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7 CHAPTER 2 Current Needs and Practices 2.1 Overview of Outreach Efforts Facility flow information at important freight handling facilities. The approach to this study was driven by a desire to under- stand the needs of decisionmakers and planners and to assess As shown in Figure 2.2, public-sector freight planners, mod- the degree to which existing technical tools meet these needs. elers, and decisionmakers place a premium on information This is a departure from the more traditional method of describing existing freight routing; and also require informa- reviewing existing models and determining the most feasible tion on freight costs and benefits and flows at individual freight improvements. As a result, this approach relied heavily on facilities. To address this broad range of freight analysis needs, information collected from a series of interviews conducted public agencies often require multiple freight analysis tools concurrently with freight modelers, planners, and decision- because a single model application is typically not appropriate makers from state DOTs and MPOs as shown in Figure 2.1. to address all statewide or regional needs. As such, public agen- These in-person and phone interviews were supported cies require a suite of models and analytical tools to apply to with a Web survey and a comprehensive review of freight different problems and to address the questions asked by deci- demand forecasting literature that focused on models and sionmakers. Typical freight analysis tools, including the unique analysis tools that enhance the understanding of freight advantages and disadvantages of each, are described in the fol- demand and public-sector decisionmaking. Taken together, lowing subsections. these outreach efforts allowed the development of an under- standing of the types of tools currently used in practice, the key issues and challenges faced by practitioners, and the types Time Series Models of improvements to freight modeling capabilities that would Based on historical or observed data over a period of time, be of the most interest to practitioners. time series models provide short-, medium-, and long-term forecasts of freight demand and freight activity. Time series 2.2 Public-Sector Freight Analysis based forecasts range in sophistication from simple regression Needs and Available Tools modeling established on past freight activity levels to more complex multivariate autoregressive models. Examples of Public-sector agencies have a wide range of freight analy- time series freight model development and implementation sis needs, including the following: are common among public-sector agencies. Tools commonly are available as trend analysis tools in many commercial com- Costs and benefits of freight programs and projects, puter analysis packages, including the widely used Excel. Performance measures specific to freight movements, With high-frequency data, time series methods produce Mode shifts in response to program and policy changes, good short-term forecasts and require less time and fewer Time-of-day shifts in response to program and policy resources to develop than other modeling approaches. How- changes, ever, building a proper time series model requires a long series Route diversion estimates in response to program and policy of observed data. Also, time series models assume that the changes, underlying economic conditions on which the forecast is Freight forecasts in response to program and policy changes, based remain the same throughout the duration of the time Existing routings of freight vehicles, and series data and continue forward through the forecast. As

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8 Figure 2.1. Interview locations. Freight Costs and Benefits 3.5 Performance Measures 3.3 Estimate of Mode Shifting 3.4 Estimate of Time-of-Day Shifting 2.9 Route Diversion Estimates 3.2 Freight Forecasts 3.4 Existing Routings 3.9 Facility Flow Information 3.5 1 2 3 4 5 Figure 2.2. Public-sector freight analysis needs.

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9 such, time series models are unable to account for changes to based and IO models do not account for many local truck market factors, freight logistics, pricing, or policy that result moves, including trips from warehouses and distribution in freight demand fluctuations. centers, fleet repositioning, empty return trips, and truck drayage moves, as well as service, utility, and construction trucks. Many of these missed truck trips are short trips within Behavioral Models urban areas. Therefore, truck models based exclusively on Behavioral models, which include both choice- and survey- commodity flow data tend to underestimate truck trips in the based demand models, capture how freight shippers perceive urban area. In addition, the commodity flow data generally and select from the many available freight shipment choices. are not available at the Traffic Analysis Zone (TAZ) level, and The models aim to depict the complex interactions between techniques of questionable accuracy must be used to dis- producers, shippers, carriers, and receivers that drive freight aggregate county-level data. Developing and implementing demand. Behavioral interactions, however, are usually com- techniques to support analysis of finer geographic scales, mercially sensitive and difficult to observe. Behavioral data empty vehicle usage, nonfreight truck trips, labor and vehicle can be collected from shipper and carrier surveys; however, productivity, and chaining of freight trips as vehicles or cargo conducting behavior surveys can be prohibitively expensive. may improve the functionality of existing models. The traditional four-step modeling approach has difficulty capturing the factors that influence shipper and carrier behav- Multimodal Network Models ior. Although more common for forecasting passenger travel demand, examples of freight behavioral modeling remain rel- Multimodal network models forecast and optimize mode atively limited. Given the proprietary nature of private-sector and route choice decisions for a specific OD combination decision making, understanding and modeling the logistics based on various transportation cost attributes. They assign decisions that affect freight demand at a regional or statewide commodity flows to the mode (or combination of modes) and level remains a challenge for many public-sector agencies. specific route within a network that minimizes total transport costs, taking into account the location of activities within the network. The models also are capable of estimating mode and Commodity-Based and route sensitivity to various cost factors. The link-node network Input-Output (IO) Models representations of freight supply generated by multimodal net- Commodity-based and IO models estimate current and work models are useful for determining travel times, costs, reli- forecasted freight traffic generation and distribution by linking ability, and overall level of service. economic activity to associated commodity flows. These mod- There are few examples of public-sector model implemen- els use economic data and IO tables to estimate the quantity tation of multimodal network models. Although many public- of each commodity produced and consumed in a geographic sector freight models include a truck component to truck area. The commodity flows are then converted to trucks or freight, few models include fully multimodal capabilities, other freight vehicle trips using average payloads or more elab- because in many cases these models are designed to evaluate orate empty trip models. This modeling approach is well suited private-sector investments and operations, rather than those to representing the economic mechanisms that drive freight for the public sector. movements to and from manufacturers and movements into or out of a region. Similarly, elasticity in the models provides Microsimulation and Agent-Based Models the ability to evaluate the effects of freight policy. Commodity- based and IO models, however, are not capable of capturing Microsimulation models depict the individual movement of empty trips that factor into freight logistics decisions without large numbers of units and their attributes, while agent-based the use of complementary empty trip models. modeling defines potential actors in freight transportation and The data required to develop a commodity-based/IO an allowable set of actions and interactions. The models allow model include existing and forecasted commodity flow data, agencies to perform "what-if" analysis and study the behavior traffic counts, employment data, characteristics of major of a system without building it. The New York Metropolitan freight generators, forecasts of economic activity, and techni- Transportation Council (NYMTC) uses a microsimulation cal coefficients to extrapolate existing production and trade approach to estimate travel patterns for their regional best patterns into the future. Although the data needs are exten- practice model. Although microsimulation and agent-based sive, generally these multimodal freight and economic activ- model development and implementation are common among ity data are readily available. Examples of commodity-based public-sector agencies, they are data intensive and expensive to and IO model development and implementation are com- build. In the absence of sufficient supporting data, modelers mon among public-sector agencies. However, commodity- must make many distribution assumptions to build the mod-