Trip Distribution. The most common trip distribution model used in statewide modeling are the gravity models (NASEM, 2017), which are based on the mathematical function form of the law of gravity in that travel activities between two TAZs are assumed to be positively proportional to the product of trip production at one TAZ and trip attraction at the other, weighted inversely by a function of travel time between the two TAZs (Ortúzar and Willumsen, 2011). The strength of the gravity models is that they are easy to implement (i.e., only three variables needed in the simplest form) and easy to calibrate. Calibration of a gravity model involves adjusting parameters of the gravity function until the observed average trip length distribution is matched by the model (Xxxxxxx and Xxxxxxxxx, 2011). However, gravity models cannot effectively model long-distance trips (e.g., trips over 50 miles) because the inverse weight of travel time increases drastically as distance increases such that the curve of the gravity function flattens out to values close to zero after a certain distance (Ortúzar and Willumsen, 2011). This is one of the reasons that statewide models usually incorporate separate long-distance passenger travel models (NASEM, 2017). The logit-based destination choice model is the other commonly used model for trip distribution (NASEM, 2017). A logit destination model hypothesizes that the probability of choosing one particular TAZ depends on the ratio of the TAZ’s utility, which is expressed as a function of land use characteristics of the TAZ (e.g., population, employment, and distance to the TAZ), to the sum of the utilities of all TAZs (Ortúzar and Willumsen, 2011). Some consider logit destination distribution models the best practice for trip distribution (VDOT, 2014), because the logit model form enables consideration of multiple factors that can affect destination choice, while the gravity models theoretically only allow for three variables (i.e., without considering composite variables) in the model form. In addition, the logit models do not have the limitation as gravity models in modeling long-distance trips. In practice, gravity models are far more commonly used for statewide modeling (i.e., 22 gravity models versus 11 logit models according NCHRP synthesis 514). Generally, use of the gravity model for trip distribution is considered acceptable practice in all regions. In small regions, the gravity model for trip distribution also is considered recommended practice...
Trip Distribution. As indicated in the RFP, the previous model development and update efforts relied heavily on the CTPP, which includes a Journey to Work dataset that was useful in calibration of trip distribution for work trips. CTTP data along with other household surveys from similar regions to Missoula, and big data (Streetlight) were used in the previous model calibration for non-work trip purposes using a pivot-point analysis. LSA will review available data sources such as the latest Census data, CTPP data, and 2017 National Household Travel Surveys, etc., for calibration of trip distribution. LSA will validate the trip distribution for each of the trip purposes using the standards found in the following document: Travel Model Validation and Reasonability Checking Manual, Second Edition (Sept. 2010), Federal Highway Administration (FHWA) Travel Model Improvement Program. The following validation and reasonableness checks will be performed: ▪ Implied speeds for each zone-interchange (consider high and low values, frequency distribution); ▪ Compare average trip time and/or length by trip purpose; ▪ Compare average trip time and/or length by production/attraction and by area type; ▪ Compare trip time and/or length distribution plots; ▪ Compare normalized friction factors by trip purpose; ▪ Compare percent of intrazonal trips by trip purpose; and ▪ Compare district-to-district trip interchanges to any observed data. Optional Data Purchase LSA strongly recommends obtaining new global positioning system (GPS)-based Origin-Destination (O-D) data for calibration of trip distribution especially to understand post-pandemic travel patterns. Most of the publicly available data sources are pre-pandemic, which raises questions on the validity of using pre- pandemic data sources for post-pandemic model validation. LSA understands that this is an expensive endeavor, but lack of post-pandemic travel data limits the number of data sources that can be used in the travel model calibration and validation. Proposed big data will provide detailed trip information specific to the Missoula region for pre-pandemic and post-pandemic conditions, which will help in a better calibration and validation of a travel model and capture appropriate changes in trip making characteristics of the region for post-pandemic conditions. For example, one of the long-term impacts of the pandemic was acceleration of work from home and online shopping. These trends of decreased work and shopping trips will not be ...
Trip Distribution. LSA will update the gravity-based trip distribution model using information from the NHTS add-on and the Census Transportation Planning Package (CTPP). New friction factors will be calibrated for each of the trip purposes included in the updated model using the NHTS add-on. Calibrated friction factors will be validated against available supplementary data, such as CTPP data. Friction factor calibration and validation will be based on trip length and/or time distributions, average trip lengths, and average travel times. LSA will document the resulting trip distribution model, friction factors, and validation in a technical memorandum. Trip distribution parameters may be adjusted during the traffic assignment calibration/validation phase. LSA will implement a speed feedback loop to replace estimated congested speeds that would otherwise be used to perform trip distribution. LSA will modify the model stream to iteratively run trip distribution and assignment until convergence criteria has been met. Feedback will be implemented using a direct feedback method, the Method of Successive Averages, or a Constant-Weight method. The best method will be chosen based on a comparison of convergence characteristics of the three methods.
Trip Distribution a. Extra trip distributions shall be equalized as possible by total hours paid. Extra trips will be posted at least three (3) days in advance, when possible, in the bus garage on the bulletin board. An up-to-date listing showing the hours paid, hours refused (in red), and total hours will be maintained and posted after each trip on the appropriate bulletin board. The total hours will be the deciding factor as to who will take the next trip out, starting with the lowest to the highest hours. Weekend trips will be assigned by seniority rotating through the list. The trips will be assigned once per week during the meeting that takes place the last scheduled workday of the week, unless an alternate date is agreed upon. At the beginning of each school year, seniority will be used for the first trip out and so on down the list until all drivers have total hours. Thereafter, total hours shall be the deciding factor except when two or more drivers have the same total hours, then seniority shall have preference. Any driver added to an already active list will be given three (3) hours above the highest number of hours on the list. Drivers who choose to drive summer trips will not have such trips counted for the school year's total hours. Distribution of summer extra trips shall be rotated by seniority. The substitution for summer special education buses/vans will be filled in accordance with section 13.3.I.
Trip Distribution. Trip distribution using a gravity model or other recommended framework that takes into account various impedances. The RFP indicates a desire to include sensitivity to other, non-auto, modes of travel within the trip distribution process. Subject to constraints an enhanced gravity model or destination choice model will be implemented. A fairly robust gravity model would include segmentation by income and time period as well as different friction factors for internal and external trips. If a Home-base University trip purpose is implemented, a separate distance-based university trip distribution model can be calibrated to student household information. The logsums from the mode choice model contain information about the available modes and could be incorporated into a gravity model, but this approach will need to be considered against the additional level of effort versus the gains in sensitivity of the distribution process. Destination Choice models provide enhancements over traditional gravity models. Regions of varying size have moved from gravity-based to destination choice models. While region size is one part of the decision on which trip distribution approach is best, it is even more important to consider the needs of a region related to what different model structures can offer. For example, a destination choice model could help NIRPC better represent some very relevant transportation options. Some of the key aspects of destination choice are listed below. • Transit Sensitivity—Gravity models typically only consider auto travel time and do not account for transit in computing the impedance measure used in trip distribution. While there have been models that use multimodal impedances for the gravity model, there have often been issues with consistency when combined with mode choice models. Adoption of destination choice is generally more consistent with mode choice models and can reflect the propensity of trip makers to make trips within transit corridors, which is especially important for the evaluation of new transit options that are added to the transportation network. • HOV and Managed Lane Sensitivity—Similar to the impact of transit on travel impedance, destination choice models can more fully reflect the addition of HOV and managed lanes to the network. Managed lanes can make trips using less congested managed lanes more attractive, especially to higher income trip makers. • Additional Market Segmentation—Destination choice can more fully consi...
Trip Distribution. The current version of FLSWM utilizes a combination of a gravity model and a destination choice model for trip distribution. For the five main passenger internal trip purposes (HBW, HBSH, HBO, HBSR and NHB), the destination choice model is used. The TT, SDEI, and LDB trip purposes use the gravity model approach. Gravity Model The friction factors Fij used in version 7 are the same as those of version 6. The gravity model for trip distribution of TT, SDEI, and LDB is described by the following equation: where: Xxx = Trips (volume) originating at TAZ i and destined to TAZ j Oi = Total trips originating at i Dj = Total trips destined at j Fij = Friction factor for trip interchange ij i = Origin analysis area number, i = 1, 2, 3, … n j = Destination analysis area number, j = 1, 2, 3, … n n = Number of analysis areas
Trip Distribution. The Project trip distribution and assignment process represents the directional orientation of traffic to and from the Project site. The trip distribution pattern of passenger cars is heavily influenced by the geographical location of the site, the location of surrounding uses, and the proximity to the regional freeway system. The trip distribution pattern for truck traffic is also influenced by the local truck routes approved by the County of Riverside, the City of Perris, and the California Department of Transportation (Caltrans). Given these differences, separate trip distributions were generated for both passenger cars and truck trips. The Project passenger car trip distribution pattern is graphically depicted on Exhibit 3. The Project truck trip distribution pattern is graphically depicted on Exhibit 4.
Trip Distribution. As directed by City staff, the proposed Project trip distribution patterns will be based on the CVAG counts taken in February/March of 2013 and the peak hour turning movement count data at the intersection of La Quinta Center Drive/Caleo Bay and Avenue 47 that will be taken for this work effort. It is requested that the City provide the CVAG count data conducted in February/March of 2013.
Trip Distribution. The current version of FLSWM utilizes a combination of a gravity model and a destination choice model for trip distribution. For the five main passenger internal trip purposes (HBW, HBSH, HBO, HBSR and NHB), the destination choice model is used. The TT, SDEI, and LDB trip purposes use the gravity model approach. Gravity Model The friction factors Fij used in version 7 are the same as those of version 6. The gravity model for trip distribution of TT, SDEI, and LDB is described by the following equation:
