Impacts of Policy-Induced Freight Modal Shifts (2019)

Below is the uncorrected machine-read text of this chapter, intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text of each book. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.

1 The main goal of NCFRP Project 44 was “to develop a handbook for public practitioners that describes the factors shippers and carriers consider when choosing freight modes and provides an analytical methodology for public practitioners to quantify the probability and outcomes of policy-induced modal shifts. . . .” To achieve this goal, the research team undertook a major effort to secure access to the confidential Commodity Flow Survey (CFS) microdata—the most comprehensive freight dataset in the United States—to com- plement the CFS with confidential shipper data and modal data and to use state-of-the-art econometric modeling techniques. This significant research effort overcame some of the most significant challenges to the study of freight mode choice in the United States. Working with the CFS microdata file allowed the team to go well beyond previous simi- lar research. This unique dataset provided the cornerstone of a behavior modeling effort to gain insight into the roles played by important variables such as door-to-door travel times (referred to in this report as “transit times”), freight rates, and commodity types. These analyses were complemented with in-depth analyses of the historical patterns of freight mode shares and technical identification of the influencing factors at the market (macro) level and the shipper (micro) level that shape freight mode choices. The research team also conducted a rigorous technical review of the potential modeling methodologies, both econometric and supply-chain-based that could be used to develop freight mode choice models. From this review, the research team conducted a critical evaluation of the advan- tages and disadvantages of the various methodologies and selected the most appropriate ones to be pursued in NCFRP Project 44. This project presented substantial challenges to the research team. The research effort involved gathering the 4.5 million records in the confidential CFS microdata file, merging them with the even larger Longitudinal Business Database (LBD), preparing custom-made datasets with the modal attributes—transit times, freight rates, and three different versions of generalized costs—and merging these data to prepare the master dataset for estimation of freight mode choice models. Using this unique dataset, the team estimated 4 sets of market-share models and 12 sets of shipment-level models and analyzed the more than 1,300 models estimated to identify the ones that met the conditions of being conceptually valid and statistically significant. In addition to the statistical modeling effort, the research team collected information from numerous market participants to inform the final model structures and policy analy- sis. The team conducted in-depth interviews with 10 market participants comprising four shippers, four receivers, and two carriers to gain insights about their mode choice decisions. In addition, the team conducted six case studies of freight mode policy efforts in the United States providing a review of the policy objectives, structure, and implementation. Based on data related to two of these case studies, the Crescent and Heartland Corridors, the research S U M M A R Y Impacts of Policy-Induced Freight Modal Shifts

2 Impacts of Policy-Induced Freight Modal Shifts team conducted numerical experiments using hypothetical examples. These experiments were conducted to gain insight on the ability of the estimated models to produce sound estimates of the impacts of hypothetical policies. This is the most comprehensive research effort on freight mode choice in the United States to date. The most unique aspect of the work was the usage of several confidential datasets. Use of the CFS and other confidential data, pioneered by the research team during the research on freight generation published as NCFRP Research Report 37: Using Commodity Flow Survey Microdata and Other Establishment Data to Estimate the Generation of Freight, Freight Trips, and Service Trips: Guidebook, provided the team with access to vast amounts of high quality data that provided insight into freight mode choice behavior that would be otherwise unreachable (Holguín-Veras et al. 2017). These data provided an extremely solid foundation for the estimation of freight mode choice models. At the same time, the size of the resulting datasets and the protocols necessary to ensure the security and privacy of the confidential data had the unintended effect of creating work- ing conditions far from typical in a transportation research project. A brief explanation of these aspects is essential to understanding the decisions made by the team. Because of the confidential nature of the data, they could only be accessed at one of the Census Bureau’s Research Data Centers (RDCs), following strict protocols of confidentiality and privacy, which required research team members to secure special sworn status as Census Bureau agents. More than 100 person-trips were conducted by research team members to the RDC in New York City to access the data and perform the econometric analyses. A complicat- ing factor was that, due to the tremendous size of the joint dataset, the computation time required to estimate some of the models took days and in some cases weeks. Frequently, to ensure that there was enough computing power for the rest of the users of the Census Bureau servers, the server administrators had to cancel the computer jobs submitted by the research team. Thus, the research team had to resubmit the jobs the next time they traveled to the RDC, which is when they were able to check the status of the submitted jobs. The net result was a very protracted modeling effort. However, these inconveniences were a small price to pay to access the quality data available at the Census Bureau. In conducting the case studies, the research team encountered a different set of chal- lenges, most notably, the lack of publicly available data about public-sector experiences with freight mode policy. To overcome this situation, the research team critically examined public information, conducted interviews, and analyzed the sparse data available to gain insight into the effects of public-sector efforts to foster changes in mode shares. The high- level perspective provided by the case studies was complemented with the bottom-up view provided by a distinguished group of private-sector leaders who participated in the in-depth interviews (IDIs) with the research team. The participants represent a wide range of industry sectors and company sizes and operations that cover the entire geography of the United States. During the IDIs, the participants provided their views about how to enhance the perfor- mance of the major freight modes in the United States. The chief findings produced by these efforts are summarized next. Development of Freight Choice Models The team conducted a rigorous review of the modeling techniques that could fulfill the objective of providing “an analytical methodology for public practitioners to quantify the probability and outcomes of policy-induced modal shifts.” The review, which included econometric and supply-chain-based models, concluded that econometric techniques

Summary 3 represent the best combination of conceptual validity, ability to capture the effects of the various key variables, and practicality. The review also concluded that supply-chain-based approaches, such as the Intermodal Transportation and Inventory Cost (ITIC), are not viable alternatives for freight mode choice analyses on account of their onerous input data requirements, which are very difficult to meet in practical applications. These conclusions are in agreement with current international practices and with the chief findings of the road map for truck size and weight research (Special Report 328 2018). Two different types of econometric models were estimated. The first family of models, referred to as market-share models, attempt to directly estimate the market shares of rail and truck for a given commodity type as a function of the average values of transit times, freight rates, and generalized costs. The second family, referred to as shipment-level models, estimates the probability that a given shipment will be sent by either rail or truck. A uniquely important feature of shipment-level models is that they are able to consider the impacts of policy efforts that affect transit times and rates at a minute level. In contrast, market-share models can only consider aggregate average variables. The econometric estimation of these models required assembling a dataset containing shipment data from the CFS, shipper data from the LBD, and the modal data (i.e., transit times, freight rates, and generalized costs), which had to be prepared by the research team. Notwithstanding the research team’s access to data not typically available to researchers, the data assembled imposed notable constraints on the number of modes that could be considered and the number of variables that could be incorporated into the models as explanatory variables of freight mode choice. The number of modes considered was con- strained by the CFS data because the vast majority of the data is for the truck and rail modes (rail and intermodal). The analyses conducted by the research team concluded that the shipment data available for the other modes were too small to try to estimate models that included these modes. Another consideration was the substantial effort required to prepare the modal data for the modes with smaller numbers of observations. As a result, the freight mode choice only considers rail and truck alternatives. Ideally, the modal data should include all the relevant variables—such as transit times, freight rates, reliability, and quality of service—that influence freight mode choice for the same time period during which the CFS was conducted. The obstacle is that data of this nature were not found and probably do not exist (with the exception of the 2012 Confi- dential Waybill Sample, which was used by the research team). As a result, the team had to impute transit times and freight rates using sensible assumptions about travel speeds, trans- fer times, and unit costs, among others. Unfortunately, variables such as reliability cannot be imputed a posteriori on the basis of the data available. As a result, such variables could not be included in the models. Ultimately, a total of 1,346 models were estimated. The shipment-size models amounted to 318 models. The market-share mode choice models totaled 266, and 762 shipment- level mode choice models were produced. To ensure the models could support a wide range of potential applications, the team applied three different weighting schemes to ensure that the models replicated the market shares in the CFS microdata, the domestic market shares embedded in the Freight Analysis Framework (FAF) version 4, and the total cargo handled in the country, domestic plus imports and exports by commodity at the level of two-digit Standard Classification of Transported Goods (SCTG) (FAF 2018). The number of unweighted shipment-level models estimated are 255 (6 pooled models, 249 commodity-wise models). Weighted shipment-level models totaling 508—250 are weighted using domestic cargo only (3 pooled models and 247 commodity-wise models), and 258 are weighted using total cargo, which includes, in addition to the domestic cargo, imports and exports (7 pooled; 251 commodity-wise models).

4 Impacts of Policy-Induced Freight Modal Shifts The results indicate that the choice of freight mode is influenced by transit time in only 3 of the 42 commodity groups considered (i.e., Paper Articles, Pharmaceuticals, and Transportation Equipment). Although this may come as a surprise to some, this result is in agreement with one of the chief results from the IDIs. As discussed in connection to the IDIs, only 2 of the 11 participants in the IDIs mentioned transit time as a key influencing variable. This finding has tremendous implications as, quite frequently, public-private efforts to foster rail usage are based on ways to reduce transit times. The results of this research suggest that these efforts are not likely to be as effective in achieving the stated objectives as they are expected to be. However, since these models include both freight rates and transit times, the subjective values of time (S-VOT) can be empirically estimated. The results indicate that the commodity group of Paper Articles has the highest value ($3.99/shipment-hour), followed by Pharmaceuticals ($1.15/shipment-hour), and Trans- portation Equipment ($0.78/shipment-hour). In contrast, freight rates were found to be the most influential explanatory variable. It suffices to say that 34 out of the 42 commodity types considered have commodity-specific parameters. However, the downside of these models is that they do not consider transit time, which in theory should be a factor because the opportunity cost of the cargo in transit is a resource cost. To have alternative models that consider transit times, albeit in a simplified manner, the research team created a generalized cost metric that included freight rates and transit times using the Intrinsic Value of the Cargo (IVC). The estimation of the shipment- level freight mode choice models using these generalized costs revealed that the 5 percent version provided the best results, in terms of the number of conceptually valid and statisti- cally significant models. This result makes sense, as the opportunity cost of 5 percent is the one most likely to represent real-life conditions. Opportunity for Mode Shifts: Lessons from the Case Studies The team conducted six case studies of freight mode policy efforts in the United States, providing a review of the policy objectives, structure, and implementation. The case studies, selected with input and approval of the NCFRP Project 44 panel, spanned a variety of policy types, modes, and geographical areas. The chief lessons from the case studies are summarized in this section. The Palouse River and Coulee City Railroad (PCC) Short-Line Freight System The state of Washington purchased a 300-mile, short-line freight system in eastern Wash- ington, providing grain shippers with an alternative to a truck-to-barge system. This public investment has spurred private-sector commitment, maintained transportation options, and provided shippers with alternatives that have allowed them to minimize transportation costs, fostering regional economic development. The Crescent Corridor The Crescent Corridor is a rail infrastructure improvement project worth more than $2.5 billion that is operated by the Norfolk Southern Railway (NS). The corridor, under development since 2008, consists of a 2,500-mile network of existing rail lines that extends from New Jersey to Memphis and on to New Orleans. Corridor projects include straightening curves, adding signals, and building new track and rail terminals. NS also partnered with five

Summary 5 states to improve the system and develop regional intermodal freight distribution centers. While the project has appeared to have shifted a significant amount of freight from trucks to rail intermodal, the change appears to be only a fraction of initial forecasts, illustrating the difficulty in both shifting freight among modes and in forecasting those shifts. The Heartland Corridor The result of a $397-million public-private partnership completed in 2010, the Heart- land Corridor connects the Port of Virginia to major destinations in the Midwest including Chicago, Detroit, Columbus, and Cincinnati. The partnership included NS, the FHWA, and the states of Virginia, West Virginia, and Ohio. It raised clearances in 28 tunnels and 24 other overhead obstructions to allow the transport of double-stack intermodal trains. It included intermodal capacity improvements made at the Rickenbacker Airport in Ohio and new intermodal terminals in Roanoke, Virginia, and Prichard, West Virginia. Given at least a doubling of intermodal traffic on the corridor, there is a lesson to be learned: policy- makers should explore investments that greatly improve efficiency at a reasonable cost and implement them where they appear feasible and efficient. However, this case also offers a cautionary tale: the $30-million Pritchard terminal, which has not achieved the operating goals that were originally established for the facility. Chicago Region Environmental and Transportation Efficiency (CREATE) Program A collection of rail and roadway improvement projects, this $3.8-billion public-private partnership consists of over 70 different projects. Members of the partnership include U.S. DOT, the Illinois Department of Transportation (DOT), six major freight rail carriers, and two passenger train systems. CREATE program projects have achieved a significant reduc- tion in delays experienced by freight and passenger trains, as well as truck freight and pas- senger automobiles. CREATE represents the first time state and local governments have collaborated with the railroad industry to solve the problem of automobile and rail conges- tion on such a large scale, and a lesson learned is that these partnerships can work. However, critics contend that the rail industry has not shared in the costs to the same extent as they have enjoyed the benefits, so there is a lesson learned there as well. Albany Express Barge The Port Authority of New York State and New Jersey (PANYNJ) is interested in find- ing alternative ways to move containerized cargo to and from ports to avoid the increased amount of road, bridge, and tunnel congestion in the region. The Albany Express Barge service started in 2003, but was suspended in 2006. The reasons cited for suspension of the service included a lack of funding, a lack of interest from shippers, and higher-than- anticipated transportation costs. Planning begun in 2014 to restart the service has not succeeded. The major lesson learned is that a mode shift from truck to container on barge (COB) is difficult to establish, and one of the major challenges to establishing this mode shift is reaching the minimum volume of cargo that ensures financially viable operations. However, factors that promote COB programs are not going away, including New York City congestion, environmental issues, and truck driver shortages. Truck Route Management and Community Impact Reduction Study Less than 10 percent of freight tonnage in the New York region is carried by modes other than truck. The relatively low rail mode share can be attributed in part to limited

6 Impacts of Policy-Induced Freight Modal Shifts freight rail connections, especially to Long Island, and in part to historical reliance on rail- to-barge car floats that by the middle of the twentieth century were no longer competitive. New York City’s DOT and other related agencies have focused on freight and have under- taken several large-scale plans and initiatives, including the 2015 Urban Freight Initiatives, the Smart Truck Management Plan, NYC DOT Strategic Plan 2016, and Freight NYC. New York City’s size, geography, and limited transportation infrastructure all contribute to a difficult environment for freight. Truck size and weight policy alternatives are restricted by the policies and politics of multiple jurisdictions, aging and inadequate bridges, safety con- cerns on local streets, and the lack of alternative freight rail and freight barge infrastructure. The expected rapid growth of freight movements presents a difficult challenge to overcome. However, given the lack of funding for large-scale transformational projects, policymakers and planners must continue to use all of the tools at their command. Overall, the results of the case studies suggest the following for policymakers and planners considering mode shift policies and projects: • Understand the economics of mode choice, • Consider vehicle types within modes, • Expect resistance to change, • Recognize the need for time and longevity to realize the targeted mode shift, • Design policies or projects to change freight mode choice, • Develop partners and encourage private investment, • Examine the potential for economic development, and • Analyze the benefits and costs to each stakeholder. One last important lesson learned is that it is difficult to start new intermodal services or terminals from scratch to foster changes in freight mode choices from truck to rail or water. Evaluate risk thoroughly, forecast carefully and conservatively, line up business in advance, seek partners, and minimize up-front investment. IDIs The IDIs revealed that the top four factors influencing mode choice are • Freight rates. The IDIs identified two factors that were most frequently mentioned as influencing mode choice. One is the rate associated with the mode, although it is never the only determinant of mode choice; the choice is usually associated with other factors because the company needs to maintain a certain level of quality in its operations. So, if the cheapest mode option does not provide the company with the minimum level of service required, typically that mode will not be selected. • Quality of service. The second of the two factors most frequently mentioned as influ- encing mode choice was quality of service, which encompasses “reliability” and “level of service.” The need for a high level of service leads to a preference for trucks, due to their service and flexibility. A reoccurring theme in the IDIs was the need to balance cost and reliability in selecting the mode. However, on-time delivery is a major factor that drives the decision because if time is a factor, most companies will opt for more expensive options to get the shipment to the destination on time. • Product type. The type of product being shipped is another significant factor influencing mode choice. One of the receivers specified that high-value products are sent by truck due to the time sensitivity of the demand, while standard products that are low value are shipped by rail. Mode choice is also based on whether the product is perishable or non-perishable.

Summary 7 • Seasonal changes. Seasonal changes include the effects of season-related weather on modes, as well as varying sales periods. Inland waterways in some locations are closed during the winter due to freezing, and for rail winter weather may also result in delays. When weather is a concern, use of rail, intermodal, and barge decreases, and use of trucks increases. The top three suggestions for improving factors affecting mode choice from the IDIs are the following: • More consistency in rail delivery times: Most of the receivers and shippers are willing to use rail if the service is up to their standards. A shipper pointed out that it is not the longer transit time that affects the use of rail, but rather the inconsistency of those transit times, pointing out that even an early arrival of goods can lead to storage problems and extra costs. One of the shippers expressed willingness to use rail if it could offer competitive pricing and more consistent service. • Dredging and preserving the land for waterways: An inland waterway carrier is con- vinced that it could increase its market share with a few improvements. The first is the dredging of local channels that have not been dredged for years. The second is to preserve the land along rivers, which is now being converted into either residential or recreational centers, for loading/storage. • Increase the allowable weight limits on trucks: A shipper and large receiver said they would encourage an increase in the allowable weight limits for trucks. The shipper found that trucks would often reach their weight limit before getting filled. So, increasing those limits would allow for more being loaded on a single truck, which would reduce trans- portation costs and lessen externalities by reducing the number of truck trips required for a given amount of cargo. Numerical Experiments The NCFRP Project 44 research team conducted numerical experiments to illustrate some of the possible applications of the mode choice models estimated as a part of this project. To give the numerical experiments a real-life flavor, the team decided to use the Heartland and Crescent Corridors as inspiration for the numerical experiments. It is worth noting that these applications do not purport to be an evaluation of the real-life impacts of the selected projects, as the applications are nothing more than “real-life inspired scenarios.” To construct these scenarios, basic details of the projects, publicly available data, and reasonable assumptions were used. The analyses demonstrate the application of both types of mode choice models: (1) market-share mode choice models and (2) shipment-level mode choice models. The application of the market-share models to the scenario inspired by the Heartland Corridor estimated the impact of a shorter route on mode split for selected com- modity types. The analysis for the Crescent Corridor focused on the impact of travel dis- tance and transit times on mode split for selected commodity types. The application of shipment-level models for the Heartland and Crescent Corridors focused on estimating the mode split along each corridor for selected commodity groups. In all cases, the numerical experiments yielded sensible results that confirm the expectations.