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9 S U M M A R Y The Research Effort The purpose of NCFRP Project 42 was to explore the development of an analytical framework and model for evaluating the allocation of operations and maintenance dollars to navigation projects, taking into account how those projects tie into the overall surface transportation system. However, the literature dealing with the prioritization of navigation projects suffers from the same shortcomings as transportation data repositoriesâit is very modal specific and does not provide a means of analyzing system effects. This research is a first step toward addressing those shortcomings. Initially, the researchers intended to use the U.S. Army Corps of Engineers (Corps) confidential waterborne trip data file to identify specific points of interchange with the landside system (docks), as well as the FHWA confidential Freight Analysis Framework 3 (FAF3) data. After the project was initiated, these agencies determined that the research- ers could not be granted access to the respective databases. Because of these limitations, the researchers developed an alternate approach using the datasets listed below. Thus, the results of this research project should be considered as a proof of concept that entities with full access to confidential data can build on to achieve their desired project evalu- ation objectives. The primary datasets used in this research effort were the following: ⢠Channel Portfolio Tool (CPT) (1). CPT accesses the dock-level, Corps-use-only tonnage database from the Institute for Water Resources (IWR) Waterborne Commerce Statistics Center (WCSC) to analyze the extent to which commercial traffic uses maintained chan- nel depths. ⢠Freight Analysis Framework 3 (FAF3) Public Data. The FAF3 data are based on the 2007 Commodity Flow Survey (CFS) and integrate data from a variety of sources to create estimates for transported tonnage and value by origin, destination, commodity, and mode for a base year (currently 2007 to be consistent with the CFS) and forecasts, currently through 2040. ⢠Federal Railroad Administration National Grade Crossing Inventory. The U.S. DOT National Highway-Rail Crossing Inventory Program is a uniform national database of grade crossing characteristics and traffic data that can be merged with accident files. Integrating MTS Commerce Data with Multimodal Freight Transportation Performance Measures to Support MTS Maintenance Investment Decision Making
10 Five port complexes were selected as case studies on which to apply the model: ⢠Duluth, Minnesota ⢠Hampton Roads (Port of Virginia), Virginia ⢠Huntington, West Virginia ⢠Plaquemines, Louisiana ⢠Portland, Oregon The Port of Portland was further divided into deep draft (import/export) traffic and inland water (Columbia/Snake River) traffic. Therefore, there actually were six case studies. For each selected port area, the researchers identified the landside corridors that tie into the waterfront origins and destinations for the primary commodity flows. Appendix C con- tains a description of the primary origin-destination corridors and the modal assignments for each corridor at each port. The researchers developed measures that defined the current (without project or before condition) utilization of the waterway asset and the post-project (with project or after condition) utilization of the asset, allowing for an evaluation of main- tenance project activities. For the modeling effort, the project team focused on the maximization of cargo flow based on tonnage, based on conversations with members of the NCFRP Project 42 panel. This measure could be expanded or even substituted in the model discussed in this report. For waterways, the conceptual approach was to assume a certain loss of water depth due to lack of maintenance dredging. The researchers analyzed the commodities and tonnage that historically moved within the affected stratum at each port, and then analyzed a 10 percent, 20 percent, or 30 percent increase in that subset of the portâs traffic. Initial results showed that, in many cases, the 10 percent and 20 percent levels had insignificant effects. In fact, in some cases, even a 30 percent increase in the tonnage at the affected depth caused a very small increase in the port-generated traffic relative to the total traffic in the port com- munity. In order to simplify the analysis and make the results meaningful, the researchers chose to use only the 30 percent level for this study. Tonnage that was assigned to the affected stratum of the waterway is referred to as âproject depth tonnage.â Since this is the tonnage that dredging will directly affect, the objective function in the model attempts to maximize this variable. Lock capacity/utilization is greatly dependent on available operating time and tow pro- cessing time. By improving the condition of the lock, it is possible to reduce the hours the lock is out of service and increase the capacity of the lock to move additional project depth tonnage over a given time frame. The researchers used a mathematical/statistical analysis of a number of locks performed by Oak Ridge National Laboratory (ORNL) to estimate lock capacity. Appendix D contains a detailed description of this methodology. The potential effect of a lock maintenance project was calculated by identifying the lowest annual average delay from 2000 to 2012 and using it as the target (after project) condition. The difference between the theoretical capacity of the lock based on historical data and the theoretical capacity of the lock with the optimum delay factor (target condition) was taken to be the potential tonnage increase for the lock. For inland port waterway segments and coastal ports, the researchers used a metric based on channel depth as a utilization indicator. By examining historical average tonnage through- put at various draft levels during periods when adequate channel depth was available (as CPT does), it was possible to develop the tonnage by commodity that has historically transited in the depth stratum being affected by maintenance dredging. In the calculation of how much tonnage could be potentially added to particular waterway segments, constraints caused by water depth and lock throughput capacity (for segments with locks) were considered.
11 The highway corridor analysis tapped into previous and ongoing work conducted by Texas A&M Transportation Institute (TTI) through its Urban Mobility Research Program (UMRP). Based on its previous work across the country, UMRP was able to determine that any potential highway congestion effects from increased port traffic would be noticeable only in the immediate vicinity of the port. The capacity analysis of potential rail corridors consisted of (1) identification of line segments where congestion is likely to be severe; (2) determination of the theoretical train count capacity based on methods developed in the National Rail Freight Infrastruc- ture Capacity and Investment Study (2); (3) determination of current train counts; and (4) calculation of remaining capacity, assuming current operating parameters and track conditions. The tonnage for each primary commodity-corridor combination was divided among the highway, rail, or water modes, based on the FAF3 data. Appendix C provides the modal assignment for the corridors associated with each port. Once the corridor modes and tonnages were established, the researchers assessed each corridor to see if there were any constraints that would prevent the tonnage increases from flowing through the corridor. Appendix E discusses the constraints. The operations research model is built on a network using network flow techniques. For each port area, a network covering the area of interest was established. The objective of the model was to maximize the possible freight throughput served by the restored or increased system availability by first identifying lock and non-lock waterway segments/ links for maintenance. In the case of dredging, the model determines dredging location and depth; in the case of locks, it determines reductions in average delay. The model then evaluates their associated combined effects on the system network including landside linkages. The freight network analysis is based on historic freight demand, as outlined above. In order to determine a reasonable cost per unit of maintenance activity, the researchers compiled data on dredging and lock expenses at the ports and potentially constrained locks. The flowchart shown in Figure S-1 depicts the means by which data are fed into the model and the outputs that result. The researchers designed several scenarios to test various aspects of the model (sensitivity analysis). They used various budgeting levels and various project combinations in order to analyze the effect of different variables on the modeling results. Findings The most obvious issue that surfaced in this research effort is that there is a lack of the kind of data needed for developing a model that can support MTS maintenance investment decision making by being correlated between the modes and almost no accurate data on origins and destinations (in the case of publicly available data). However, combining trade literature with publicly available information from the Corps (primarily CPT data); FAF3; and regional, state, and local freight studies makes it possible to gain enough of an under- standing of a portâs primary trade flows to be able to provide meaningful input into the model developed for this project. Furthermore, the metrics currently used for prioritizing maintenance and improvement expenditures are not based on post-project evaluation of the effects of such spending; rather, they are based on presumed measures of the general importance of the asset: tonnage, sus- tainability of the region (i.e., how much the region depends on the waterway for its eco- nomic survival), and so forth. In order for a model of the type developed under NCFRP Project 42 to have real value, there must be a way to measure the effect of maintenance projects on freight flows.
12 Certain findings became obvious, even before running the model. They are explained in depth in the body of the report. In summary: 1. Ports vary widely in the degree to which the bottom strata of water depth are used in terms of tonnage relative to total port tonnage. 2. Highways are not at all critical in some instances, depending on the primary commodities and the mode they depend on. 3. Highway congestion caused by port truck traffic is a constraint only in the immediate vicinity of the port terminals; it does not generate city-wide or area-wide congestion. Operations Research Model Monetary data ($): ï¼Dredging cost (cij) ï¼Lock improvement cost (wij) ï¼Available budget (B) Current availability data (tons): ï¼Segment (s ij) ï¼Lock/dams (l ij) Capacity increase for a unit of improvement (tons): ï¼Segment (q) ï¼Lock/dams (r) Commodity data (tons): ï¼O-D demand (Dijk ) Model parameter: ï¼Weighting factor (Ï ij) Input Data Output Results Results: ï¼The depth of dredging (dij) ï¼The increased operational time of lock/dam (hij) ï¼The total ï¬ow of commodity k (xijk ) ï¼The total ï¬ow between each O-D pair (fijk ) Figure S-1. Flowchart of operations research model.
13 Appendix F shows the results of the model for each of the selected dredging scenarios. In summary: 1. The improvement of only a few waterway segments could really make a difference. Improving segments that already have little tonnage throughput or are already con- strained on the landside will not affect total system-wide tonnage capacity or throughput. 2. For this reason, not all eligible segments need to be fully maintainedâsome not at all. 3. It is possible for budgets to be set too high. Even if money is available, it might as well not be spent on segments that may not affect total tonnage throughput due to low demand or landside constraints. 4. In some cases, itâs all or nothing. Decreasing the budget by just 10 percent (from 100 per- cent down to 90 percent) results in a zero maintenance decision. This occurs in cases where a port has only one water origin-destination corridor. 5. Locks are not a capacity constraint as long as they continue to operate as well as they have historically. Note that the model is artificially constrained and does not incorporate all factors that may have to be considered in a real world situation. For example, shoaling effect is not consid- ered. The model only assumes a static state, which means dredged depth will remain effective for a period of time. Additionally, system reliability often requires continuous maintenance of the waterway system so that it always has a capacity that is higher than needed in order to hedge against capacity reduction due to external unforeseen events such as weather and other incidents.
