Improving ADA Paratransit Demand Estimation: Regional Modeling (2012) / Chapter Skim
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26 C h a p t e r 3 This chapter describes the component models that make up the full modeling systems, how these components are related to each other, the results of the model estimation process, how the models were calibrated, and the results of sensitivity tests. Overview of the Model Components The model system is made up of aggregate components and disaggregate components: • The aggregate components include a model that predicts the number of registered users of the ADA paratransit system living in each census tract and traffic analysis zone (TAZ)
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the aDa paratransit Demand Models 27 Although the first approach is more typical in applied travel demand modeling, the second approach seems more accurate for this study context. There are at least two important reasons for this.
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28 Improving aDa paratransit Demand estimation: regional Modeling previous 12 months. (Those characteristics describe the sampling universe for our survey sample.)
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the aDa paratransit Demand Models 29 The intermediate stop generation model: As part of the 1,030 home-based tours in the data, there were approximately 300 "extra" stops made in addition to the stop at the primary tour destination. This small number of stops was deemed important enough to include in the model system, but not large enough to estimate a detailed model with all household and person characteristics.
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30 Improving aDa paratransit Demand estimation: regional Modeling per census tract, with about 90 of those tracts having only one registered person, and 9 tracts having over 100 registered persons. One extreme case is a tract in the Fort Worth area that has 555 registered persons, almost 30% of the adults in that census tract.
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the aDa paratransit Demand Models 31 Income: Two income variables were found to be significant. The first is that the registration rate increases with the fraction of the population with incomes below the poverty level.
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32 Improving aDa paratransit Demand estimation: regional Modeling the prediction divided by the percent change in the input variable) is in the range of 0.2 to 0.4 for most of the variables tested.
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the aDa paratransit Demand Models 33 specific TAZ. This assumes that the spatial distribution of the ADA-eligible population across zones in the tract is the same as the spatial distribution of the general population.
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34 Improving aDa paratransit Demand estimation: regional Modeling calculated by dividing the fare coefficient of –0.168 for this group by the fare coefficient of -0.288 for the lower income group. The result is shown in the Equivalent Fare column.
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the aDa paratransit Demand Models 35 Time may have to wait on hold on the telephone: This variable also has an unexpected sign, with a slight positive coefficient for more minutes spent waiting on hold. In this case, however, the result is not significantly different from zero, so we can assume that the effect of this variable is negligible compared to the other ones, and omit this variable from model application.
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36 Improving aDa paratransit Demand estimation: regional Modeling Details of the Disaggregate Models Disaggregate Model Input Data Three main types of data were used to estimate the disaggregate models: 1. Travel survey data: Full trip diary data for 2 days for each of 800 respondents 2.
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the aDa paratransit Demand Models 37 and tour mode choice models, while the generalized ADA paratransit time is used in the model of destination choice for ADA paratransit trips. Many regions have attraction employment variables split into more categories than the list above, including government employment, office employment, food service employment, and entertainment employment.
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38 Improving aDa paratransit Demand estimation: regional Modeling • Civic/religious • Recreation • Social visit In cases where more than one destination was visited during a tour, the primary destination and purpose was defined as the one with the highest priority activity purpose, with the assumed priority order as above -- medical the highest and social visit the lowest. In cases where the same activity purpose was carried out at two different destinations within a tour, the primary tour destination was selected as the one with the longest duration of stay at that location.
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the aDa paratransit Demand Models 39 Model name Tgen2bw # Observations 2612 Final log-likelihood -2664.0 Rho squared w.r.t. 0 0.536 Rho squared w.r.t.
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40 Improving aDa paratransit Demand estimation: regional Modeling Weighting of observations: In order to gain efficiency in data collection and heterogeneity in the sample, the survey sample was drawn using a stratified approach, oversampling frequent ADA paratransit users and younger age groups. In a tour frequency model, the choice probability of making a tour is not independent of the probability of being in the sample, particularly given that we oversampled more frequent travelers.
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the aDa paratransit Demand Models 41 estimate income effects reliably. Compared to those with higher incomes, those with the lowest incomes are more likely to attend adult daycare, but less likely to travel for social visits and medical purposes.
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42 Improving aDa paratransit Demand estimation: regional Modeling Service area: About 51% of the observations are from those living in the DART (Dallas) service area and the other 49% are from the MITS (Fort Worth)
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the aDa paratransit Demand Models 43 Social tour 2.0 -1.008 -2.1 DART area 52.3 0.268 1.7 Residual constant 100.0 -6.534 -0.277 -4.327 -8.444 -1.519 -8.1 -1.0 -- 4.9 -2.6 -1.0 Model name mode2bw # Observations 1022 Final log- likelihood -971.1 Rho squared w.r.t. 0 0.429 Rho squared w.r.t.
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44 Improving aDa paratransit Demand estimation: regional Modeling 2. Road-based, fixed route and schedule: (2a)
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the aDa paratransit Demand Models 45 discretionary purposes -- shopping, meals, recreation, and social visits. Other specialized transit services are most likely to be chosen for work, school, and medical purposes, corresponding to the types of institutions/facilities most likely to offer those services.
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46 Improving aDa paratransit Demand estimation: regional Modeling Age 40-54 22.5% -0.323 -2.2 Physical impairment 70.2% 0.707 3.9 Sensory impairment 24.8% 0.543 3.0 Proxy data 6.2% 0.458 1.6 Number of tours in day 100.0% 1.066 5.1 1st half tour 46.7% 1.297 5.4 2nd half tour- No. of trips in 1st half tour 53.3% 0.254 1.6 Already made 1 stop 11.2% -0.394 in half tour -2.3 Already made 2 stops 3.6% 0.184 in half tour 0.7 Already made 3 stops 1.2% 1.224 in half tour 2.3 Residual constant 100.0 -1.606 -2.748 -0.923 -2.342 0.675 -1.632 -1.016 0.557 -3.5 -3.7 -2.9 -4.1 5.2 -6.6 -5.2 1.2 Model name sgen2bw # Observations 2342 Final log- likelihood -1239.2 Rho squared w.r.t.
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the aDa paratransit Demand Models 47 Household income: Those in lower income households tend to make fewer extra stops per tour, on average. It is a typical result in travel studies that the number of activities outside the home tends to increase with income.
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48 Improving aDa paratransit Demand estimation: regional Modeling mode is different from the tour mode. Most of those cases are car passenger trips as part of ADA paratransit tours (97 cases)
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the aDa paratransit Demand Models 49 • The impedance function: An average of the travel time to go from the home location to each possible destination zone and the time to return back home again. • The attraction function: A composite function of the number of jobs and households in each possible destination zone that attract trips to that zone.
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50 Improving aDa paratransit Demand estimation: regional Modeling The impedance function: Separate impedance functions can be used for the six different trip modes. The destinations for car drive-alone and shared-ride trips are the most sensitive to automobile travel time, with coefficients of –0.181 and –0.172, respectively -- both with very high t-statistics.
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the aDa paratransit Demand Models 51 Even though the NCTCOG zone system is detailed, with over 5,000 zones in the region, some observed trips in the survey data are intra-zonal, meaning that the origin and destination address are in the same TAZ. In those cases, there is no zone-to-zone travel time information to use in the models, so a separate dummy variable is added for the origin zone alternative to represent the probability of making an intra-zonal trip.
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52 Improving aDa paratransit Demand estimation: regional Modeling The second section of the first table is based on full client databases provided by DART and MITS (excluding anyone who did not make any trips and did not register in the previous 12 months)
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the aDa paratransit Demand Models 53 on previously observed behavior. This is most likely a survey-related bias that needs to be corrected in model calibration.
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54 Improving aDa paratransit Demand estimation: regional Modeling registered population, and then making corresponding changes to the survey expansion factors in the ADA population synthesis procedure. Three scenarios were tested: 1.
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the aDa paratransit Demand Models 55 number of ADA paratransit trips per person, for a net negative elasticity of roughly -0.4. Aging of the population (increase in the fraction of people over age 60)
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