Freight Demand Modeling and Data Improvement (2013) / Chapter Skim
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Pages 21-50
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22 • Lower population growth in the Northeast and Rust Belt states, with higher growth rates occurring in Southern and Western states. • A gradual shift in location advantage for traditional industries.
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23 The various factors described here, coupled with the increased complexity of supply chains and logistics processes, have resulted in an environment in which incorporating freight movement considerations in the transportation planning process has become increasingly difficult at the very time that these considerations are more critical to the ability to forecast long-term transportation trends and plan for future needs. This situation is exacerbated by the inherent political dilemma faced by decision makers involved in almost any freight-related aspect of the transportation system: the widespread disconnect between users of the system (shippers and carriers)
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24 payments made in exchange for them. Economic flow models are built on four economic activities: 1.
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25 and forecasted activity levels of production and consumption within each zone for each commodity. The disaggregated flows are converted into trucks and assigned to a network (Holguín-Veras et al.
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26 • The three-step plus port model has little behavioral relationship to the actual decision-making process in freight movement, lacks multimodal goods movement characteristics, and is more dependent on certain local survey data than the three-step model. • Tour-based microsimulation is computationally complex, is more dependent on some local survey data than the simple three-step model, and lacks multimodal goods movement characteristics.
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27 WeakneSSeS • The QRFM method has little behavioral basis and does not incorporate locally observed characteristics. • The QRFM plus matrix estimation method has little behavioral basis and cannot be readily used for forecasting in areas with rapid development.
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28 • CFS; • FAF; • Transearch data; • Other federal resources; and • Private sector data sets. Local Data Sources Local plans and studies provide information about truck traffic counts and forecasts of truck and passenger car travel.
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29 destinations, values, weights, modes of transport, distance shipped, and ton-miles of commodities shipped. CFS is a shipper-based survey that is conducted every 5 years as part of the Economic Census.
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30 multiple and diverse information sources are placed in a single, consistent format (IHS Global Insight 2010)
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31 in a plethora of industries, from the transaction level to the container level. Each enterprise stores its own data for use in internal applications.
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32 document pricing and cost variables for various legs of multimodal freight transportation processes. • There are very few freight modeling and data university research centers, freight planning consultants, and freight data providers, which limits both the development and use of tools and data and the incentive to innovate.
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33 rect impact on freight demand; in contrast, the economic, demographic, and operational factors more directly affect freight demand. Theoretically, researchers should comprehensively consider all of these factors in freight demand models.
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34 random fluctuations in demand by determining patterns in the data and extrapolating into the future. These techniques remove random fluctuations through the use of parameters that dictate the extent to which more recent observations are weighted in isolating the trend (Cambridge Systematics 1997)
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35 Still, elasticity models face a number of challenges. Planners must be careful when dealing with results from elasticity studies in the 1970s, before deregulation, which may not be appropriate in today's context of a deregulated and highly competitive business environment.
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36 support modeling for freight shipments by mode, commodity, O-D pair, origin, destination, or a combination of these parameters. The simplest aggregate demand models use a total flow approach, which uses regression-based statistical methods to calculate an overall aggregate measure of freight travel demand in an economy.
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37 the well-known multinomial logit model. Due to the shifting nature of freight logistics, this model is best suited for analyzing small windows of time.
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38 LimitationS • Is not comprehensive. Summary and Implications • Freight demand is characterized by a variety of factors, including quantity, geographic scale, time period, source, transportation mode, and commodity; • Freight demand models are emerging as tools to inform transportation policies; however, insufficient and inferiorquality data remain a critical challenge in the development of these tools; • Freight demand is intrinsically interrelated with regional, national, and international economic and demographic characteristics, operational factors and logistics, infrastructure, public policy and regulations, technology; and environmental factors, all of which have varying data sets that are incomplete or contain inaccuracies, or both; • Freight demand model developers use a variety of methods to account for this missing information, such as making assumptions or narrowing the modeling focus when selecting a freight model; and • Current best practices in freight demand model development include trend analysis and time series analysis methods, elasticity methods, logistic network models, aggregate and disaggregate demand models, and input–output models.
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39 maximization technique (Wang and Holguín-Veras 2010) to address a key limitation of traditional four-step modeling as it pertains to local deliveries.
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40 employment trends should be enhanced by incorporating a wide variety of land uses, especially those that are major generators of freight traffic (e.g., manufacturing, warehousing, retail sales, transportation terminals)
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41 Table 3.1. Freight Decision-Making Needs and Gaps Decision-Making Needs Gaps Between Needs and Current Practices Data or Modeling Requirements to Close Gaps Standardized data sources with common definitions • Various data sources collected through different programs result in extensive inconsistencies.
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42 Table 3.1. Freight Decision-Making Needs and Gaps Decision-Making Needs Gaps Between Needs and Current Practices Data or Modeling Requirements to Close Gaps Development of a universal multimodal, network-based model for various geographic scales • The fragmentation of modeling techniques and data means that practitioners typically must develop or improvise data and models for their own applications.
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43 opment of a full network-based freight forecasting model that incorporates all modes of freight transport and accurately reflects the various factors related to the supply of freight infrastructure and services (Strategic Objective 2) and the underlying demand for these services (Strategic Objective 3)
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44 movement planning. Some technologies are familiar to almost anyone with a modern cell phone, and others are emergent from the freight industry itself.
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45 on Earth. Location estimates are achieved through triangulation of a time differential as measured by the time it takes for a signal to leave a satellite and reach a ground-based receiver and the time stamp indicating when the signal was sent.
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46 automated Container tranSPort Automated container transport (ACT) refers to an automated system of controlled vehicles on dedicated lanes between ports and terminals.
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47 touch-screen computer, and the FROG vehicle automatically drives toward the destination, leaving the operator free to perform other tasks. Statistics (pickups, deliveries, movements, events)
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48 routing around congested areas. Wireless drayage updating provides a means to wirelessly and inexpensively exchange information with drivers regarding trip assignments, traffic congestion information, trip status, and location information through a truck-mounted driver interface device (CrossTown Improvement Project 2010)
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49 Innovative Freight Studies Several ongoing freight studies have explored how data from modern technologies can be integrated to advance freight planning. These efforts bridge various technologies to solve specific problems, identify trends, and gauge the willingness of private shippers to embrace technology in their day-to-day business.
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50Table 3.4. Sample Research Initiatives Sample Research Initiativesa Research Dimensions Strategic Objectives Knowledge Models Data 1.
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51 mining and evaluations that can effectively serve the needs of both public and private decision making. Some of the major findings from this section of the research follow: • The majority of freight technologies and innovative programs use GPS or ITS technologies, or both; • A multitude of current freight technologies, programs, and innovative studies are not fully used by freight model developers due to institutional challenges that limit the use of the data; • Institutional challenges that might explain why these technologies are underused include a lack of national direction and modeling standards, the proprietary nature of the data sets, and the cost and time required to collect (or purchase)
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