Trip Generation. The trip generation step of trip-based models has two components: trip production and trip attraction (Ortúzar and Willumsen, 2011). For trip production, majority of states adopted the cross-classification method, which is considered the recommended practice for trip production modeling with the trip-based approach (VDOT, 2014). A cross-classification model estimates the number of trip production by multiplying the numbers of households in a specific cross- classification (e.g., four-person household with one car) in a TAZ with corresponding trip production rate for that class. Most states derived household trip production rates for a two- variable cross-classification system of household size and auto-ownership from the NHTS (NASEM. 2017). Arkansas statewide model adopts a three-variable classification system for the TAZs, including area types (defined as a function of population and employment density), household size, and income groups (NASEM, 2017). The additional area type variable introduces additional information about land use into trip production models (VDOT, 2014). By defining TAZs with the area type class variable, trip production can be more precisely modeled than the conventional two-class system. Trip attraction models on the other hand are typically estimated with regression models with land use characteristics of the TAZs as independent variables (Ortúzar and Willumsen, 2011). Attraction models are usually linear regression equations where the independent variables are employment by types (e.g., retail, service, or industrial) and the number of households or population.
Trip Generation. This section of the analysis shall include a table showing trip generation rates that include:
Trip Generation. The trip generation process of FLSWM determines trip productions (i.e., the number of trips that originate from each TAZ) for the TAZs. Eight passenger trip purposes are used in version 7 of FLSWM, including Home-Based Work (HBW), Home-Based Shopping (HBSH), Home-Based Other (HBO), Home-Based Social Recreation (HBSR), Non-Home Based (NHB), Truck-Taxi (TT), Long Distance Business (LDB) and Short Distance External-Internal or Internal-External (SDEI). Trip attractions (i.e., number of trips ending in each TAZ) for the five primary passenger trip purposes (i.e., HBW, HOSH, HBO, HBSR, and NHB), typically required for trip distribution by gravity models, are no longer modeled with version 7, because a destination choice model replaced the gravity model that was used for version 6 of FLSWM. The TT, SDEI, and LDB trip purposes still use the gravity model approach for trip distribution. The trip production for the four home-based trip purpose follows the cross-classification method, by which trip rates per dwelling unit vary by categories of number of autos per dwelling unit, number of persons per dwelling unit, and dwelling unit type (i.e., single family, multi-family and hotel-motel units). Table 3, Table 4, and Table 5 show home-based trip production rates for single- family, multi-family, and hotel/motel. Table 3 Home-Based Trip Production Rates for Single-Family Dwelling Unit Source: FDOT (2020a) Table 4 Home-Based Trip Production Rates for Multi-family Dwelling Unit Source: FDOT (2020a) Table 5 Home-Based Trip Production Rates for Hotel/Motel Source: FDOT (2020a) For each TAZ in the model, specific weights are applied to estimate the numbers of households by household sizes (i.e., persons per household, from 1 to 5+) and auto ownership cross- classifications (i.e., 0 car, 1 car, 2+ cars). The estimated numbers are then multiplied with the corresponding trip generation rates in Table 3, Table 4, and Table 5 to obtain trip productions for the four home-based trip purposes (i.e., HBW, HBSW, HBSR, and HBO). Production for NHB for each TAZ is determined with the equation: NHB Productions = a * Commercial Employment + b * Service Employment + c * Dwelling Units (Eq. 1), and production for TT (Truk Taxi) with: TT production = d * Total Employment + e * Dwelling Units (Eq. 2), where coefficients a, b, c, d, and e vary by counties. Attractions for TT for each TAZ are set to be equal to the productions. Passenger trip productions at the external stations are pr...
Trip Generation. This development is estimated to generate 904 additional vehicle trips per day (0 existing); 71 additional vehicle trips per hour in the PM peak hour (0 existing), based on the traffic impact study.
Trip Generation. The Parties have completed a trip generation study summarized in Exhibit M-1. The Parties agree that the target for maximum vehicle trips into and out of the ASP is 50,000 trips per day. All applications for Major Permits for the Property and the Third-Party Property shall be designed to achieve this 50,000 maximum vehicle trip target, and shall detail professionally reasonable tactics for achieving them. The Parties agree that there is a “hard cap” maximum of 55,000 trips per day into and out of the ASP.
Trip Generation. Sub-consultant shall provide initial guidance for offsite traffic improvements by bracketing the level of development early in the conceptual design process. Sub-consultant shall provide empirical vehicle trip generation and parking demand characteristics for three conceptual site plans based on the proposed amenities. Thresholds that trigger significant off-site development (i.e., new traffic signals, lane widening, etc.) will be identified during the iterative process to allow the Master Plan team to refine the plan such that it becomes self-mitigating in terms of number of trips being generated. The scope includes two iterations of the projected trip generation estimates under this task – one using an initial schedule of preferred on-site uses, and a second after the team has been able to review and refine the plan. The trip generation rates developed in this task will be used in the environmental impact report. For programmed uses, trip generation estimates will be based on forecasts of the proposed schedule and use intensity (i.e., number of participants or site users). Special event trip generation will use data collected from previous events. For passive uses, we will explore a variety of methods to determine the most appropriate trip estimating approach. Trip generation and parking demand forecasts will be prepared for both daily conditions and peak hours. Depending upon the Master Plan alternative, Sub-consultant shall estimate the percent of park attendees that use different modes of travel. For park attendees that drive, Sub- consultant shall use average vehicle occupancy (AVO) similar to the reported AVO in the Nation Household Transportation Survey for recreational trips. In addition, local data will be used to supplement available national standards. Institute of Traffic Engineers trip generation rates will also be considered
Trip Generation. Trip generation represents the amount of traffic that is attracted and produced by a development, and is based upon the specific land uses planned for a given project. Trip generation rates for the Project are shown in Table 1. The trip generation summary illustrating daily and peak hour trip generation estimates for the proposed Project by buildings are shown on Table 2 in passenger car equivalent (PCE) and on Table 3 for actual vehicles. The trip generation rates used for this analysis are based upon information collected by the Institute of Transportation Engineers (ITE) as provided in their Trip Generation manual, 10th Edition, 2017. For purposes of this analysis, ITE land use code 154 (High-Cube Warehouse/Distribution Center) has been used to derive site specific trip generation estimates. As noted on Table 1, refinements to the raw trip generation estimates have been made to provide a more detailed breakdown of trips by vehicle mix. Total vehicle mix percentages were also obtained from the ITE Trip Generation manual in conjunction with the South Coast Air Quality Management District’s (SCAQMD) recommended truck mix, by axle type. Finally, PCE factors were applied to the trip generation rates for heavy trucks (large 2-axles, 3- axles, 4+-axles). PCEs allow the typical “real-world” mix of vehicle types to be represented as a single, standardized unit, such as the passenger car, to be used for the purposes of capacity and level of service analyses. The PCE factors are consistent with the recommended PCE factors in Appendix “B” of the San Bernardino County Congestion Management Program (CMP), 2016 Update. Trip generation rates with PCE factors are also shown on Table 1. As shown on Table 2, the proposed Project is anticipated to generate a net total of 1,938 PCE trip-ends per day with 186 PCE AM peak hour trips and 203 PCE PM peak hour trips. In comparison, as shown on Table 3, the proposed Project is anticipated to generate a net total of 1,368 actual trip-ends per day with 129 actual AM peak hour trips and 153 actual PM peak hour trips.
Trip Generation. In accordance with the City of La Quinta’s Engineering Bulletin #06-13, the project trip generation rates to be used for the traffic impact analysis will be based on the Institute of Transportation Engineers (ITE) Trip Generation manual, 9th Edition (2012). Trip generation estimates for the Project have been determined by utilizing the published rates for the peak hour of the generator rather than for the peak hour of adjacent street traffic, where possible. Average trip generation rates have been utilized for the shopping center component as opposed to application of the regression equations for the shopping center portion of the Project due to its size and nature. As the shopping center portion of the Project is much smaller than the average shopping centers surveyed in Trip Generation and represents a small portion of the existing Washington Park Shopping Center as opposed to a standalone land use, utilization of the regression equation based trip generation rates, as advised by Engineering Bulletin #06-13, would significantly overstate the trip generation for the shopping center component of the Project. Trip generation rates are presented on Table 1. As shown on Table 1, the proposed Project is anticipated to generate a net total of approximately 4,842 trip-ends per day on a typical weekday with 151 vehicles per hour (VPH) during the weekday AM peak hour, 707 VPH during the weekday PM peak hour and 758 VPH during the Saturday mid-day peak hour.
Trip Generation. The household variables (household size and income) used in the current model provide sufficient detail to capture the trip-making differences among different types of households. Similarly, the six employment categories in the model provide ample sensitivity to the model trip generation. Bivariate household variables, household size, and household income used in the existing model were based on the 2010 Census. LSA will explore the latest available ACS data and will consider update of the bivariate variables, if the data are deemed sufficient to do so. Regional bivariate distributions by household size and income will also be considered for update using latest available Public Use Microdata Sample (PUMS) data. The 2017 National Household Travel Survey (NHTS) was recently released and LSA has experience in the analysis of previous NHTS datasets and household travel surveys for multiple travel model updates. Trip rates from the existing model will be compared with the latest datasets available and any necessary changes will be included in the update after the MPO’s review. External station traffic counts will be updated to the new base year. No changes are proposed to methodology for external trips other than a simple update of the counts. IE trip splits and EE trip interchanges between external stations are not proposed for any modification.
Trip Generation. As suggested in the RFP, LSA will continue use of existing cross-classification/bivariate trip production methodology for households. Currently the model uses household variables such as household income and household size for trip generation. Similarly, the current model disaggregates employment into six employment categories that provide sufficient sensitivity for generation of the model’s attractions. LSA will review trip production and attraction rates and update them for model calibration and validation. Potential adjustments to trip rates will occur during the entire calibration and validation process. Missoula is home to the University of Montana (UM), which is modeled as a special generator in the model. The UM campus is separated into four traffic analysis zones. The most recent enrollment and employment data for UM will be requested from UM. Total enrollment will be divided into on-campus and off-campus enrollment based on data from the previous model. Special generator values will be updated based on changes in student enrollment and employment. LSA will review the latest available PUMS data and ACS data sets to identify any updates to the regional household distributions by income and size. LSA will obtain the most recent traffic counts from MDT at the model external stations. The traffic counts will be disaggregated into internal-external/external-internal (IE/EI) and external-external trips. LSA will use the same approach utilized in the previous model updates. LSA is recommending the purchase of big data, described in detail in Task 3.2, which can be used to update the IEEI/EE splits based on the most recent external trip patterns.
