Freight Demand Modeling and Data Improvement (2013)

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18 C h a p t e R 2 The challenge in developing the Freight Demand Modeling and Data Improvement Strategic Plan was to identify a compelling direction for the freight planning community—including meeting the immediate needs of decision makers—while con- tinuing to foster innovation among researchers for making long-term improvements to freight modeling and data. To meet this challenge, the research team focused on defining the gaps in models, data, and information for decision makers and formu- lating future directions to guide the long-term initiatives identi- fied throughout the research process. A robust approach to define needs, innovations, and long- term goals centered on input from practitioners, decision makers, and past practices. This approach involved a review of research conducted on freight modeling and data improve- ment and an analysis of current domestic and international practices within the industry. Practitioners were engaged through an Innovations in Freight Demand Modeling and Data Symposium and competition. Involvement of freight stake- holders fostered discussion for validating research priorities and strategic directions for the freight modeling and data improvement community. The approach elements, their pur- pose, and outcomes are shown in Table 2.1. Defining the Strategic Needs Understanding and defining needs requires asking specific ques- tions. The first and most critical question for defining the depth of this research was, “Whose needs?” Many public and private entities with a stake in the efficient movement of goods are involved in freight modeling, data collection, and decision mak- ing. Public sector groups such as freight planners, policy makers, safety officials, and regulatory officials require freight modeling and data to maximize capacity, increase safety, and target the best use of funding to make the maximum impact on the trans- portation system. Public sector interests function at various geo- graphic levels; to achieve their individual goals and mandates, each group has specific freight data and modeling needs. Private sector interests seek information to maximize effi- ciency throughout the logistics chain, part of which is mov- ing goods on public infrastructure. Within the private sector are companies and service providers with unique character- istics and needs relative to freight demand modeling and data. For example, trucking companies require different freight data than third-party logistics providers, whose needs are different from those of railroads, port terminal operators, suppliers, and manufacturers. The identification of the strategic needs of these freight demand modeling and data stakeholders has been systemati- cally considered in this research. Both the public and private sectors have important data and analytic needs. Each sector also has something to offer for the benefit of all. The key is to identify the data and tools available, note where there are overlapping needs, and address how to fill any remaining gaps that may benefit the freight community as a whole. Public sector and private sector strategic needs were iden- tified through an outreach campaign that included work- shops with private sector representatives and officials from DOTs, MPOs, toll authorities, and county planning agencies. These workshops and outreach sessions took place in Newark, New Jersey; Tacoma, Washington; Tempe, Arizona; New Orleans, Louisiana; Irvine, California; Minneapolis, Minnesota; Columbus, Ohio; and Washington, D.C. Identifying Innovative Research efforts Identifying innovative research is imperative to SHRP 2 C20. Innovations are what led to the development of today’s travel demand models for analyzing passenger movements, air quality, congestion, corridors, and other factors. Identifica- tion of those innovations specifically aimed at freight demand modeling and data improvement will provide the building blocks for the freight analysis tools of tomorrow. As depicted in Figure 2.1, the SHRP 2 C20 Freight Demand Modeling and Research Approach

19 Table 2.1. Research Approach Elements Approach Element Purpose Outcome Technical Expert Task Group •  Articulate the project and industry vision •  Advise project team •  Review interim and final findings •  Overall project oversight and direction Background Research •  Identify domestic and international best practices •  Identify historic freight modeling and data  challenges •  Identify opportunities, innovations, and unique data  sources •  Catalog of current and best freight modeling and  data collection practices •  Determination of potential areas for improvement  and innovation •  Background in defining strategic needs Innovations in Freight Demand  Modeling and Data  Symposium •  Identify domestic and international innovative  practices •  Discuss applicability and improvements to  innovations •  Launch a forum for sharing of freight demand   modeling and data innovations •  Current innovative initiatives •  Brought the data and modeling community  together to foster the best thinking on the subject •  Venue for future sharing of innovative ideas •  Formal structure for rewarding freight modeling  and data innovations Stakeholders Outreach and  Workshops •  Validate the strategic directions •  Discuss a series of key issues •  Review, critique, and validate strategic research   initiatives that will affect freight transportation for  years to come •  Validation and supporting ideas and discussion on  the Strategic Plan •  Validation and discussion on research priorities •  Ideas for continuing innovations to meet decision- making needs Strategic Plan •  Frame the long-term direction for freight modeling  and data improvement •  Foster innovative practices in modeling and data •  Set an agenda for short- and long-term research  initiatives •  Documented strategic needs and innovative  research efforts •  Developed a feasible approach to freight transpor- tation modeling and data improvement •  Identified short- and long-term strategic research  initiatives •  Developed a strategic plan and road map Figure 2.1. Innovations considered in the SHRP 2 C20 Freight Demand Modeling and Data Improvement Strategic Plan.

20 Data Improvement Strategic Plan incorporates these ele- ments as part of the strategic future directions and research initiatives to glean fresh ideas and identify and fill the knowl- edge and data gaps that remain relative to freight analysis and decision-making needs. The innovations shown in Figure 2.1 were identified through various efforts. Research provided a perspective on past innovations, specifically the history of the development of passenger travel demand models and data. Outreach meet- ings and interviews with private sector practitioners revealed several modest and recent innovations that, if combined with existing practices or new innovative practices, should foster the development of better freight planning tools. The pri- mary source of innovative practices was the 2010 Innovations in Freight Demand Modeling and Data Symposium, which was a pilot initiative to assist the freight community in the identification of such innovations. The two-day symposium was organized into six sessions based on six areas of research interest as determined by the topics of the papers that were submitted: 1. Regional freight model development; 2. Alternative techniques for modeling freight transport; 3. The application of econometric and statistical methods; 4. International perspectives on modeling freight; 5. Data collection and visualization techniques for analyzing freight travel patterns; and 6. Microsimulation approaches to freight forecasting. Each session comprised two or three 20-minute presenta- tions, followed by a 15-minute question-and-answer discus- sion involving the entire audience and the presenter. As a participation incentive, the freight modeling presentations competed for a $1,000 prize. The symposium attracted a diverse audience representing academia, public sector practi- tioners, and private industry. Participants examined, evalu- ated, and promoted innovative and promising advances in freight demand modeling, data collection, and freight fore- casting research methods. Local, state-level, regional, domes- tic, and international models were presented. Developing a Feasible approach to Freight transportation Modeling and Data Issues related to freight transportation link closely to other rel- evant issues such as land use planning, economic development (economic growth, employment, funding sources), environ- mental protection, infrastructure planning and development (including both transportation and nontransportation infra- structure), and energy considerations. Moving forward, a key element of the freight planning process will be its ability to link pertinent information and collaborative analyses with these other planning efforts to the maximum extent possible. Feasible implementation of freight travel demand model- ing and data innovations requires additional and ongoing data gathering, technological advances, incentives, funding, collaboration, and coordination. The efforts of the freight community—public and private—must be involved in fram- ing the pertinent questions that need to be answered, and in developing the data required for analysis, the tools needed, and the sources of funding that will enable the development and implementation of versatile freight travel demand models. The research team worked strategically to craft the approach to pursue short-term and long-term freight demand modeling and data improvements. These complex issues were considered by developing pertinent information obtained from workshop discussions, one-on-one discussions, and research publications. The collaboration and coordination necessary to develop new freight planning tools and data were pursued throughout the SHRP 2 C20 outreach effort. The results are incorporated into the research initiatives and future directions described in Chapter 4. establishing a Venue for Supporting Innovation Freight demand modeling is more dynamic and heteroge- neous than passenger demand modeling because of the many complex interactions between international and domestic flows, public and private interests, and logistics behavior. The inadequacy of freight modeling and data for many of the pressing issues facing decision makers and freight planners makes it is important to create a venue in which the freight community can share innovative ideas and discuss ways to apply and improve them. This effort is critical to sustain fur- ther research efforts. Ideally, this venue would involve a col- laboration of public and private interests that share a common goal of improving freight transportation planning efforts and can secure access to financial resources, innovative ideas, and extensive data to support these efforts in the future. The 2010 Innovations in Freight Demand Modeling and Data Symposium served as a pilot for future symposia with the intent of establishing a venue and format for the sharing of innovative freight modeling and data ideas. This collaborative, multisector symposium was intended for a diverse audience of academia, public sector practitioners, and private industry interested in furthering the science and application of freight demand modeling and forecasting. The participants were tasked with examining, evaluating, and promoting innovative and promising advances in freight demand modeling, data collection, and freight forecasting research methods.

21 The 2010 symposium centered on discussing how current freight models fall short and identifying data needs that can bridge the gap between a traditional freight model and a valid real-world tool that can be used with confidence for day-to-day planning and operations. Featured presentations addressed the challenge of finding the next generation of freight demand models. Future symposia should be tailored to the seven strategic objectives described in Chapter 4, provided there is sufficient research interest in those areas expressed in future responses to calls for papers. Potential topics to be discussed in future sym- posia include the accuracy and dynamic requirements of valid freight models and forecasting; data collection, data quality, and data relevance; energy and environmental impacts (includ- ing mode shifts, pollution reduction technology penetration, and fuel prices); creation of local-level dynamic modeling data outputs in response to national events; public and private sec- tor funding impacts on local freight traffic and logistics; and relevant performance measures to determine a model’s useful- ness for investments and public sector funding decisions. Participation in freight symposia will continue to bring freight modeling practices closer to real-world, practical, and relevant freight model generation and outputs, and impor- tantly, further the science.