National Long-Distance Passenger Travel Model Sample Clauses

National Long-Distance Passenger Travel Model. For mid/long-term enhancement of FLSWM, transitioning of the model structure from trip-based to activity-based is recommended for short distance passenger travel. With the transition, FLSWM’s existing trip-based visitor and LDB models should also be replaced with activity-based models built upon the same Florida synthetic population used to model short-distance passenger travel. The national long-distance passenger travel demand model (Xxxxxxxx and Xxxxxxx, 2018) developed for the Federal Highway Administration (FHWA) is an activity-based model that can be integrated with FLSWM to model long distance visitor and business trip. The model, known as rJourney, is a microsimulation of long-distance passenger travel activities including both visiting and business purposes that occur in a single year for all households in the United States (i.e., 117 million households and 309 million people based on the 2010 Census). The simulated long-distance tours (i.e., each tour is a series of long-distance stops serving various activity needs) include work-related (i.e., commute and business) and nonwork travel (i.e., visiting, personal business, and leisure). The definition of a long-distance tour used in rJourney is at least 50 miles, which is derived from the definition used by the National Household Travel Survey long-distance add-on (BTS, 2020a). The simulated long-distance tours in rJourney are distinguished by purposes (i.e., commute, business, visiting, personal business, and leisure). Thus, rJourney can cover the two types of long-distance trips (i.e., long-distance business and visitor trips) modeled by FLSWM. rJourney is implemented as an application for desktop computers running the Microsoft Windows operating system with at least 4 GB of RAM and 10 GB of free disk space (Outwater, Bradley, Xxxx, and Oak, 2018). rJourney was written and compiled into a desktop executable application with the software Delphi (Pascal), which has the advantage of fast run times (Outwater, Xxxxxxx, Xxxx, and Oak, 2018). It is expected that integration of rJourney with FLSWM for continuous model run will require one-time alteration and recompilation of rJourney source code. Figure 28 shows the structure of the long-distance passenger travel demand model framework developed for FHWA. (Source: Xxxxxxxx and Xxxxxxx, 2018) Figure 28 National Long-Distance Passenger Travel Demand Modeling System Table 24 National Long-Distance Passenger Travel Model Data Sources (Source: Xxxxxxxx and Xxx...
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