Travel Demand Forecasting: Parameters and Techniques (2012) / Chapter Skim
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Pages 100-113
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From page 100... ...
7.2.1 Trip Generation Trip Production Rates Trip production rates for Gtown for all trip purposes are applied using a three-dimensional, cross-classification model with household size, number of vehicles, and income level as variables. All person trips are modeled, including nonmotorized trips.
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From page 101... ...
Autos Persons 1 2 3 4 5+ Average 0 0.2 0.7 1.1 1.0 0.9 0.5 1 0.6 0.8 1.2 1.7 1.5 0.8 2 0.7 1.3 2.0 2.0 2.3 1.6 3+ 0.9 1.4 2.6 2.9 3.3 2.3 Average 0.5 1.2 2.0 2.3 2.4 1.4 Gtown Trip Rates Autos Persons 1 2 3 4 Average 0 0.9 1.3 1.4 1.5 1.1 1 0.9 1.4 1.8 1.8 1.3 2 1.0 1.6 2.0 2.1 1.8 3+ 1.0 1.7 2.4 2.7 2.2 Average 0.9 1.5 2.1 2.2 1.7 Table 7.1. Comparison of Gtown HBW trip production rates to NHTS data from Table C.5.
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From page 102... ...
Vehicles Household Size 1 2 3 4 5+ Average 0 0.7 1.7 2.0 3.7 3.9 1.3 1 1.4 2.3 3.5 3.9 3.9 2.0 2 1.6 2.6 3.9 5.5 5.6 3.5 3+ 1.6 2.7 4.5 5.8 7.1 4.4 Average 1.3 2.5 3.8 5.3 5.7 3.0 Gtown Trip Rates Autos Persons 1 2 3 4 Average 0 1.0 1.3 1.7 2.1 1.2 1 1.4 2.0 2.3 3.1 1.8 2 1.7 2.1 2.3 3.2 2.5 3+ 2.0 2.4 2.5 3.6 2.9 Average 1.5 2.1 2.3 3.2 2.3 Table 7.3. Comparison of Gtown NHB trip production rates to NHTS data from Table C.7.
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From page 103... ...
7.2.2 Trip Distribution The reasonableness of the Gtown trip distribution model can be assessed by comparing the friction factors used in the Gtown gravity model and the resulting average trip lengths with comparable values provided in Section 4.5. Average Trip Length Table C.10 provides average trip length by mode (travel times in minutes)
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From page 104... ...
However, since the average travel times for NHB trips from the Gtown model are the same as those from the NHTS, the difference in friction factors may not be significant. 7.2.3 Mode Choice The Gtown model uses a nested logit mode choice model with coefficients for the classic three trip purposes.
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From page 105... ...
Home-based work trip distribution friction factors. 0.1 1.0 10.0 100.0 1,000.0 10,000.0 100,000.0 1,000,000.0 10,000,000.0 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 HBNW MPO 1 HBNW MPO 2 HBNW MPO 3 HBS Gtown Income 1 HBS Gtown Income 2 HBS Gtown Income 3 HBS Gtown Income 4 HBO Gtown Income 1 HBO Gtown Income 2 HBO Gtown Income 3 HBO Gtown Income 4 Travel time (min)
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From page 106... ...
The coefficients of the Gtown HBW mode choice model are not too different from those of Models F, G, and I, although the Gtown cost coefficients are lower in absolute value. Looking at the relationships between coefficients, Table 7.7 shows that the ratio of the out-of-vehicle time and invehicle time coefficients in the Gtown model is comparable to those for Models F, G, and I, as shown in Table 4.9.
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From page 107... ...
The other models shown in Tables 4.14 and 4.15 have coefficient values that vary widely, but the coefficients from Gtown fit well within this range. In summary, the value of time, indicating the willingness to pay for travel timesavings by switching modes, seems high for home-based trips in the Gtown model.
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From page 108... ...
7.2.6 Summary This section provides a comparison of model parameters and results produced by the model for a hypothetical large MPO and the values in this report. Overall, the Gtown model parameters and results appear to be reasonable when compared to the values in Chapter 4 of the report, although some Gtown model parameters, such as cost coefficients in the mode choice models for home-based trip purposes, should be checked further.
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From page 109... ...
School enrollment data by school were provided by the Schultzville School District and allocated to the appropriate TAZs; this information was supplemented by information the MPO collected directly from the larger private schools in the region. 7.3.3 Trip Generation Trip Productions The MPO was able to develop estimates of households crossclassified by household size and number of vehicles, and by workers by number of vehicles for each zone.
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From page 110... ...
7.3.4 Trip Distribution The doubly constrained gravity model, described in Equation 4-5, was used as the trip distribution model for Schultzville. The inputs to the trip distribution model include: • The trip generation outputs -- productions and attractions by trip purpose for each zone; • Highway travel time, as the measure of travel cost between each pair of zones; and • Friction factors, as discussed in the following section.
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From page 111... ...
As can be seen in Table 7.15, the average trip lengths resulting from this initial set of friction factors are lower than the average travel times reported in Table C.10. Since Schultzville is a small geographic area with little congestion, one might expect that the average trip length would be lower than the NHTS average reported for all areas with a population less than 500,000.
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From page 112... ...
The comparison of the mean travel times resulting from the use of these revised friction factors with those from Table C.10 is shown in Table 7.16. The final friction factors are not as steep as those that were initially used and result in mean travel times closer to those shown in Table C.10.
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From page 113... ...
In this diagram, the width of each link in the network is proportional to the volume on that link. An assessment was made of the quality of the traffic assignment on links where traffic counts were available by comparing the root mean square error (RMSE)
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