed, high-capacity port. Most of the leading products being transported include those types of bulk and heavy break-bulk items that extol a large environment tax, a large carbon tax, and a large civic tax (road repairs, etc.)\u003c/p\u003e\u003cp\u003eComparatively, Arkansas moves about \u003cspan\u003e$\u003c/span\u003e141B annually and is growing at a rate of about 8% annually. Rail moves about \u003cspan\u003e$\u003c/span\u003e7.5B annually and is growing at a little over 7.5%. Arkansas has pipeline traffic, and it moves a little over \u003cspan\u003e$\u003c/span\u003e26.5B worth of product annually. The pipeline traffic has grown, but it appears to be flattening out, indicating that it may be reaching capacity, and the construction of additional pipelines is limited. Product moving on the Arkansas river is a little over a half a billion dollars per year and is growing at almost 9% annually.\u003c/p\u003e\u003cp\u003eWhile the Arkansas River supports efficient, year-round barge transport, the Red River plays a smaller role in commercial transportation. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the 10-year traffic of both rivers from 2013\u0026ndash;2022, illustrating consistent utilization of the Arkansas River and highlighting the Red River\u0026rsquo;s potential for expanded freight movement once infrastructure and navigational enhancements are in place.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e \u003cb\u003eAbout Here\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis study employs a three-factor, two level DOE analysis to determine if further development of the Red River could offer a valuable alternative route for regional supply chains, reduce congestion on existing corridors, and lower transportation costs. The three-factor analysis compares Arkansas River and Red River traffic assessing the traffic volumes growth over a five-year span and evaluating differences across various voyage types. The three factors include Rivers, Traffic, and Years and the levels are included below next to the factors.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eRiver Factor\u003c/strong\u003e\u003cp\u003eLevels are Red vs. Arkansas (X axis)\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e\u003cb\u003eTraffic Factor\u003c/b\u003e: Levels are Voyage Types: Shipments (and through) vs. Receipts (and \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ei\u003c/span\u003entraportal) (Y axis)\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eTime Period Factor\u003c/strong\u003e\u003cp\u003eLevels are the 5 year period from 2018 through 2022 vs. the 5 year period from 2013 through 2017 (z axis)\u003c/p\u003e\u003c/p\u003e\u003cp\u003eDesigned experiment methodology utilizes regression analysis to assess the results of the data (Stading, et al, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). Similarly, Khaddage-Soboh, et al., (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) use regression analysis will be used to analyze the designed experiment results. DOE methodology has the capability to identify the significant differences between the factors that will indicate if developing a river for traffic transportation is of the value necessary to justify the development for regional economic development (Stading, et al, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2001\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eDesign of Experimentation (DOE) methodology has been used in a number of applications including to simulate data when a lack of data is present or when conditions are not safe to run designed experiments (Petsagkourakis and Galvanin, 2021). In such cases, Gaussian Processes (GP) are used to estimate constraints and expected values of objective functions. Nimmeegers, et al. (2020) utilize confidence regions estimated from parameters collected, which is more robust DOE. With data available from Inland Waterways, parameters are available from which to build estimates. This provides a robust analysis to predict the outcomes of developing a river system.\u003c/p\u003e"},{"header":"4 Data Analysis","content":"\u003cp\u003eThe data in this study is analyzed using design of experiment methodology. The data is provided by the National Waterways Conference (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The data is collected from actual shipping data as reported into a national database. These sources of data provide an excellent basis for exploring the advantage of developing the Red River. The model will be refined as new studies provide updated information.\u003c/p\u003e\u003cp\u003eTo analyze if there are advantages to additional development of a waterway, the DOE will lay out in the current configuration with levels mapped out in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e below.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e \u003cb\u003eAbout Here\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe levels for each factor are depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The river factor compares the currently partially developed Red River traffic with the fully developed Arkansas River traffic. The second factor compares shipping product upriver versus shipping product down river. Finally, the third factor compares two different 5-year periods to see if growth is occurring in the shipping patterns. The results of studying this data should provide insights to better understand the advantages of continuing the development of the Red River. The component which cannot be tested for, of course, is the volume of traffic which may occur when the Dallas-Ft. Worth (DFW) urban area gains access to a waterway. The reason this cannot be studied in parallel is because the Arkansas River just simply does not have access to the size of the market equal to the DFW urban region. Additionally, the Arkansas River also does not have access to the same oil field regions as a developed Red River would have.\u003c/p\u003e\u003cp\u003eDesign of experiments (DOE) is a common analysis technique using Gaussian process with a trust region when utilizing existing databases or to substitute missing data to compute the results in the absence of controlled experimental data (Petsagkourakis and Galvanin, 2021). Gaussian principles are used to quantify the uncertainty of the physical system and to calculate the optimal experimental design while accounting for the parametric uncertainty present in the model with existing data (Petsagkourakis and Galvanin, 2021). Ultimately, this analysis technique provides results with the probabilistic satisfaction of the constraints of the design of experiments model.\u003c/p\u003e"},{"header":"5 Results","content":"\u003cp\u003eThe results from the design of experiments analysis of the data provide insight into the significance of various decision factors referenced in evaluating the decision to develop the Red River. The model incorporates various inputs and data including volumes of products shipped in and out of river port destinations which decision makers (including congress members) use to evaluate the decision to develop the Red River. These results include business and economic data that drives the development of a river for the transportation and environmental needs of the people within a region and for the whole country.\u003c/p\u003e\u003cp\u003eThe first set of results are analyzed with the average and standard deviations of each level. Annual total shipping values are included as well for perspective. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e below shows these results:\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e \u003cb\u003eAbout Here\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe data is first examined by using a t-test pairwise comparison of the main effects. The results support the idea that growth over time is achieved as river shipping is further developed. Evaluating the data on the first individual axis, the river differences shows statistical differences in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e below.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003et-test: two-sample test between the Red River and the Arkansas River\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eRed River\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eArkansas River\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1850678.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2690906.275\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.17652E\u0026thinsp;+\u0026thinsp;12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.26385E\u0026thinsp;+\u0026thinsp;12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObservations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e40\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypothesized Mean Difference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDf\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003et Stat\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-2.093975167\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP(T\u0026thinsp;\u0026lt;\u0026thinsp;=\u0026thinsp;t) one-tail\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.019755173\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003et Critical one-tail\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.664624645\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP(T\u0026thinsp;\u0026lt;\u0026thinsp;=\u0026thinsp;t) two-tail\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.039510347\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003et Critical two-tail\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.990847069\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e \u003cb\u003eAbout Here\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe Arkansas River is more fully developed, while the Red River is only partially developed. The p-statistic shows that the tonnage shipped on the Arkansas River is statistically larger than the tonnage that is shipped on the Red River.\u003c/p\u003e\u003cp\u003eThe next factor evaluated is the differences between five-year time periods. While, just simply looking at average tonnages by year, it looks like there may have been a decline in shipping. However, a significant difference between these groups is marginal on the pairwise comparison (t-stat is below the critical t). See Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e below.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003et-test two-sample test between five year time periods\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003e2013\u0026ndash;2017\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003e2018\u0026ndash;2022\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2441493.625\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2100091.55\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.85573E\u0026thinsp;+\u0026thinsp;12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.8869E\u0026thinsp;+\u0026thinsp;12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObservations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e40\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypothesized Mean Difference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDf\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003et Stat\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.831536412\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP(T\u0026thinsp;\u0026lt;\u0026thinsp;=\u0026thinsp;t) one-tail\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.204137849\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003et Critical one-tail\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.665151353\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP(T\u0026thinsp;\u0026lt;\u0026thinsp;=\u0026thinsp;t) two-tail\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.408275697\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003et Critical two-tail\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.99167261\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e \u003cb\u003eAbout Here\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe average amounts shipped in each time period is well withing the expected variance of normal amounts of shipping. It also supports the idea that river shipping is somewhat timeless. The need for river shipping of the types of products that use river shipping will continue to use that mode of transportation over time.\u003c/p\u003e\u003cp\u003eThe next main effect being measured is if there is a difference between product moving up or down the river. Product moving upriver is considered a receipt at river ports. If the product is being shipped downriver it is considered a shipment. The significance for this pairwise comparison effect is provided below in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003et-test for the two-sample test for shipments vs. receipts\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eReceipts\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eShipments\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2526991.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2014593.725\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.75232E\u0026thinsp;+\u0026thinsp;12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.91545E\u0026thinsp;+\u0026thinsp;12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObservations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e40\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypothesized Mean Difference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDf\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003et Stat\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.255009411\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP(T\u0026thinsp;\u0026lt;\u0026thinsp;=\u0026thinsp;t) one-tail\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.106660404\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003et Critical one-tail\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.665151353\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP(T\u0026thinsp;\u0026lt;\u0026thinsp;=\u0026thinsp;t) two-tail\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.213320809\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003et Critical two-tail\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.99167261\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e \u003cb\u003eAbout Here\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e challenges that there may be marginal significant differences of product being shipped or received. The p-value shows that there may only be marginal significant difference in these two directions of shipment. This makes sense because a shipping company would not want to move empty barges up or down river if at all possible, supporting the idea that backhauling is targeted as much as possible on river systems.\u003c/p\u003e\u003cp\u003eMoving to the analysis of the experimental design, the significance of these data points does confirm statistical significance at each main effect. The full Anova shows the results in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e below:\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAnova results for the full factorial experiment\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEffect\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSum of Squared\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDegree of Freedom\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eF Statistics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eProbability(\u0026gt;\u0026thinsp;F)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRiver (Arkansas river Vs. Red river)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e52.22e\u0026thinsp;+\u0026thinsp;14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e190.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.00e-15\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eType of Shipment (Receipts in Vs. Shipments out)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e26.18e\u0026thinsp;+\u0026thinsp;14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e95.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.94e-11\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYear Span (2013\u0026ndash;2017 Vs. 2018\u0026ndash;2022)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.01e\u0026thinsp;+\u0026thinsp;14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e14.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.55e-4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRiver_Type of Shipment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23.58e\u0026thinsp;+\u0026thinsp;14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e85.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.05e-10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRiver_Year Span\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.17e\u0026thinsp;+\u0026thinsp;14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eType of Shipment_Year Span\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20.92e\u0026thinsp;+\u0026thinsp;11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.78\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eResidual\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9.06e\u0026thinsp;+\u0026thinsp;14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e \u003cb\u003eAbout Here\u003c/b\u003e\u003c/p\u003e\u003cp\u003eEach of the main effects in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows statistical significance. However, the main river effect showed the strongest effect supporting the argument that the fully developed river does result in significant differences in moving freight over a less developed river. The other main effects are showing some level significance but considerably less.\u003c/p\u003e\u003cp\u003eMoving to the interaction effects, the interaction between the river and either cargo type or time period effect shows some limited significance. This indicates a strong contribution in effect from the differences in rivers, thereby, continuing to support the notion that the more developed river generates economic development. The results are provided in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e below:\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eRegression of the full factorial interactions\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003eLinear Regression Model Summary:\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003eR-squared: 0.922\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003eAdj. R-squared: 0.908\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003eF-statistic: 65.03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003eProb (F-statistic): 7.25e-17\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTerm\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003ecoefficient\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003et statistics\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eP\u0026gt;|t|\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e[0.025\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.975]\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eIntercept\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.01e\u0026thinsp;+\u0026thinsp;06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e36.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.84e\u0026thinsp;+\u0026thinsp;06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.18e\u0026thinsp;+\u0026thinsp;06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRiver (Arkansas river Vs. Red river)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-1.14e\u0026thinsp;+\u0026thinsp;06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-13.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-1.31e\u0026thinsp;+\u0026thinsp;06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-9.74e\u0026thinsp;+\u0026thinsp;05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eType of Shipment (Receipts in Vs. Shipments out)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-8.09e\u0026thinsp;+\u0026thinsp;05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-9.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-9.78e\u0026thinsp;+\u0026thinsp;05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-6.40e\u0026thinsp;+\u0026thinsp;05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYear Span (2013\u0026ndash;2017 Vs. 2018\u0026ndash;2022)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-3.17e\u0026thinsp;+\u0026thinsp;05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-3.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-4.85e\u0026thinsp;+\u0026thinsp;05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-1.48e\u0026thinsp;+\u0026thinsp;05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRiver_Type of Shipment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-7.68e\u0026thinsp;+\u0026thinsp;05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-9.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-9.36e\u0026thinsp;+\u0026thinsp;05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-5.99e\u0026thinsp;+\u0026thinsp;05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRiver_Year Span\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.71e\u0026thinsp;+\u0026thinsp;05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.047\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.68e\u0026thinsp;+\u0026thinsp;3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.4e\u0026thinsp;+\u0026thinsp;05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eType of Shipment_Year Span\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-2.29e\u0026thinsp;+\u0026thinsp;04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.784\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-1.91e\u0026thinsp;+\u0026thinsp;05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.46e\u0026thinsp;+\u0026thinsp;05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e \u003cb\u003eAbout Here\u003c/b\u003e\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e5.1 Summary of Results\u003c/h2\u003e\u003cp\u003eThe ANOVA results have shown significance with each of the main effects and some interaction effects. However, the strongest or most significant of the results is that a fully developed Arkansas River provides more freight transportation than a partially developed Red River. The interactions in the ANOVA also show the most significant interaction at the 0.05 level between the river along and the type of cargo shipment. The other two main effect interactions are also significant at a slightly weaker level.\u003c/p\u003e\u003cp\u003eThe regression results show an adjusted R-squared to be 0.99. This is a strong support that the regression model is a good predictor of the results. The specific interaction breakdowns showed decent adjusted R-squared results. After completing the regression, the t-test comparison of each of the main effects, the DOE main effects, and the interaction effects support further development of the Red River.\u003c/p\u003e\u003cp\u003eIn summary, the main effect of the river differences is significant. On this test, the type of shipments and the differences in years show that, while these main effects are significant from the ANOVA, more testing may likely be needed to firmly settle on these conclusions.\u003c/p\u003e\u003cp\u003eUltimately, the significance of each of the interactions may be explained by the size and strength of the numbers from the main effects, which could possibly be overpowering the measured interaction effects. More tests are required to determine if each of the interaction significances are real. However, due to the individual measured results of the main effects, in the end, these results support developing the Red River.\u003c/p\u003e\u003c/div\u003e"},{"header":"6 Discussion","content":"\u003cp\u003eThe results provide insight into the importance of the decision to develop the Red River. The model is useful for evaluating further development of the Red River by key decision makers who are primarily congressional representatives from the various constituencies representing the states of Louisiana, Arkansas, Texas, and Oklahoma. Since congressional representatives are not generally considered experts on river development, this model is a valuable aid by putting test results from relevant data into the hands of decision experts. The multiple regressive design of experiments analysis supports a decision to develop the Red River.\u003c/p\u003e\u003cp\u003eThe test results showed that developing the Arkansas river increased freight transport up and down the river to achieve higher levels of trade to ports and locations up and down the river. Similarly, the product transports up and down the Red River to the portion of the Red River that has been developed showing a productive level of traffic on those river ports. The freight moving up and down the Red River is about half the freight moving up and down the Arkansas River. This makes sense because approximately half the Red River has been developed.\u003c/p\u003e\u003cp\u003eThe analysis supports the idea that all three main effects are significant as seen in the p-value results. The main effect of the most significance is that river transport is greater on the more developed river especially since developing the Red River provides access to larger cities in the region (e.g. Dallas, Fort Worth, Oklahoma City). In the case of the Arkansas river, it provides freight transportation access to Little Rock, Arkansas. In the case of the Red River, developing it will provide river freight access to a much larger metropolitan area, specifically the Dallas-Fort Worth (DFW) metroplex.\u003c/p\u003e\u003cp\u003eIn addition, developing the Red River will provide river transportation to move oil and gas equipment and supplies access to the oil fields of Oklahoma and West Texas. Most of the heavy items being transported to the oil fields are currently being shipped by truck. Truck transport with these types of cargo and in these quantities result in significant costs, especially to the publicly maintained infrastructure. Heavy loads in these quantities require a significant amount of maintenance costs especially to road repair and maintenance.\u003c/p\u003e\u003cp\u003eOne important aspect of developing a river for navigation includes also the development of port operations. One of the key measures of port operations is the dwell time of cargo (Frittelli, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Congress is concerned about these measures. There are many factors that affect dwell time, but one important point is that new ports also bring new and more efficient operations, especially since those ports place importance on new and efficient processes to improve the current cargo dwell time measures. The main key performance indicator includes the amount of time to load and unload the cargo. New operational methods will be available and provided for the new ports, which will be developed when the Red River navigability gets developed. These new ports will include new and improved efficiencies that help achieve the kinds of throughput velocity for loading river barges necessary to compete in today\u0026rsquo;s marketplace.\u003c/p\u003e\u003cp\u003eRevenues are generated for river traffic through various ways. First, barge operators pay a fuel tax through the Inland Waterways Trust Fund (IWTF) that covers the cost of new projects and major rehabilitation projects (Frittelli, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This fund covers about half the cost of operating the waterways. The other half is covered by general funds. This study provides strong evidence that that the development of this leg of the Red River can generate economic development and commercial cargo use of the Red River that will provide the return on investment (ROI) that will make this project a revenue generator.\u003c/p\u003e\u003cp\u003eKong and He (2020) select various financial criteria to measure the success of an investment for economic development of a waterway representing the regional GDP. These criteria include expenditure and budget from local expenditures, proportions of value or secondary and tertiary industries to GDP, year-end residential savings, and electricity consumption per unit of GDP. These criteria are relevant for the development of the regions measured in this study. While there is not necessarily a way to measure these items directly in this study, this study does produce results that support positive correlations in each of these categories, especially in evaluating the types of cargo loads that address these revenue sources. The designed experiment results provide evidence that the shipments of products that are statistically significant in increased volume including both agricultural goods and oil and gas support items producing the types of value that improve the GDP in each of these categories.\u003c/p\u003e\u003cp\u003eKong and He (2020) make a request to the research community for researchers to verify their results on ports driving economic development using data and designed experimental results. This research answers that call. Kong and He found through their theoretical research that factors contributing to economic development through ports and waterways development include labor input, capital input and a growth factor using GDP as the measurement. The research presented in these results supports the idea that heavy loads of goods are best transported through water transportation, and that, the transport of these goods using waterways does help the economic growth of the regions supported by these waterways, including those specific to the port cities.\u003c/p\u003e"},{"header":"7 Conclusion","content":"\u003cp\u003eThe Red River is one of the last rivers in the United States that is not fully developed for freight transportation. The research question is whether or not the Red River should be developed to a point just north of Dallas. The results showed that through an experimental test comparison of an existing database provided by the National Waterways Conference that a partially developed river like the Red River to a fully developed river like the Arkansas River results in shipping benefits for fully developing the Red River. The rivers are a good comparison because they are geographically relatively close, they carry similar types of cargo, and they serve similar client bases, including significant agricultural regions of the United States. The potential of developing the Red River is even greater than the fully developed Arkansas River because the Red River has the potential of serving at least one major metropolitan effort of the Dallas \u0026ndash; Fort Worth region; serving a second major metropolitan effort of Oklahoma City; and, also, serving a major oil industry in Texas and Oklahoma with many products that may be used in oil fields.\u003c/p\u003e\u003cp\u003eThe results show significance in various factors that support the development of the Red River. This study reinforces the importance of heavy cargo loads contributing to decision factors for developing the Red River. This study also demonstrates how water development to carry heavy loads contributes with lesser studied tax and security factors. The results of this research and the model do provide encouraging evidence that Red River development should proceed. The Red River\u0026rsquo;s development will result in significant economic, environmental, and social benefit for the region. It will drive significant transportation development which will help with the economic development of the region.\u003c/p\u003e\u003cp\u003eThe Office of Management and Budget (OMB) will not request funding unless the economic benefit is 2.5 times the cost (Frittelli, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The projects which are projected to have this kind of return are focused in three or four areas: the Ohio and Tennessee River Valleys, the Gulf Intercoastal Waterway with its petrochemical industry focus, and the agricultural heartland. The development of the Red River addresses the need for 2 of the top priorities for the OMB. Developing the Red River provides an artery directly into a major portion of the agricultural heartland providing access to Arkansas, the Oklahoma, and the Texas rich agricultural economy. Equally as important, developing the Red River to Denison, TX also provides an artery into the rich gas and oil economies of Oklahoma and West Texas.\u003c/p\u003e\u003cp\u003eThe need to develop the river highways is recognized in Washington D.C. The \u0026ldquo;Marine Highways\u0026rdquo; act from Congress in 2007 was passed acknowledging the need to divert rail and truck freight to the water highways (Frittelli, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Rivers are one of the most efficient forms of freight movement (Bhutyal, et al., 2024). The \u0026ldquo;Marine Highways\u0026rdquo; act even provided funding for two of the largest costs in developing the marine highway. This funding is in the form of grants for upgrading terminal equipment and vessel upgrades. In 2012, Congress expanded the act to include all U.S. ports and marine loading and unloading operations of containers between large vessels and smaller vessels (Frittelli, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This study supports the objectives of these Congressional acts in demonstrating the advantage of developing a river highway which results in significantly larger cargo shipments by utilizing the river highway for heavy cargo shipments.\u003c/p\u003e\u003cp\u003eIn summary, when each of these factors are fully considered, developing the Red River to the point just North of Dallas on the Texas \u0026ndash; Oklahoma border will result in meeting the goals that are the criteria for economic development, including revenues generated through taxes from the transporting using river waterways. The heavy loads carried by this water traffic will address the regional needs of Oklahoma, Texas, and Arkansas including the important agricultural and oil and gas products necessary for this region in the country. Furthermore, the types of economic development which will occur will meet the criteria established by government initiatives including economics 2.5 times over the cost of development (Frittelli, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e"},{"header":"8 Limitations","content":"\u003cp\u003eA pre-existing database was used to conduct the analysis (The National Waterways Conference, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). As with all pre-existing databases, the major limitation is that the researchers do not have control of the data collection. The collection of data always involves a certain amount variation in the collection of that data. Without being a part of the collection process, the sources of potential variation are unknown.\u003c/p\u003e\u003cp\u003eThe positive contribution for using existing databases, especially this database, is that it is a generally accepted and \u0026ldquo;official\u0026rdquo; database vetted by a trusted organization (The National Waterways Conference, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The data represents data collected from traditional and generally accepted sources. This type of data, while it may originate from sources not considered \u0026ldquo;laboratory\u0026rdquo; controlled experimentally designed sources, the data does represent top lined summarized data that generally has uncontrolled variation \u0026ldquo;washed out.\u0026rdquo; Therefore, this data, while not ideal, produces reliable and trustworthy results.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eG.S. was primary authorM.K. was the primary data analyst and author of the results sectionF.N. was an editor\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data is resident at The National Waterways Conference (2024). Arlington, VA, https://waterways.org/.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAsborno, Magdalena I., Sarah Hernandez, and Taslima Akter. 2020. Multicommodity Port Throughput from Truck GPS and Lock Performance Data Fusion. \u003cem\u003eMaritime Economics \u0026amp; Logistics\u003c/em\u003e, 22 (2), 196-217. https://doi.org/10.1057/s41278-020-00154-7.\u003c/li\u003e\n\u003cli\u003eBhurtyal, Sanjeev, Sarah Hernandez, Sandra Eksioglu, and Manzi Yves. 2024. A Two-Stage Stochastic Optimization Model for Port Infrastructure Planning. \u003cem\u003eMaritime Economics \u0026amp; Logistics\u003c/em\u003e, 26,185-211. https://doi.org/10.1057/s41278-023-00262-0.\u003c/li\u003e\n\u003cli\u003eBurns, James R. and Pinyarat Sirisomboonsuk. 2022. Applications of System Dynamics and Big Data Oil and Gas Dynamics in the Permian Basin.\u003cem\u003e International Journal of Business Analytics\u003c/em\u003e, 9 (1), 1-22. \u003c/li\u003e\n\u003cli\u003eCollins, Gabriel. 2018. Addressing the Impacts of Oil and Gas on Development on Texas Roads.\u003cem\u003e Testimony to the Texas House of Representatives Transportation Committee,\u003c/em\u003e Rice University\u0026rsquo;s Baker Institute of Public Policy, https://www.bakerinstitute.org/research/addressing-impacts-oil-gas-development-texas-roads. \u003c/li\u003e\n\u003cli\u003eEdih, University O., Fidelia Igemohia, and Nyanayon Faghawari. 2022. The Effect of Optimal Port Operations on Global Maritime Transportation: A study of Selected Ports in Nigeria. \u003cem\u003eJournal of Money and Business\u003c/em\u003e, 2 (2), 173-185. \u003c/li\u003e\n\u003cli\u003eFrittelli, John. 2020. Federal Freight Policy: In Brief. \u003cem\u003eCongressional Research Service\u003c/em\u003e, R44367, (14), 1-13. https://crsreports.congress.gov.\u003c/li\u003e\n\u003cli\u003eFrittelli, John. 2018. 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Student Participation from East Central Oklahoma University School of Business or Marketing Department.\u003cem\u003e An email letter published to the Red River Valley Authority Organization,\u003c/em\u003ePro bono consultant for the Red River Association. \u003c/li\u003e\n\u003cli\u003eXu, Changyan, Chan Lu, and Jingyao Song. 2024. Evolutionary Game of Inland Waterways LNG Construction Under Government Subsidy and Carbon Tax Policy Under Fuzzy Environment.\u003cem\u003e International Journal of Low-Carbon Technologies,\u003c/em\u003e 19, 780-797. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":false,"email":"","identity":"maritime-economics-and-logistics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Maritime Economics \u0026 Logistics","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"VoR Journals","inReviewEnabled":false,"inReviewRevisionsEnabled":false},"keywords":"Economic development, Red River marine water highway system, Design of experiments, Marine Highways Congressional Act","lastPublishedDoi":"10.21203/rs.3.rs-7236082/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7236082/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe Red River is one of the last great rivers in the United States that is only partially developed for transportation and recreational purposes. The continued development of the Red River, however, remains a policy question. The impact of Red River navigability offers opportunities for regional economic development, and the creation of shipping related businesses have the potential to drive capital investment. This paper utilizes design of experiment (DOE) methodology on data from a database provided by the National Waterways Conference to provide information of which decision makers, policy makers, and congressional members can use to evaluate the decision to continue further developing the Red River. The rivers present a good experimental comparison because they are geographically relatively close, they carry similar types of cargo, and they serve similar client bases, including significant agricultural regions of the United States. The results from this experiment show significance in various factors that support the development of the Red River. This study reinforces the importance of heavy cargo loads contributing to decision factors for developing the Red River. This study supports the objectives of the \u0026ldquo;Marine Highways\u0026rdquo; Congressional Act with results encouraging the final stages of development for the Red River highway system.\u003c/p\u003e","manuscriptTitle":"Developing the Red River: Facilitating the decision utilizing design of experiment methodology","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-30 15:12:53","doi":"10.21203/rs.3.rs-7236082/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-08-28T08:49:56+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-27T18:56:33+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-11T20:18:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"307665796586959279937924607409104836723","date":"2025-07-29T17:18:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"128359171100218575579276607627758656136","date":"2025-07-29T15:18:11+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-29T06:53:45+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-29T04:35:45+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-29T04:35:12+00:00","index":"","fulltext":""},{"type":"submitted","content":"Maritime Economics \u0026 Logistics","date":"2025-07-28T16:51:27+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":false,"email":"","identity":"maritime-economics-and-logistics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Maritime Economics \u0026 Logistics","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"VoR Journals","inReviewEnabled":false,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"1f83efd0-3139-4597-b53a-4c968062a5dd","owner":[],"postedDate":"July 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-11-12T03:08:18+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-30 15:12:53","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7236082","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7236082","identity":"rs-7236082","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}