Conference Proceedings on the Web 22: Transforming the Marine Transportation System Through Multimodal Freight Analytics (2018)

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64 APPENDIX A Interactive Demonstration Titles and Abstracts Freight and Service Activity Generation Software ................................................................................... 65 Shama Campbell, presenter Ports of the Future: Deploying Emulation and Real-Time Simulation for Identifying Technologies for Improved Port Supply Chain Performance ............................................................. 67 Lawrence Henesey

65 Freight and Service Activity Generation Software Shama Campbell, Rensselaer Polytechnic Institute, presenter José Holguín-Veras, Lokesh Kumar Kalahasthi, Rensselaer Polytechnic Institute Carlos A. González-Calderón, National University of Colombia at Medellin FSA is essential to the functioning of an economy as well as the livability of an area. The metrics that are used to represent FSA are freight generation (FG), which is the amount of cargo generated by the commercial establishment; freight trip generation (FTG), which is the vehicular trips that are generated as a result of the FG; and service trip generation (STG), which is the service trips generated by a commercial establishments. All these metrics have freight attraction (FA) (cargo or trips coming to the business) and freight production (FP) (cargo or trips leaving the business) components to represent the flows. The estimations of these metrics offer transportation planners and other decision makers increased understanding of FSA within an area and its impacts on the network. Also, a better understanding of the variables driving freight demand enables more accurate demand forecasts and better quantification of the traffic impacts. Table 7 shows some of the typical applications of the FG, FTG, and STG models. The research published in NCFRP Report 37: Using Commodity Flow Survey Microdata and Other Establishment Data to Estimate the Generation of Freight, Freight Trips, and Service Trips: Guidebook13 estimated models for FP, FA, FTP and STA models with the use of establishment survey data; in addition, the research estimated freight FP models with the use of census microdata (2007 CFS microdata and the LBD). With regression analysis, the models were estimated as a function of establishment characteristics; that is, establishment size indicated by employment and the economic activity performed indicated by industry type as classified by the NAICS at two- and three-digit levels. TABLE 7. Typical Applications of FSA Models. Description Freight Trip Generation Service Trip Generation Freight Generation FTA FTP FTG STA STP STG FA FP FG Traffic impact analysis ✔ ✔ ✔ ✔ ✔ ✔ Number of parking spaces needed by freight vehiclesa ✔ ✔ ✔ Number of parking spaces needed by service vehiclesa ✔ ✔ ✔ Number of parking spaces needed by commercial vehiclesa ✔ ✔ ✔ ✔ ✔ ✔ Analysis of trends in FAa ✔ ✔ ✔ Analysis of trends in FSAa ✔ ✔ ✔ ✔ ✔ ✔ Estimation of FTGa ✔ ✔ ✔ Estimation of STGa ✔ ✔ ✔ Estimation of FGa ✔ ✔ ✔ NOTE: FTA = freight trip attraction; FTP = freight trip production; STA = service trip attraction; STP = service trip production. a These analyses can be conducted at any level of geography. 13 TRB, Washington, D.C., 2012.

66 SUMMARY OF DEMONSTRATION CONTENT These models serve as a tool for planners in estimating freight flow patterns produced from specified origins, as the models are able to reflect the geographic patterns of freight activity. To assist in producing FSA estimates, the research team developed the Freight and Service Activity Generation Software using the models documented in NCFRP Research Report 37. The interactive poster presentation focused on demonstrating this tool. The software, available at https://coe-sufs.org/wordpress/software/, is a web application that is able to generate zip code and business-level estimates of FSA for two-digit and three-digit NAICS codes. The user is able to select the level of aggregation that is applicable to the study (e.g., establishment, zip code, city, state, county), the industry sectors, the model type, and, in the case of the FP models developed from the census microdata, the mode grouping. The selections from the users accompanied with the business level data are input in the model to provide FP, FA, FTA, FTP, and STA estimates. Figure 5 shows the schematic of how the software works. FIGURE 5. Schematic of Freight and Service Activity Generation Software. Options • Aggregation: state, city, county, zip code, and establishment • Industry sector: 2- or 3-digit NAICS • Model: linear, nonlinear, or CFS models • Mode: all modes and road mode Inputs • Zip code business pattern or • Business-level data • Coefficient input file (Admin) FSA Generation Software Outputs • Estimates of FG, FTG, STA • Freight production estimates using CFS models

67 Ports of the Future: Deploying Emulation and Real-Time Simulation for Identifying Technologies for Improved Port Supply Chain Performance Lawrence Henesey, Blekinge Institute of Technology The CHESSCON tool developed by ISL Applications in Bremerhaven, Germany, is used in scientific studies of container terminal operations. The simulation and emulation facilities provide novel means for researchers and academics to study and analyze methods for improving productivity in container terminals. The container terminal domain is engineered into different modules, each representing a particular operational function of the container terminal. The ability to simulate with real data that are fed into the simulation models adds significant weight to the results. In addition, the ease in developing the virtual container terminal for simulation is an advantage over developing in-house container simulation software. The CHESSCON tool emulates a virtualized container terminal. This concept of virtualization implies that a real, physical container terminal is modeled for later analysis of real data from a TOS, studied under numerous what-if scenarios. The ability to analyze real container terminal operations with such a tool allows for evaluating new optimization algorithms, planning rules, and physical configurations. The demonstration showed an actual terminal in Northern Europe simulated in the CHESSCON model. Various scenarios of the TOS are studied, and an emulation is shown in a short video. The emulated TOS is NAVIS N4, which is an established TOS used by many large container terminals worldwide and developed by the NAVIS company in Oakland, California. The results of the simulations shown in the demonstration indicate strong positive results, including improved planning, optimized resource planning, and faster ship service at the marine side of the container tribal.