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Innovations in Freight Demand Modeling and Data Improvement (2014)

Chapter:Florida Multimodal Statewide Freight Model: A Model Incorporating Supply-Chain Methods and Providing Linkages to Regional Tour-Based Truck Models

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Suggested Citation:"Florida Multimodal Statewide Freight Model: A Model Incorporating Supply-Chain Methods and Providing Linkages to Regional Tour-Based Truck Models." National Academies of Sciences, Engineering, and Medicine. 2014. Innovations in Freight Demand Modeling and Data Improvement. Washington, DC: The National Academies Press. doi: 10.17226/22336.
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Suggested Citation:"Florida Multimodal Statewide Freight Model: A Model Incorporating Supply-Chain Methods and Providing Linkages to Regional Tour-Based Truck Models." National Academies of Sciences, Engineering, and Medicine. 2014. Innovations in Freight Demand Modeling and Data Improvement. Washington, DC: The National Academies Press. doi: 10.17226/22336.
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Suggested Citation:"Florida Multimodal Statewide Freight Model: A Model Incorporating Supply-Chain Methods and Providing Linkages to Regional Tour-Based Truck Models." National Academies of Sciences, Engineering, and Medicine. 2014. Innovations in Freight Demand Modeling and Data Improvement. Washington, DC: The National Academies Press. doi: 10.17226/22336.
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Suggested Citation:"Florida Multimodal Statewide Freight Model: A Model Incorporating Supply-Chain Methods and Providing Linkages to Regional Tour-Based Truck Models." National Academies of Sciences, Engineering, and Medicine. 2014. Innovations in Freight Demand Modeling and Data Improvement. Washington, DC: The National Academies Press. doi: 10.17226/22336.
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Suggested Citation:"Florida Multimodal Statewide Freight Model: A Model Incorporating Supply-Chain Methods and Providing Linkages to Regional Tour-Based Truck Models." National Academies of Sciences, Engineering, and Medicine. 2014. Innovations in Freight Demand Modeling and Data Improvement. Washington, DC: The National Academies Press. doi: 10.17226/22336.
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Florida Multimodal Statewide Freight Model: A Model Incorporating Supply-Chain Methods and Providing Linkages to Regional Tour- Based Truck Models Colin Smith, Kaveh Shabani, and Maren Outwater RSG, Inc. Vidya Mysore FHWA Resource Center S. Frank Tabatabee Florida Department of Transportation Presentation Notes: Presented by Colin Smith, RSG, Inc. The purpose of the model is to enhance modeling capacities. This led to replacing the state’s freight model with a national supply-chain model, while the passenger model was left untouched. The model looks at the way freight moves to individual locations and assigns mode and transfer locations. Additionally, it creates a set of agents that represent firms, similar to population synthesis used in activity-based models. The commodity data are based on FAF3, 2007 benchmark input-output account, to determine type and percentage of commodities used by each industry. The model visualizes trading partners and shows characteristics of commodity and type. The statewide model is designed to be integrated with the regional model where both can run simultaneously. Abstract In recent years, freight forecasting has been identified as a way to understand the patterns of interstate and international trade, economic growth, and the impacts created by the use of the transportation system for the movement of freight. These impacts can include congestion and delay, potential exposure to hazardous materials and other safety concerns, and economic impacts, as well as energy use and environmental consequences. The fact that more and more freight today is moved by heavy trucks on the interstate highway system has become an area of particular concern to planners. Despite recent advances in freight forecasting, the current methods are not adequate to address the increasingly complex issues related to freight demand. Current models are mostly based on methods that were developed for personal passenger travel. Freight is obviously different from personal vehicle travel and requires a different technical approach. Given the transition that is currently underway to implement disaggregate modeling techniques, it is only logical to apply disaggregate techniques for modeling the movement of freight as well. Tour-based and economic methods have become the state of the art in household travel modeling. This new approach offers a myriad of benefits that include the ability to model various aspects of choice behavior explicitly. These factors are relevant in personal travel but are also 37

important in freight modeling as well. In research funded by the Federal Highway Administration (FHWA), the authors have implemented a freight demand forecasting framework in the Chicago region, based on existing research on tour-based and logistics supply chain models for commercial movements that demonstrates the potential of these new methods as a basis for new freight demand forecasting models. The authors have now transferred this freight demand forecasting model to Florida in a project funded by the Florida Department of Transportation. In this work, the researchers have implemented the national level supply-chain model that micro-simulates shipments of commodities between businesses and produces truck, rail, air freight, and water-borne freight volumes. The model is called the Florida Multimodal Statewide Freight Model. The model is designed to link with the regional portion of the freight demand forecasting framework, which is a regional tour-based truck model that micro-simulates the pickup and delivery of each shipment in a metropolitan region. The Florida Multimodal Statewide Freight Model is described in this paper, with particular emphasis on the implementation of the national supply-chain model to support statewide freight modeling in Florida. An overview of the regional tour-based truck model, which is planned to be implemented in one or more metropolitan areas in Florida and was implemented in Chicago, is also included. Description of the Florida Multimodal Statewide Freight Model The Florida Multimodal Statewide Freight Model is comprised of several steps that simulate the transport of freight between each supplier and buyer business in the United States. This modeling system includes selection of business locations, trading relationships between businesses, and the resulting commodity flows, distribution channel, shipment size, mode, and path choices for each shipment made annually. Firm Synthesis The initial element of the model synthesizes all firms in the United States and a sample of international firms. The geographic detail within Florida, Alabama, and Georgia is traffic analysis zones, while outside those three states the geographic detail is defined by Freight Analysis Framework zones. This model synthesizes firms by industry category and by size category to capture the primary drivers of commercial vehicle travel. Supplier Selection and Goods Demand The next element of the model predicts the annual demand in tonnage for shipments of each commodity type between each firm in the synthetic population. The demand represents the goods produced by each firm and the goods consumed by each firm. The model is applied in two steps. In the first step, buyers who have a demand for goods are paired with suppliers who sell those goods using a probabilistic model. The connections between industry types for each commodity are based on input-output tables. Once the buyer-supplier relationships are established, the 38

amount of commodity shipped on an annual basis between each pair of firms is apportioned based on the number of employees at the buyer and their industry, so that observed commodity flows are matched. Distribution Channels Using a multinomial logit model, each shipment between each buyer-supplier pair is assigned a probability of choosing a specific distribution channel to represent the supply chain it follows from the supplier to the consumer. The model predicts the level of complexity of the supply chain, for example whether it is shipped directly, or whether it passes through one or more warehouses, intermodal centers, distribution centers, or consolidation centers. This is a simple representation of supply chains, limited by the available data to estimate distribution channels by industry. Shipment Size Shipment size is estimated using a discrete-choice model based on a variety of firm, commodity, and travel characteristics. It is as this point in the model that the units of analysis change from annual commodity flows between pairs of firms to discrete shipments that are individually accounted for and delivered from the supplier to the buyer. Modes and Transfers There are four primary modes (road, rail, air, and water) that are modeled. Detailed networks of road and rail for the United States are used, with simpler functions of distance and the value of goods being transported to represent the air and water modes. The modes and transfer locations on the shipment paths are determined based on the travel time, cost, characteristics of the shipment (perishable, expedited, containerized), and characteristics of the distribution channel (whether the shipment is routed via a warehouse, consolidation or distribution center, and whether the shipment includes an intermodal transfer, e.g. truck-rail-truck). Daily Shipments and Warehouse Selection Once the modes and intermodal transfers have been assigned, the shipment list is converted from all annual shipments to a daily sample to represent the day being modeled. This component of the model can be adjusted to allow for seasonal variations in commodity flows. This component of the model also assigns shipments to specific warehouse, distribution, and consolidation centers if the shipment passes through one of those locations. The model also incorporates a multimodal transportation network that provides supply- side information to the model including costs for different paths by different modes (or combinations of modes), and to which freight vehicle flows are assigned. While the model is focused on Florida, it encompasses freight flows between Florida and the rest of the world. 39

Overview of the Regional Tour-Based Truck Model The Florida Multimodal Statewide Freight Model is designed to be integrated with a regional truck-touring model, which is a sequence of models that takes shipments from their final transfer point to their final delivery point. The final transfer point is the last point at which the shipment is handled before delivery, i.e. a warehouse, distribution center, or consolidation center for shipments with a more complex supply chain or the supplier for a direct shipment. It does the same in reverse for shipments at the pickup end, where shipments are taken from the supplier and taken as far the first transfer point. For shipments that include transfers, the tour-based truck model accounts for the arrangement of delivery and pickup activity of shipments into truck tours. The model produces trip lists for all of the freight delivery trucks in the region that can be assigned to a transportation network. The truck-touring model predicts the elements of the pickup and delivery system within a region through several modeling components: Vehicle and Tour Pattern Choice This multinomial logit model predicts the joint choice of whether a shipment will be delivered on a direct tour from transfer to delivery (i.e., where a truck departs the transfer location, delivers the shipment, and returns to the transfer location) or a peddling tour where the truck makes multiple deliveries or pickups, and the size of the vehicle that will make the delivery. Number of Tours Choice This multinomial logit model predicts the complexity of the peddling tour in which a shipment is contained: for example, a truck might return to the transfer point after one large loop or might break the delivery schedule into two, three, or more tours. Number of Stops This model uses hierarchical clustering to divide the shipments into spatially collocated groups that can be reasonably delivered by the same truck. Stop Sequence This model uses a greedy algorithm to sequence the stops in a reasonably efficient sequence but not necessarily a shortest path (our research shows that touring trucks only sometimes deliver in a sequence that is efficient in shortest path terms). Stop Duration This multinomial logit model predicts the amount of time taken at each stop based on the size and commodity of the shipment. 40

Delivery Time of Day This multinomial logit model predicts the departure time of the truck at the beginning of the tours. Based on this, the travel time of each trip, and the stop duration of each delivery, all of the trips on the tour can be associated with a time period for assignment purposes. Model Implementation The Florida Multimodal Statewide Freight Model is currently being calibrated and validated to a series of modal data sources in the state. Commodity flows by mode will be validated against Transearch data within the state, truck origin-destination patterns will be validated against ATRI data, and modal volumes will be validated to truck counts, Waybill data, Piers data, and T-100 data for highway, rail, water and air modes, respectively. 41

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TRB’s second Strategic Highway Research Program (SHRP 2) Report: Innovations in Freight Demand Modeling and Data Improvement provides detail to the events of "The TRB Second Symposium on Innovations in Freight Demand Modeling and Data," which took place October 21-22, 2013. The symposium explored the progress of innovative freight modeling approaches as recommended by the Freight Demand Modeling and Data Improvement Strategic Plan.

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