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Activity Based Model Calibration Coordinated Travel – Regional Activity Based Modeling Platform (CT-RAMP) for Atlanta Regional Commission

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Activity Based Model Calibration Coordinated Travel – Regional Activity Based Modeling Platform (CT-RAMP) for Atlanta Regional Commission NOVEMBER 2015 Activity Based Model Calibration Coordinated Travel – Regional Activity Based Modeling Platform (CT-RAMP) for Atlanta Regional Commission Prepared for: Atlanta Regional Commission 40 Courtland Street, NE Atlanta, Georgia 30303-2538 Prepared by: Parsons Brinckerhoff 400 SW Sixth Avenue, Suite 802 Portland, OR 97204 With: Atkins 1600 RiverEdge Parkway Atlanta, GA, 30328 Introduction Atlanta Regional Commission, the metropolitan planning agency for the 20 county Atlanta region, is actively developing a comprehensive travel demand model to equip itself with a powerful forecasting tool that will be adequate for new socioeconomic environments and meeting emerging planning challenges. The ABM developed for ARC uses the Coordinated Travel – Regional Activity Based Modeling Platform (CT-RAMP) to model the activity travel patterns of all individuals in the region. The basic tenets of activity based model system is the representation of space and time – the CT-RAMP model developed for ARC uses a new 5000+ zone system and a temporal resolution of half-hour. Modeling at this fine spatial-temporal resolution greatly improves the accuracy of activity-travel patterns estimates. Thus, the ARC-ABM is designed to serve as a robust tool for evaluating conventional highway and transit projects as also to test a variety of policies including land use scenarios, highway pricing and HOV analysis. This document describes the ARC-ABM model system calibration. ARC-ABM utilizes econometric models specifically tailored for the region. Most of the models are estimated while a few are asserted (for example, the mode choice models). Calibration of these models involves attuning certain alternative specific model parameters to ensure consistency with the observed data. The data for the model calibration is primarily drawn from the ARC Regional Travel Survey conducted in 2011. There were a total of 26,203 individual tours and 2,199 joint tours derived from the travel survey data from 10,278 households and 25,810 persons. This data was supplemented with data from the 2010 Census and 2011 Transit On-Board Survey. The model calibration process was guided by a general philosophy with the following tenants: 1) Look for underlying causes of poor model fit and address via explanatory variables. Poor model fit was explored via additional summaries and explicit utility calculation traces in an attempt to identify underlying problems in model specification or implementation. Sometimes this revealed software bugs that, once addressed, fixed the poor model fit. In a few cases, this process revealed an underlying problem with the model specification that needed to be addressed via additional estimation. For example, the stop duration model was re-estimated with a set of period-specific constants in order to better match the observed non-linear duration distributions. 2) Avoid alternative-specific constants tied to specific geographies. In general, we avoid constants that are tied to specific geographic areas, and instead look for underlying causes that explain why certain geographic areas are over or under-estimated. For example, the old workplace location choice models had an alternative-specific constant for intra-Fulton County work flows. New workplace location choice models were developed this year with occupation-specific size terms, and the new models do not require the term. The only geography-specific constant in the model system is a CBD constant for transit, which reflects the increased non-auto accessibility of the CBD to all other destinations. 3) Evaluate magnitude of constants for reasonableness. Sometimes, very large magnitude constants are necessary because certain alternatives are observed very infrequently. For example, there are few households with two or more joint tours, and a large negative constant is needed to reflect this behavior even after accounting for previously scheduled mandatory activities and other household characteristics. However, some types of constants, such as those that represent the non-included attributes of premium transit, should be in a reasonable range considering the types of non-included attributes compared to the base alternative. In general, when it comes to evaluating the appropriate size of such constants, we believe that it is better to have a model with reasonable constants, even though the model may not match observed data, as well as a model with unreasonable constants, and to formulate a plan for potential future enhancements that may eventually improve the accuracy of the model. 4) Avoid alternative availability rules that result in elasticity ‘cliff effects’. Examples of such rules include cutting off the availability of transit if the trip distance is less than a certain threshold. Such rules are convenient ways to ensure that the model matches observed data, but can cause illogical results when using the model in forecasting. In such cases, we would prefer to use a continuous function in which disutility is high at short distances and low, or eventually zero, at long distances. In general, the procedure for calibrating a choice model is as follows. First, the model output is aggregated along the dimensions we want to compare. For instance, in the context of auto ownership models, a few dimensions of interest would be the total number of households by number of vehicles owned and the number of households by number of workers and vehicles owned. The corresponding observed distributions are obtained from the ARC Regional Travel Survey. Based on the comparison between the observed and the predicted distributions adjustment factors are calculated. These adjustment factors are computed by taking the natural log of the observed shares divided by the predicted shares. Further, the adjustments are normalized by setting one alternative as a baseline and subtracting it’s adjustment from all the alternatives. The adjustment factors so computed are believed to explain for the difference between observed and predicted shares (arising due to significant unobserved factors (to the analyst) at the time of model estimation). The new model alternative specific constants are derived by adding these adjustments to the existing constants and are applied to the Utility Expression Calculator (UECs). The steps described till now are repeated till the predicted and the observed distributions are satisfactory close. A dampening factor (typically set to 0.5) is applied to the constants to help eliminate oscillating patterns between iterations of the calibration routine. A slightly different approach is used to calibrate the models where the response variable follows a continuous distribution. A good example of this is the tour length frequency distributions for destination choice models or out-of-direction distance distributions for the stop destination choice models. In such cases, the variable is grouped into bins (say, 0.5 mile distance bins) and an adjustment factor is computed for each bin as the natural log of observed divided by estimated. These adjustments are then regressed on the linear and the polynomial terms of the variable which are in the destination choice model (such as linear distance, distance-squared, distance- cubed and log of distance). The coefficients obtained on each of the terms in this regression would serve to explain the difference in the observed and the predicted distributions. Additionally, bin specific constants are applied to explain peaks and valleys in the distribution. As with the previous method, the coefficients from the regression are added to the existing coefficients before updating the UECs. In the next sections, the details of calibration and results for each of the ARC-ABM model component is discussed. Mandatory (workplace/university/school) Activity Location Choice Work Location Results The work destination choice model predicts the usual work location for all workers in the population. The model uses size terms to capture the “attractiveness” of the region as a usual work location. The model uses size terms developed for total employment by occupation as opposed to total employment by income from the earlier model. These size terms were developed from 2007-2011 ACS PUMS data. Each worker was coded according to their occupation category, consistent with PECAS occupations, and their NAICS industry category, consistent with the model input employment data. Then the size terms were calculated by cross-tabulating workers by occupation and industry and calculating row percentages, indicating the share of workers by occupation who work in each industry. The size terms are shown in Table 1. Table 1: Work Location Choice Size Terms Occupation Industry White Retail And Blue Services Health Collar Food Collar Agr,Forest,Fish 0.3005 0.0908 0.0111 0.0171 0.5805 Mining,Oil 0.4115 0.0573 0.0000 0.0663 0.4649 Utilities 0.5500 0.0226 0.0027 0.0200 0.4047 Construction 0.2212 0.0100 0.0007 0.0125 0.7556 Manufacturing 0.4031 0.0232 0.0025 0.0676 0.5036 Wholesale 0.3682 0.0112 0.0011 0.3788 0.2407 Retail 0.2471 0.0264 0.0268 0.5602 0.1395 Transport,Warehouse 0.4008 0.0528 0.0014 0.0202 0.5249 Information 0.6219 0.1366 0.0005 0.1225 0.1184 Finance,Insur 0.7912 0.0125 0.0064 0.1830 0.0068 RealEstate 0.4207 0.0740 0.0004 0.3993 0.1055 Prof,Scien,Tech 0.8401 0.0721 0.0174 0.0436 0.0269 Management 0.8676 0.0493 0.0042 0.0347 0.0442 Admin,Support,WasteMa 0.3514 0.4302 0.0192 0.0549 0.1444 Educ 0.8199 0.0780 0.0219 0.0405 0.0397 HealthCare 0.3841 0.1146 0.4595 0.0216 0.0201 Arts,Enter,Rec 0.1738 0.6212 0.0121 0.1503 0.0426 Accom,FoodService 0.1786 0.0595 0.0003 0.7264 0.0352 OtherNonPubAdmin 0.3029 0.3837 0.0128 0.0607 0.2399 PublicAdmin 0.6251 0.2617 0.0273 0.0062 0.0796 After implementation of the revised size terms, the results from this model was compared against the ACS county to county worker flows data and the 2011 ARC Regional Travel Survey.1 No calibration was necessary for this model, as comparisons indicated that the estimated model replicated observed worker flows very well in the base year.
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