Innovations in Freight Demand Modeling and Data Improvement (2014)

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Design of an Agent-Based Computational Economics Approach to Forecasting Future Freight Flows for the Chicago Region John Gliebe, Colin Smith, Kaveh Shabani, and Maren Outwater RSG, Inc. Kermit Wies Chicago Metropolitan Agency for Planning Presentation Notes: Presented by John Gliebe, RSG, Inc. The model was developed for the Chicago Metropolitan Agency for Planning (CMAP). While original models derived commodity flows from FAF3, the MPO wanted to model changes in macroeconomic conditions such as global supply chain shifts, advances in technology, and near-shoring, for example. The model accounted for the idea that companies have individuals who make decisions based on several drivers—imperfect information, cultural bias, personal affinity to service providers, a limited search effort—and decisions are not optimized. This model takes a bottom-up approach in which individual agents are simulated in a virtual world. This approach matches buyers and sellers and synthesizes firms or establishments outside of the region. It is inspired by the trade network game where buyers and sellers have attributes, some of which lead to buyer preferences for a seller’s cost-service bundle. Abstract Transportation planners in the United States have benefited greatly by the development of the Freight Analysis Framework (FAF) set of data products, which have enabled analysis of not only recent (2007) but also future freight flows by commodity type across zone systems, permitting a useful level of spatial resolution between and within metropolitan regions. The most recent available version, FAF3, provides forecasts of future freight flows forecasts for the year 2040 based on a continuation of current trends, which is one possible depiction of the future. For policy and planning sensitivity analysis, however, it is desirable to vary forecasts to reflect potential changes in macroeconomic conditions (e.g., foreign trade and oil prices), large- scale infrastructure changes (e.g., extra-regional port expansions as well as intra-regional infrastructure investments), technological shifts in logistics and supply chain practices (e.g., near-sourcing, outsourcing), and other assumptions related to regional economic competitiveness. The Chicago Metropolitan Agency for Planning (CMAP) has recently developed the first generation of a regional freight forecasting model that features two levels of resolution: mesoscale resolution, connecting Chicago to the rest of the nation and the world; and microscale resolution, a tour-based simulation model of freight movements within the region. To this already comprehensive set of tools, CMAP is now developing an extension to the mesoscale model to forecast future freight flows, independent of FAF, using a flexible agent-based computational economics approach for modeling the evolution of regional supply chains. This 33

represents the “macroscale” resolution. This paper will provide a brief overview of agent-based computational economics and discuss how it will be applied in the model. Overview of Agent-Based Computational Economics Agent-based computational economics (ACE) (e.g., Tesfatsion 2005) is an emerging, alternative approach to classical economic theory that is often applied to the study of market behavior. ACE methods utilize computer simulations of individual market actors, or agents, who follow relatively simple sets of rules in interacting with other agents. Through careful construction, it is possible for the analyst to specify a set of agents endowed with decision rules, outcome payoffs, and starting conditions, such that the simulated interactions played out over time will result in outcomes that resemble real market behavior. By modifying agent decision rules, payoffs, and starting conditions, it is possible to test a wide variety of potential markets and assumptions. Some of the most commonly studied applications of ACE include the formation of trading networks and production sourcing decisions. At the core of ACE models are decision rules based on game theory. Perhaps the most well known is the prisoner’s dilemma in which two apprehended agents independently decide whether to cooperate or defect in confessing to a crime, with their sentencing varying in severity depending on whether they both confess, both deny guilt, or one confesses while the other denies guilt. This fundamental cooperation-versus-defection paradigm with varying payoffs depending on the outcome is the basis for a wide variety of decision contexts in which agents act on privately held knowledge, while making assumptions about what other actors may or may not do. It forms the basis for many modern microeconomic models of bargaining and cooperation under varying assumptions of information provision and the evolution of market equilibrium conditions, weak stability, or structural change. At a very practical level, game theoretic principles have been used in the global supply chain and logistics profession to develop more efficient practices, particularly for sourcing decisions and supply chain coordination more generally. Indeed, with the rise of Internet-based commerce, electronic procurement systems have emerged as a preferred means for producers of commodities to solicit bids and select suppliers for various input commodities and services. A chief reason for the popularity of “e-procurement” systems is the ability to control market outcomes through auction mechanism design. Mechanism design, a hot area of applied research in economics, finance, and operations studies, amounts to reverse engineering a market process by specifying a set of rules, payoffs, and initial conditions that will result in a preferred market outcome. Researchers in the field of algorithmic game theory have combined mechanism design with computer science to develop sophisticated algorithms to provide computationally efficient implementation solutions that can handle a wide variety of complex market situations in an automated fashion. For example, one desirable outcome of a procurement situation, from the perspective of the buyer, is for suppliers to bid truthfully with respect to their actual costs, that is, to avoid strategic lying. An early solution to this problem, which seems to hold under many contexts, is 34

the second-price sealed-bid auction in which the winning provider/seller was the submitter of the lowest bid (in the case of procurement); however, the price paid to the winner was that of the second-lowest bid. Thus, there is no incentive to bid higher than cost. Auction mechanisms vary in complexity and computational tractability, depending on starting conditions, such as whether there are multiple buyers as well as sellers (so-called double auction), or whether there is a single commodity up for bid or a multi-commodity bundle. CMAP Freight Model Design In the CMAP freight modeling system, firms are represented as commodity-producing agents, defined by firm size and by the 43 categories used by the Standard Classification of Transported Goods (SCTG) and FAF3. Firms are simulated based on data from County Business Patterns and Bureau of Economic Analysis’s (BEA) Input-Output (IO) Accounts and are located either in traffic analysis zones within the Chicago region, or in FAF zones representing other regions of the United States and foreign countries. The design for the future freight forecasting element utilizes the relationships found in the IO data as the foundation for supply chain network formation. For each output commodity produced by a firm, the IO use tables provide a recipe for the producer value of input commodities. To forecast future freight flows, the modeling system will allocate total commodity production to producer agents, based on firm size and SCTG code. For each input commodity specified by the IO tables, the producing firm will choose one or more suppliers from a pool of agents in the simulation who produce that commodity. The supplier pool may include intra-firm establishments, other regional suppliers, and supplier agents from other U.S. states and other countries. Costs of production will vary by each potential supplier’s attributes, taking into account regional differences in both transport and non-transport costs. Transport costs will be derived from multi-modal network path values, considering truck, rail, water, and air modes. An ACE approach will be used to select suppliers for each commodity to be sourced under a variety of commodity markets and firm typology assumptions. Of central importance to the model design is the ability to reflect the degree of vertical integration in firms’ procurement practices as well as propensities to outsource to foreign countries and so-called near-sourcing. Thus, the utility of choosing a firm as the source for a particular commodity will depend on the commodity itself (e.g., bulk versus specialty goods). This will result in varying weights assigned to cost vis-à-vis quality considerations, such as timeliness of delivery and the need to maintain quality control over the product. To remain flexible with respect to ACE methods, consider a couple of flexible mechanism design strategies such as bilateral trading scenarios and double auctions. To facilitate scenario testing, the model design calls for the ability to vary assumptions on • total levels of production; • technical coefficients of production found in the IO tables; • transport and non-transport unit costs, including foreign trade tariffs; and 35

• the ability to code network infrastructure changes, such as port capacity expansions, intermodal terminal capacities, and similar infrastructure changes. Testing Once the model is implemented, the researchers plan to conduct several tests of the model performance for the purposes of fine tuning as well as demonstrating its capabilities. An initial test will be a comparison with FAF3 baseline and future freight flows. In addition, the team plans to conduct a series of sensitivity tests. Preliminarily, these include • Completion of the Chicago Region Environmental and Transportation Efficiency Program (CREATE), a multi-billion dollar program to improve rail infrastructure to ease congestion and minimize impacts on non-rail traffic; • Introduction of a dedicated truck network in Chicago; • Introduction of a large intermodal facility in Iowa; • Expansion of the Port of Prince Rupert, British Columbia; and • Changes in the volume of international trade with China. Reference Tesfatsion, L. Agent-Based Computational Economics: A Constructive Approach to Economic Theory, 2005. Prepublication chapter in Handbook of Computational Economics, Volume 2: Agent-Based Computational Economics (L. Tesfatsion and K. Judd, eds.), Handbooks in Economics Series, North-Holland. 36