Freight Demand Modeling and Data Improvement (2013)

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13 C h a p t e r 1 Background Freight transportation in the United States has been a subject of growing interest to policy makers, state departments of trans- portation (DOTs), metropolitan planning organizations (MPOs), and varied stakeholders, particularly since the passage of the Intermodal Surface Transportation Efficiency Act (ISTEA) of 1991. The overarching policy challenge for trans- portation agencies is to make informed investments in trans- portation infrastructure that support efficient freight mobility and access. Long-range transportation plans, transportation improvement programs, corridor studies, and project develop- ment need to have a more meaningful freight focus. This need to better integrate freight with transportation planning recog- nizes the importance of goods movement to economic perfor- mance and meeting consumer needs. Progress in freight planning also will require effective communication and coordi- nation with the private sector (shippers and carriers) and local government, particularly with respect to development and land use considerations. Although more than 20 years have passed since ISTEA was enacted, accurate and timely freight planning and forecasting still remain formidable challenges with sub- stantial opportunity for improvement. Increasingly, the importance of freight data and modeling is being recognized. Transportation investments are capital intensive and represent long-term commitments for taxpay- ers and stakeholders in the public and private sectors. It is important that transportation planners possess both the tools and the skills to forecast freight demand and to analyze scenarios and investment alternatives as part of the overall transportation analysis. Travel demand forecasting, however, has historically been oriented toward passenger transporta- tion. Passenger-oriented forecasting models draw on eco- nomic and demographic variables that are insufficient and sometimes irrelevant for estimating future freight demand, which is shaped by a much wider range of factors as a result of a complex logistics chain. Freight transportation has undergone dramatic change nationally and globally in recent decades, much of it occur- ring behind the scenes and outside of the public eye. Popula- tion growth, changes in consumer behavior, underlying economic forces (both national and global), and advances in technology have driven major changes in freight transporta- tion. The increasing complexity of the logistics and supply chain process has made it more difficult for public and private decision makers to understand the implications of freight trends for the planning process for capital improvements related to the movement of goods through the U.S. transpor- tation system. Incorporating freight movement considerations into the transportation planning process has become increasingly dif- ficult at a time when these influences are more critical to the ability to forecast long-term trends and plan for future infra- structure needs. This difficulty is exacerbated by the underly- ing dilemma faced by decision makers involved in any transportation project or policy decision in which freight transportation is a key consideration: the physical, opera- tional, economic, and political disconnects between the users of the system (shippers and carriers) and those who benefit from the system (businesses and consumers). Some MPOs engage the private sector through freight working groups; however, there are far more regions where these stakeholders may not fully understand the benefits the system provides, the implications of their own decisions on freight transporta- tion, and the real and perceived negative impacts of freight movement. The standard approach to forecasting freight traffic does not serve decision making well for several reasons: • Transportation decisions are largely based on passenger movements. Freight movement is far more complex than passenger movement and typically involves multiple travel modes and transportation characteristics at different points in the production and delivery process. Introduction

14 • The impacts of freight movement on the transportation network and the need for decision makers to understand the various elements of the logistics process vary widely depending on geography. Existing data resources are best suited to large geographic scales and do not translate well to local levels. • Transportation forecasting and modeling practices are usually based on average trip generation rates for various land uses, but freight is heterogeneous in nature and does not lend itself well to average quantities of production, consumption, and movement. • Freight transportation is carried out by private shippers and private carriers whose impact is often felt on public facilities. However, freight’s complex production, trans- portation, and storage elements are not readily apparent to public decision makers. • Similarly, the growing role of third-party freight transpor- tation providers and logistics services makes freight less visible to many shippers and receivers. This change makes it more difficult for public agencies to gather information on freight activity through traditional shipper surveys. • Different commodities often have significant variations in travel modes and logistics patterns for identical origin and destination points. • Peaking characteristics for passenger travel are typically seen by time of day and day of the week, but freight move- ment often demonstrates substantial seasonal variations that vary by commodity type. • Freight typically moves over long distances with modal transfers and changes in freight characteristics at different points in the supply chain (raw materials to components to finished product). Freight movement is also more mark- edly affected by the unique aspects of international trans- portation (e.g., customs requirements, security). Because of the weaknesses inherent in current freight data and modeling practices, public and private decision makers have limited information on which to base critical freight- related decisions. These decisions may relate to infrastructure investment, economic development, business planning, land use, capacity enhancements, and logistics. The implications of poor or ill-informed decision making in the realm of freight transportation are potentially more far-reaching than for pas- senger transportation in terms of economic costs, environmen- tal degradation, and loss of competitive advantage for a city or region—though perhaps less obvious, except in hindsight. research purpose The SHRP 2 C20 research initiative was developed to provide a strategic framework for continuous improvement in freight forecasting, planning, and data, and in the acceleration of innovative breakthroughs. The stated objective was to “foster fresh ideas and new approaches to designing and implement- ing freight demand modeling.” This objective promotes fun- damental change in the integration of freight considerations into the planning process while recognizing that although various short-term measures represent marginal improve- ment to freight movement planning and current practice, they contain many inherent weaknesses. Development of better freight demand models and data sources will provide the tools necessary for public and private sector planners and other leaders to make better decisions. These decisions would be based on relevant information regard- ing the current movement of goods, modal variations, shipping costs, time in transit, consumption rates, logistics chains, and other information that is critical to the freight industry. Documents produced as part of the SHRP 2 C20 research project include this detailed report, the Strategic Plan, and a speaker’s kit. The speaker’s kit is available online at www.trb .org/Main/Blurbs/167628.aspx. Strategic plan Development The Freight Demand Modeling and Data Improvement Strate- gic Plan was developed through a highly inclusive process with stakeholders from U.S. and international freight planning communities. The plan’s aim to foster innovation in freight demand modeling and data was informed through previous research, discussion, and outreach at various events through- out the United States, as well as the Innovations in Freight Demand Modeling and Data Symposium conducted in 2010. The development of the plan focused on collecting infor- mation and ideas to • Determine strategic needs by defining an optimal perspec- tive of how the freight planning process should work in an unconstrained environment, with all of the model param- eters clearly identified and the necessary data available. The goal is to promote the development of new tools for modeling and data collection and generate ideas for dra- matic changes in freight transport planning practices. • Identify and promote innovative research efforts that could help develop new modeling and data collection and pro- cessing tools in the near and long-term future. These efforts should include different geographic scales, with sound the- ories and approaches, forecasting methods, and relevant model and data tools for the appropriate geography. • Establish and strengthen links between freight transporta- tion planning tools and data, as well as other aspects of planning and public policy in which freight movement has major implications. • Leverage existing practices, innovations, and technologies into a feasible freight transportation planning and model- ing approach.

15 • Establish a venue for promoting and supporting innova- tive ideas, modeling methods, data collection, and analysis tools; such a venue is critical to sustain further research. Modeling and Data Issues in Brief Passenger travel demand models, data, and practices are well defined. Over the past 60 years, these tools (and the support- ing data) have been developed through an iterative process among the modeling community. Funding was available to make steady incremental progress. This long-term develop- ment allowed the science behind the modeling to continue to evolve. Federal, state, and local requirements focused on pas- senger travel because these movements represented the majority of the traffic on the roadways. After the four-step trip-based modeling process was adopted as the standard in the early to mid 1970s, which coincided with the fulfillment of the requirements of the Clean Air Act of 1970, there were initial breakthroughs and innovations. From the 1980s to the present, there have been fewer large leaps and more minor process improvements in the state of the practice. Freight demand models and data have not received the same attention as passenger transportation. This is primarily a result of the highway system being developed to accommodate pas- senger (and military) vehicles, and the justification for building the highway system required tools to estimate demand (and later air quality impacts). Freight has been difficult to model and freight data have been difficult to collect because • Data have historically not been available regarding com- modities, shipments, demands, and production cycles; • Freight transportation is primarily a private sector busi- ness activity and little has been understood about the sup- ply and logistics chains from a private sector point of view; • The modeling community has not understood the broad economic influences on local freight movement (and vice versa); and • Freight model development is driven by the available data, which are lacking in detail for many applications and decision-making needs. Recently there has been an acknowledgment that freight models and data are critical to assessing national, regional, and local highway capacity; economic development initia- tives; and for informing the transportation planning process. Further, it has become clear that the existing tools and data are limited primarily to national-level and larger urban areas. Even these have limited application in informing decision makers, and recent pressures on state and local budgets have scaled back freight modeling and data improvement initia- tives and training. In recent years global positioning systems (GPS), weigh in motion (WIM), and other electronic data collection methods have been used to inform models on an ongoing basis. These methods provide good truck movement data, but they do not provide commodity flow information or data associated with the movement of goods via rail, water, or other freight modes. Having access to and understanding these data can assist planners and decision makers, whose aim is to reduce conges- tion, increase efficiency, promote economic development, and make informed land use decisions. Need for Freight Modeling and Data Innovation The historic inadequacies of freight modeling and data are now juxtaposed against the need for better freight decision making. The planning, economic development, and freight communi- ties now require that substantial leaps in freight modeling and data innovation occur in the near and long-term future. In 2007, $11.7 trillion worth of goods were transported via the U.S. transportation infrastructure (Research and Innova- tive Technology Administration 2009), and truck miles (94 billion) accounted for 7.5% of the total vehicle miles trav- eled that year (U.S. Census Bureau 2009). Considering the capacity impact that freight has on transportation infrastruc- ture, planners must be able to account for freight movements and potential shifts to adequately plan for the future. It is widely acknowledged within the modeling community that existing tools are inadequate for most regional and local freight planning applications. The existing methods are typi- cally oriented toward national data; local applicability of national data is limited, so tools are typically not robust. In addition, there is growing recognition that tools should incorporate land use, economic trends, and freight activity. Current innovations are trending toward electronic data col- lection tools such as GPS and WIM rather than advancement of the models themselves. To achieve the necessary advances, the freight modeling and data community must understand the unique character- istics of freight and the modeling and data challenges associ- ated with the development of needed tools. A brief overview of these topics is presented below. Unique Characteristics of Freight Freight forecasting and modeling are challenging because the transportation of freight involves unique transportation pro- cesses and is subject to highly complex and variable external influences. Goods movement is affected by short-term changes in the conditions that drive supply and demand for various products and raw materials. These variables are not easily quantifiable in long-term forecasting. As a business activity

16 that is inextricably tied to the behavior of producers and con- sumers, freight transportation is reactive by nature and meets the classic economic definition of a derived demand (i.e., demand for freight transportation and affiliated services occurs as a result of demand for products and raw materials). The derived nature of freight transport activity renders traditional transportation planning tools and methods unsuitable for accurate forecasting. Important factors that make freight demand difficult to quantify and predict include • Transportation activity, including transport modes and equipment, varies widely for different types of commodities. A load of coal shipped from a mine to a power plant 50 miles away, for example, would likely be moved by rail because of the efficiency of the rail system in accommodating heavy loads moved in large quantities. The same quantity (mea- sured by volume or weight) of household electronics, on the other hand, would likely be moved by truck over any dis- tance shorter than several hundred miles. • Time sensitivity is a major factor in the decision-making process for shippers of many commodity types. Materials of low unit value that are moved in large quantities, such as coal or aggregates, are likely to be moved via slower modes such as rail or barge for domestic transport or by ship for international transport. Conversely, high-value products, especially products with a limited shelf life, such as pharmaceuticals or fresh food, are more likely to be delivered by truck for domestic moves or by air for inter- national transport. • Unlike passenger travel, for which peak periods of activity tend to occur in predictable patterns by time of day and by day of the week, freight transportation is more heavily influ- enced by seasonal variations. For example, peak demand for consumer products tends to occur in the months and weeks before the December holiday season, while demand for materials used to produce energy fluctuates by energy type (e.g., the heaviest use of gasoline occurs in the summer months, while home heating oil is used almost exclusively in the winter). • Freight corridors in the United States go beyond jurisdic- tional boundaries and link MPOs, states, and subregions within the United States and may also be connected to Canada and Mexico. Corridors like I-95, I-29/I-35, and I-5 have begun crossing jurisdictions in their planning, and although they all have different characteristics, they strug- gle with similar passenger, freight, and congestion issues. • The supply and demand for any commodity imported to or exported from the United States is influenced heavily by international trends and economic considerations that are difficult to forecast. This trend has become more impor- tant over time, and is likely to continue, as the global econ- omy has become more interconnected. The types of commodities moved around the world and the countries where these commodities originate and are consumed are influenced by factors such as currency exchange rates, political stability, demographic changes, and technological development in emerging economies. These factors are often subject to rapid change, which makes them extremely difficult to predict over long time periods. Modeling Challenges The complexity of the private freight transportation business model poses challenges to the predictive capabilities required by the public sector. Supply chains are global; however, impacts to the transportation system are felt locally. Simulat- ing freight movements to the level of detail that is useful for regional, corridor, or local planning is challenging with the tools available because the geographic scale of models and data used in the planning process needs to be refined. Sound planning tools must be developed in line with impor- tant foundational principles. Because incorporating some of these principles into useful planning tools requires a great deal of effort, it is important for the freight modeling community to understand the challenges associated with them. Some of these challenges include • Formulating a relationship between planning tools and data at different geographic levels (aggregate versus disaggregate); • Developing commodity-based tools at refined geographic scales; • Using resources to review and evaluate the results of past projections in order to refine the tools and data used for better forecasting; • Bridging the vexing gap between long-range public sector planning horizons and near-term private sector decision cycles; • Developing freight movement activity in the context of land use decisions at all steps in the process (production, delivery, and consumption); • Presenting opportunities to use freight planning data and tools to support ongoing transportation planning processes (special studies, development of transportation improve- ment programs and long-range transportation plans, freight corridor identification) and other efforts (e.g., bridge and pavement design); and • Ultimately, developing a standard and universally accessi- ble toolbox of freight planning data and tools. Only by addressing these challenges effectively and system- atically will the results required by freight modelers, decision makers, and the general public be achieved. Creating effective and useful freight planning models will depend on improving the data that support those models, while simultaneously improving the knowledge of the planning community regard- ing the workings of private industry and the resulting impact on the freight transportation system.

17 Data Challenges Current practices in modeling have been developed based on the data available. If new and more robust data sets become available, then freight models will evolve to better reflect the practices that drive the demand for freight and the result- ing impact of that demand on the nation’s transportation infrastructure. Data advances in recent years have come from enforce- ment and tracking measures (such as WIM and GPS) that were not originally intended to be used by the freight plan- ning community. Although these data sources are more accu- rate and less time consuming to use than their predecessors, they require manipulation for modeling and forecasting use. Freight models require data that are specifically collected for freight modeling and contain the detail needed to make deci- sions at various geographic levels. Some of the challenges to achieving freight-specific data include • Perceived or real difficulty in obtaining proprietary data from private sources; • Difficulty in quantifying the touring (i.e., local delivery) component of truck traffic in metropolitan areas; • Lack of clarity in the relationships between land uses and freight generation and attraction (which are less clear than for passenger travel); • Difficulty in quantifying the role and implications of empty and partially loaded freight vehicles (trucks, rail cars, ships) in the freight transportation process; • Inconsistency of data across different modes of transport (rail versus highway versus air cargo versus intermodal); • Need to manipulate data collected for other purposes in order to incorporate or expand the data for modeling purposes; • Limitations in the local applicability of national data, which typically result in less than robust tools; • Discontinuation of certain current data collection pro- cesses (e.g., the Vehicle Inventory and Use Survey) that could provide critical data at various geographic levels; • Inaccurate or nonexistent local-level commodity flow data; • Different characteristics of long-haul trucks and local deliveries, as well as empty movements for all modes (espe- cially truck); • Lack of data regarding the growing role of small-package and overnight freight relative to traditional freight move- ment; and • Matching the type and format of local and national data collection efforts. These modeling and data challenges can be overcome with the proper use of technology and resources. Modeling and data will need to be advanced simultaneously to enable the best data to be used with the best tools at a given time for a specific geography. To achieve these simultaneous advances, another category of challenges must be addressed: knowledge. Knowledge Challenges There is a present disconnect between data collection and model building that can be surmounted with the technical knowledge within the planning community, as it was during the advent of passenger travel demand models. There is, how- ever, a fundamental gap in the knowledge between those who build freight demand models and those who make freight- related decisions that is harder to bridge. This knowledge challenge involves many disparate stakeholders among the freight community, such as modelers, state DOT program- ming staff, elected officials, economic development agencies, trucking companies, shippers, receivers, railroads, port ter- minal operators, and local planners. To bridge this knowledge gap, data and modeling initiatives must work to create a col- lective understanding among the many stakeholders of the issues related to freight movement. Steady progress to close the knowledge gap will be a springboard for many other advances. Within this overarching challenge are smaller, more discrete challenges, which include • Developing a thorough understanding of real-world sup- ply chain processes, including the broad economic influ- ences on local freight movement; • Clarifying the role of terminal operations and intermodal load transfers on mode selection in freight transportation and the impact on the surface transportation system; • Understanding the role of the backhaul in service options and pricing; • Quantifying the potential for public–private and public– public data sharing arrangements; • Understanding the potential local impacts of major national and international economic changes; • Identifying environmental justice and community issues associated with freight movement; • Addressing the dynamic nature of freight movement in capacity assessments; • Expanding the understanding by decision makers of sys- tem throughput and its effect on freight system manage- ment; and • Demonstrating to the private sector the tangible benefits of their participation in the planning process. These knowledge challenges are the most important and foundational because they give the model and data improve- ments their purpose. The disconnect between the interests and knowledge of the various stakeholders should be acknowledged as innovations in freight demand modeling and data continue so that these innovations may be directed toward bridging this knowledge gap.