This module calculates household transit trips, walk trips, and bike trips. The models are sensitive to household DVMT so they are run after all household DVMT adjustments (e.g. to account for cost on household DVMT) are made.
Hurdle models are estimated for calculating the numbers of household transit, walk, and bike trips using the pscl package. Separate models are calculated for metropolitan and non-metropolitan households to account for the additional variables available in metropolitan areas.
Following are the estimation statistics for the metropolitan and nonmetropolitan walk trip models.
Metropolitan Walk Trip Model
Call:
hurdle(formula = ModelFormula, data = Data_df, dist = "poisson", zero.dist = "binomial",
link = "logit")
Pearson residuals:
Min 1Q Median 3Q Max
-4.7373 -1.3362 -0.5994 0.5859 32.2096
Count model coefficients (truncated poisson with log link):
Estimate Std. Error z value Pr(>|z|)
(Intercept) 4.588e+00 6.696e-03 685.10 <2e-16 ***
HhSize 3.171e-01 8.368e-04 378.95 <2e-16 ***
LogIncome 1.417e-01 6.557e-04 216.19 <2e-16 ***
LogDensity -4.032e-03 3.344e-04 -12.06 <2e-16 ***
BusEqRevMiPC 1.632e-03 1.302e-05 125.36 <2e-16 ***
Urban 4.580e-02 6.120e-04 74.84 <2e-16 ***
LogDvmt -2.187e-01 7.165e-04 -305.19 <2e-16 ***
Age0to14 -3.256e-01 9.078e-04 -358.64 <2e-16 ***
Age15to19 -8.747e-02 1.099e-03 -79.61 <2e-16 ***
Age20to29 4.688e-02 9.317e-04 50.31 <2e-16 ***
Age30to54 2.091e-02 7.154e-04 29.23 <2e-16 ***
Age65Plus -3.504e-02 8.485e-04 -41.30 <2e-16 ***
Zero hurdle model coefficients (binomial with logit link):
Estimate Std. Error z value Pr(>|z|)
(Intercept) -2.2779405 0.2614106 -8.714 < 2e-16 ***
HhSize 0.4593582 0.0385316 11.922 < 2e-16 ***
LogIncome 0.2794224 0.0259941 10.749 < 2e-16 ***
LogDensity 0.0232475 0.0137835 1.687 0.091678 .
BusEqRevMiPC -0.0038107 0.0005493 -6.938 3.98e-12 ***
Urban 0.0643767 0.0260840 2.468 0.013585 *
LogDvmt -0.2552992 0.0313200 -8.151 3.60e-16 ***
Age0to14 -0.3715079 0.0422595 -8.791 < 2e-16 ***
Age15to19 -0.1962355 0.0555825 -3.531 0.000415 ***
Age20to29 0.0929654 0.0428981 2.167 0.030226 *
Age30to54 0.0648896 0.0309901 2.094 0.036271 *
Age65Plus -0.0372897 0.0344148 -1.084 0.278571
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Number of iterations in BFGS optimization: 18
Log-likelihood: -2.475e+06 on 24 Df
Nonmetropolitan Walk Trip Model
Call:
hurdle(formula = ModelFormula, data = Data_df, dist = "poisson", zero.dist = "binomial",
link = "logit")
Pearson residuals:
Min 1Q Median 3Q Max
-2.9708 -1.2632 -0.5842 0.5359 34.5821
Count model coefficients (truncated poisson with log link):
Estimate Std. Error z value Pr(>|z|)
(Intercept) 6.1534005 0.0044802 1373.47 <2e-16 ***
HhSize 0.3302854 0.0006750 489.34 <2e-16 ***
LogIncome -0.0238485 0.0005589 -42.67 <2e-16 ***
LogDensity -0.0377050 0.0001842 -204.67 <2e-16 ***
LogDvmt -0.0412971 0.0010340 -39.94 <2e-16 ***
Age0to14 -0.3605450 0.0007034 -512.59 <2e-16 ***
Age15to19 -0.1465219 0.0008402 -174.40 <2e-16 ***
Age20to29 0.0241464 0.0006777 35.63 <2e-16 ***
Age30to54 -0.0190250 0.0005360 -35.49 <2e-16 ***
Age65Plus -0.0301608 0.0006138 -49.14 <2e-16 ***
Zero hurdle model coefficients (binomial with logit link):
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.498728 0.159757 -3.122 0.00180 **
HhSize 0.144693 0.029160 4.962 6.98e-07 ***
LogIncome 0.057696 0.019819 2.911 0.00360 **
LogDensity -0.034436 0.006878 -5.007 5.53e-07 ***
LogDvmt 0.118819 0.036144 3.287 0.00101 **
Age0to14 -0.190284 0.030047 -6.333 2.40e-10 ***
Age15to19 0.021423 0.038482 0.557 0.57774
Age20to29 0.092751 0.028744 3.227 0.00125 **
Age30to54 0.064009 0.021311 3.004 0.00267 **
Age65Plus -0.049782 0.023213 -2.145 0.03199 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Number of iterations in BFGS optimization: 16
Log-likelihood: -4.195e+06 on 20 Df
Following are the estimation statistics for the metropolitan and nonmetropolitan bike trip models.
Metropolitan Bike Trip Model
Call:
hurdle(formula = ModelFormula, data = Data_df, dist = "poisson", zero.dist = "binomial",
link = "logit")
Pearson residuals:
Min 1Q Median 3Q Max
-1.2354 -0.3524 -0.2790 -0.2282 34.1229
Count model coefficients (truncated poisson with log link):
Estimate Std. Error z value Pr(>|z|)
(Intercept) 6.330e+00 2.473e-02 255.94 <2e-16 ***
HhSize 1.105e-01 4.271e-03 25.88 <2e-16 ***
LogIncome -6.754e-02 2.903e-03 -23.27 <2e-16 ***
BusEqRevMiPC -2.240e-03 6.019e-05 -37.22 <2e-16 ***
LogDvmt -1.697e-01 3.325e-03 -51.03 <2e-16 ***
Age0to14 -1.936e-01 4.480e-03 -43.23 <2e-16 ***
Age15to19 -1.357e-01 5.291e-03 -25.64 <2e-16 ***
Age20to29 7.879e-02 4.425e-03 17.80 <2e-16 ***
Age30to54 7.896e-02 3.618e-03 21.83 <2e-16 ***
Age65Plus 4.916e-02 4.317e-03 11.38 <2e-16 ***
Zero hurdle model coefficients (binomial with logit link):
Estimate Std. Error z value Pr(>|z|)
(Intercept) -4.7713578 0.3801813 -12.550 < 2e-16 ***
HhSize 0.1658710 0.0580388 2.858 0.004264 **
LogIncome 0.2005757 0.0431518 4.648 3.35e-06 ***
BusEqRevMiPC -0.0061592 0.0008648 -7.122 1.06e-12 ***
LogDvmt -0.0399244 0.0495123 -0.806 0.420040
Age0to14 0.0452386 0.0599474 0.755 0.450466
Age15to19 0.2071779 0.0717209 2.889 0.003869 **
Age20to29 0.2301316 0.0614262 3.746 0.000179 ***
Age30to54 0.1712935 0.0483191 3.545 0.000393 ***
Age65Plus -0.0756286 0.0613717 -1.232 0.217836
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Number of iterations in BFGS optimization: 36
Log-likelihood: -1.193e+05 on 20 Df
Nonmetropolitan Bike Trip Model
Call:
hurdle(formula = ModelFormula, data = Data_df, dist = "poisson", zero.dist = "binomial",
link = "logit")
Pearson residuals:
Min 1Q Median 3Q Max
-2.4409 -0.3567 -0.2792 -0.2273 57.2919
Count model coefficients (truncated poisson with log link):
Estimate Std. Error z value Pr(>|z|)
(Intercept) 5.892104 0.017447 337.71 <2e-16 ***
HhSize 0.242550 0.002576 94.17 <2e-16 ***
LogIncome 0.033409 0.002126 15.71 <2e-16 ***
LogDvmt -0.387245 0.003255 -118.98 <2e-16 ***
Age0to14 -0.289406 0.002742 -105.55 <2e-16 ***
Age15to19 -0.092299 0.003159 -29.21 <2e-16 ***
Age20to29 0.095888 0.002578 37.19 <2e-16 ***
Age30to54 0.024688 0.002280 10.83 <2e-16 ***
Age65Plus -0.033849 0.002863 -11.82 <2e-16 ***
Zero hurdle model coefficients (binomial with logit link):
Estimate Std. Error z value Pr(>|z|)
(Intercept) -4.62279 0.27757 -16.654 < 2e-16 ***
HhSize 0.21575 0.04553 4.738 2.15e-06 ***
LogIncome 0.19353 0.03287 5.887 3.93e-09 ***
LogDvmt -0.12136 0.05767 -2.104 0.035354 *
Age0to14 -0.05063 0.04528 -1.118 0.263517
Age15to19 0.18242 0.05279 3.456 0.000549 ***
Age20to29 0.25782 0.04318 5.971 2.36e-09 ***
Age30to54 0.17485 0.03476 5.031 4.89e-07 ***
Age65Plus -0.18268 0.04441 -4.114 3.90e-05 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Number of iterations in BFGS optimization: 18
Log-likelihood: -2.874e+05 on 18 Df
Following are the estimation statistics for the metropolitan and nonmetropolitan transit trip models.
Metropolitan Transit Trip Model
Call:
hurdle(formula = ModelFormula, data = Data_df, dist = "poisson", zero.dist = "binomial",
link = "logit")
Pearson residuals:
Min 1Q Median 3Q Max
-3.8998 -0.3420 -0.2262 -0.1478 34.7586
Count model coefficients (truncated poisson with log link):
Estimate Std. Error z value Pr(>|z|)
(Intercept) 6.029e+00 1.043e-02 577.932 <2e-16 ***
HhSize 1.347e-02 6.685e-04 20.148 <2e-16 ***
LogIncome 5.790e-02 1.000e-03 57.896 <2e-16 ***
LogDensity 4.491e-02 5.881e-04 76.364 <2e-16 ***
BusEqRevMiPC 1.888e-03 2.542e-05 74.259 <2e-16 ***
LogDvmt -9.576e-02 1.004e-03 -95.366 <2e-16 ***
Urban 3.454e-02 1.076e-03 32.110 <2e-16 ***
Age15to19 -1.256e-03 1.207e-03 -1.041 0.298
Age20to29 6.571e-02 1.333e-03 49.312 <2e-16 ***
Age30to54 4.872e-02 1.262e-03 38.620 <2e-16 ***
Age65Plus 8.644e-03 1.662e-03 5.200 2e-07 ***
Zero hurdle model coefficients (binomial with logit link):
Estimate Std. Error z value Pr(>|z|)
(Intercept) -4.1704957 0.4117472 -10.129 < 2e-16 ***
HhSize 0.5182818 0.0245849 21.081 < 2e-16 ***
LogIncome 0.3439376 0.0403431 8.525 < 2e-16 ***
LogDensity -0.0399763 0.0214212 -1.866 0.062014 .
BusEqRevMiPC 0.0097638 0.0008815 11.077 < 2e-16 ***
LogDvmt -1.0649137 0.0416130 -25.591 < 2e-16 ***
Urban 0.0713825 0.0400027 1.784 0.074352 .
Age15to19 0.3066829 0.0470226 6.522 6.94e-11 ***
Age20to29 0.1935694 0.0521167 3.714 0.000204 ***
Age30to54 0.3872192 0.0466700 8.297 < 2e-16 ***
Age65Plus -0.5825307 0.0644610 -9.037 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Number of iterations in BFGS optimization: 21
Log-likelihood: -3.382e+05 on 22 Df
Nonmetropolitan Transit Trip Model
Call:
hurdle(formula = ModelFormula, data = Data_df, dist = "poisson", zero.dist = "binomial",
link = "logit")
Pearson residuals:
Min 1Q Median 3Q Max
-6.3324 -0.2401 -0.1568 -0.1048 45.1873
Count model coefficients (truncated poisson with log link):
Estimate Std. Error z value Pr(>|z|)
(Intercept) 6.7699170 0.0105608 641.04 < 2e-16 ***
HhSize 0.0532249 0.0018397 28.93 < 2e-16 ***
LogIncome 0.0588111 0.0012727 46.21 < 2e-16 ***
LogDensity -0.0124144 0.0004657 -26.66 < 2e-16 ***
LogDvmt -0.1353373 0.0019911 -67.97 < 2e-16 ***
Age0to14 -0.0083800 0.0018218 -4.60 4.23e-06 ***
Age15to19 0.0236052 0.0020681 11.41 < 2e-16 ***
Age20to29 -0.0322833 0.0021758 -14.84 < 2e-16 ***
Age30to54 -0.0274216 0.0017203 -15.94 < 2e-16 ***
Age65Plus -0.0709431 0.0027659 -25.65 < 2e-16 ***
Zero hurdle model coefficients (binomial with logit link):
Estimate Std. Error z value Pr(>|z|)
(Intercept) -1.44021 0.35057 -4.108 3.99e-05 ***
HhSize 0.49047 0.06451 7.603 2.90e-14 ***
LogIncome 0.23233 0.04346 5.346 8.99e-08 ***
LogDensity -0.17540 0.01442 -12.164 < 2e-16 ***
LogDvmt -1.27574 0.06945 -18.370 < 2e-16 ***
Age0to14 0.25493 0.06336 4.023 5.74e-05 ***
Age15to19 0.38514 0.07126 5.405 6.49e-08 ***
Age20to29 0.07020 0.07214 0.973 0.331
Age30to54 0.53298 0.05692 9.363 < 2e-16 ***
Age65Plus -0.60607 0.08592 -7.054 1.74e-12 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Number of iterations in BFGS optimization: 21
Log-likelihood: -2.818e+05 on 20 Df
This module is run after all household DVMT adjustments are made due to cost, travel demand management, and light-weight vehicle (e.g. bike, scooter) diversion, so that alternative mode travel reflects the result of those influences. The alternative mode trip models are run and the results are saved.
This module has no user input requirements.
The following table documents each dataset that is retrieved from the datastore and used by the module. Each row in the table describes a dataset. All the datasets must be present in the datastore. One or more of these datasets may be entered into the datastore from the user input files. The table names and their meanings are as follows:
NAME - The dataset name.
TABLE - The table in the datastore that the data is retrieved from.
GROUP - The group in the datastore where the table is located. Note that the datastore has a group named 'Global' and groups for every model run year. For example, if the model run years are 2010 and 2050, then the datastore will have a group named '2010' and a group named '2050'. If the value for 'GROUP' is 'Year', then the dataset will exist in each model run year group. If the value for 'GROUP' is 'BaseYear' then the dataset will only exist in the base year group (e.g. '2010'). If the value for 'GROUP' is 'Global' then the dataset will only exist in the 'Global' group.
TYPE - The data type. The framework uses the type to check units and inputs. Refer to the model system design and users guide for information on allowed types.
UNITS - The units that input values need to represent. Some data types have defined units that are represented as abbreviations or combinations of abbreviations. For example 'MI/HR' means miles per hour. Many of these abbreviations are self evident, but the VisionEval model system design and users guide should be consulted.
PROHIBIT - Values that are prohibited. Values in the datastore do not meet any of the listed conditions.
ISELEMENTOF - Categorical values that are permitted. Values in the datastore are one or more of the listed values.
| NAME | TABLE | GROUP | TYPE | UNITS | PROHIBIT | ISELEMENTOF |
|---|---|---|---|---|---|---|
| Marea | Marea | Year | character | ID | ||
| TranRevMiPC | Marea | Year | compound | MI/PRSN/YR | NA, < 0 | |
| Marea | Bzone | Year | character | ID | ||
| Bzone | Bzone | Year | character | ID | ||
| D1B | Bzone | Year | compound | PRSN/SQMI | NA, < 0 | |
| Marea | Household | Year | character | ID | ||
| Bzone | Household | Year | character | ID | ||
| Age0to14 | Household | Year | people | PRSN | NA, < 0 | |
| Age15to19 | Household | Year | people | PRSN | NA, < 0 | |
| Age20to29 | Household | Year | people | PRSN | NA, < 0 | |
| Age30to54 | Household | Year | people | PRSN | NA, < 0 | |
| Age55to64 | Household | Year | people | PRSN | NA, < 0 | |
| Age65Plus | Household | Year | people | PRSN | NA, < 0 | |
| LocType | Household | Year | character | category | NA | Urban, Town, Rural |
| HhSize | Household | Year | people | PRSN | NA, <= 0 | |
| Income | Household | Year | currency | USD.2001 | NA, < 0 | |
| Vehicles | Household | Year | vehicles | VEH | NA, < 0 | |
| IsUrbanMixNbrhd | Household | Year | integer | binary | NA | 0, 1 |
| Dvmt | Household | Year | compound | MI/DAY | NA, < 0 |
The following table documents each dataset that is retrieved from the datastore and used by the module. Each row in the table describes a dataset. All the datasets must be present in the datastore. One or more of these datasets may be entered into the datastore from the user input files. The table names and their meanings are as follows:
NAME - The dataset name.
TABLE - The table in the datastore that the data is retrieved from.
GROUP - The group in the datastore where the table is located. Note that the datastore has a group named 'Global' and groups for every model run year. For example, if the model run years are 2010 and 2050, then the datastore will have a group named '2010' and a group named '2050'. If the value for 'GROUP' is 'Year', then the dataset will exist in each model run year. If the value for 'GROUP' is 'BaseYear' then the dataset will only exist in the base year group (e.g. '2010'). If the value for 'GROUP' is 'Global' then the dataset will only exist in the 'Global' group.
TYPE - The data type. The framework uses the type to check units and inputs. Refer to the model system design and users guide for information on allowed types.
UNITS - The units that input values need to represent. Some data types have defined units that are represented as abbreviations or combinations of abbreviations. For example 'MI/HR' means miles per hour. Many of these abbreviations are self evident, but the VisionEval model system design and users guide should be consulted.
PROHIBIT - Values that are prohibited. Values in the datastore do not meet any of the listed conditions.
ISELEMENTOF - Categorical values that are permitted. Values in the datastore are one or more of the listed values.
DESCRIPTION - A description of the data.
| NAME | TABLE | GROUP | TYPE | UNITS | PROHIBIT | ISELEMENTOF | DESCRIPTION |
|---|---|---|---|---|---|---|---|
| WalkTrips | Household | Year | compound | TRIP/YR | NA, < 0 | Average number of walk trips per year by household members | |
| BikeTrips | Household | Year | compound | TRIP/YR | NA, < 0 | Average number of bicycle trips per year by household members | |
| TransitTrips | Household | Year | compound | TRIP/YR | NA, < 0 | Average number of public transit trips per year by household members |