4 Model Inputs

Modifiable Inputs

Files	Brief Description	Data Development
Networks
scenario_routes.rts	Transit route system network used in a scenario.	Model User
scenario_links.dbd	Highway network used in a scenario including highway and transit only links.	Model User
init_cong_time.bin	Congested travel times from a tightly converged model run used as a ‘warm start’ for feedback in Choice Models.	Caliper/Model User
SE Data
scenario_se.bin	TAZ-level socio-economic data including HH, pop, population descriptive variables, employment, univ stud, K12 stud, parking data, external station perc	Census, MPOs, TJCOG (CommunityViz)

Unmodifiable Inputs

Files	Brief Description	Data Development
Accessibility
accessibilities.csv	Parameters for gamma function and attraction rates which will be used for the walkability model and the GS index.	Caliper
attraction_rates.csv	Attraction rates used for the walkability model and the GS index.	Caliper
Airport
airport_ directionality.csv	Directionality factors to convert airport PA matrix into OD format.	Caliper
airport_model.csv	Coefficients for calculating airport production.	Caliper
airport_tod.csv	Time of day factors.	Caliper
Assignment
vot_params.csv	Values representing what a driver is willing to pay to save time by using a limited access toll facility. The values have been calibrated to match modeled and observed volumes.	Caliper
CV
cv_generation.csv	Coefficients by variable and CV type (CV, SUT, MUT).
cv_gravity_xx.csv	Gravity model inputs by CV type.
cv_tod.csv	TOD factors by CV type.
External
ee_tod.csv	EE trip % by vehicle type (auto, SUT, MUT) and TOD. Percentages are the same for each vehicle type and are borrowed.	The data available was not adequate to calculate EE TOD shares. Instead, CV factors were used.
ee-seed.csv	EE beginning value for EE trip calculations from O_TAZ to D_TAZ for three vehicle types (auto, SUT, MUT).
ieei_ directionality.csv	IEEI PA factors by vehicle type and TOD. Same value across vehicle types.
ieei_gravity.csv	Gravity model coefficients.	IE/EI trips are controlled by external station volumes after EE travel is subtracted. These trips are distributed using a gravity model. For this model form, attractions and gamma parameters must be estimated.
ieei_model.csv	Coefficient for employment and population variables.
ieei_tod.csv	TOD factors for IEEI trips.	The data available was not adequate to calculate IE/EI TOD shares. Instead, N_HB_OD_Long factors were used (similar to HBO in other models). Compared to using work trips, this shifts demand from the AM and PM periods into the off peak. These travelers (e.g. with long commutes to work) need to leave earlier in the day. They cross the model boundary earlier (and later) than internal residents.
ieei_transit.csv	Zone to zone IEEI transit trips by transit type and period.
Networks
_rts_creation _results.csv	Output file comparing scenario transit route dimensions to the master. Comparisons can be used to determine if the scenario route was created correctly.
bus_speeds.csv	Factors applied to auto speeds to get bus speeds for local and express buses. The factors are based on HCMType and area type.	Caliper/ borrowed from SCAG model
capacity.csv	LOS D and E hourly lane capacities calculated using HCMType and area type.	The primary source for capacities is the North Carolina Statewide Model (NCSTM) model, averaged over the entire piedmont and calibrated using NCHRP 825 and observed counts. Differentiation by median type was included.
capacity_period_ factors.csv	Hours of capacity assigned to each TOD period.	Caliper
ff_speed_ alpha_beta.csv	Adjustment factors applied to posted speeds to reflect realistic travel speeds, including HCMType, AreaType, ModifyPosted, Alpha and Beta	Caliper
transit_mode_ table.csv	Classifiction of transit modes in the network and the in vehicle trave time (IVTT), dwell, on/off times for each mode.	Caliper
Resident
/auto_ownership
ao_coefficients.csv	Coefficients for calculating the utility function of auto ownership discrete choice multinomial logistic (MNL) regression model	Variables from the synthetic population and zonal accessibility to make predictions
/dc
dc_attr_ rates.csv	Attraction rate for home based work trips (double constrain)	regression model estimated from the survey data
dc_size_terms.csv	Factor for each destination zone representing how many individual choices are in that zone. The term itself is a linear combination of employment variables and their coefficients.	Observed choice data (observed OD)
shadow_prices.bin	This is an extra term in the utility equation that is adjusted iteratively to match predicted attractions (double constrain). Shadow price can represent the unknown cost (impedance) of a choosing a destination.	Crated in model estimation iteration
16 more csv files	Coefficients for destination models by trip type (3 work tour and 5 non-work tour types) by zone/cluster.	Statistical model estimation
/disagg_model
income_curves.csv	The curve used to create synthesised income categories for each zone from income ratio. It is used in synthesizing the population of residents.	Estimated from ACS 5-year file
size_curves.csv	The curve used to create synthesised household size (1, 2, 3, 4+) for each zone from average size. It is used in synthesizing the population of residents.	Multiple data product
worker_curves.csv	The curve used to create synthesised household workers (0, 1, 2, 3+) for each zone from average size. It is used in synthesizing the population of residents.	Estimated from CTPP data
/generation
calibration_ factors.csv	The calibration factors are the ratio of the observed to modeled trips. They are applied to ensure total trip making matches the survey (based on trip weight).	Household survey
production_ rates.csv	Trip generation production rates for 8 trip types.	Household survey use decision Trees (DT)
/mode
Target_HB_ MCShares.csv	The targets of home-based trips that the mode choice ASCs were calibrated to match. Cell is the % of each mode.	Generated from the survey for each combination of trip purpose and HH market segment.
8 *_nest.csv files	Parent nest coefficient for each HB trip type.	household survey, transit parent nest coefficients are from transit submodel
8 more csv files	Coefficient used for utility functions by each HB trip type (5 for Nonwork HB trips; 3 for work HB trips). Note NHB mode is conditional based on the mode of the HB leg of the tour. So NHB are not included here. Note not all modes are available for all trip types (e.g. knr and pnr does not apply to K12 trips.)	household survey, onboard survey,FTA/STOPS guidance(for those mode that does not exist today)
/nhb
nhb_dc_size_ terms.csv	Factor for each destination zone representing how many individual choices are in that zone. The term itself is a linear combination of employment variables (K12 also used for auto trips).	Developed based on observed choice data (observed OD)
nhb_n_ auto_dc.csv	Destination choice model coefficients for non-work non-HB trips by auto	Observed OD
nhb_ transit_dc.csv	Destination choice model coefficients for non-HB trips by transit	Observed OD
nhb_w_ auto_dc.csv	Destination choice model coefficients for work non-HB trips by auto	Observed OD
nhb_ walkbike_dc.csv	Destination choice model coefficients for non-work non-HB NM trips	Observed OD
nhb_calibration_factors.csv	Calibration factors or ratios of observed to modeled trips. They are applied to ensure total trip making matches the survey (based on trip weights).	Household survey
29 more csv files	The model coefficients for NHB trips as the result of multiple linear regression with a forced intercept at zero, the only predictor in the model is the HB trips by trip type.	Household survey
/nonmotorized
n_hb_**_all.csv	Binary model of nonmotorized choice for non-work HB ** trips (8 trip types).	Household survey
nm_gravity.csv	Factors for using a gravity model capturing the impact of walking time to each nonmotorized trip choice.	Household survey
/parking
ParkandShuttle _CBD/Univ.csv	Model for whether auto travelers will park and take transit to their ultimate destination in CBD / Univ.	Household survey
ParkandWalk _CBD/Univ.csv	Model for whether auto travelers will park and walk to their ultimate destination for CBD / Univ.	Household survey
/population_synthesis
HHSeed_PUMP _TRM.bin	Household seed needed for IPF to generate synthetic households.	ACS 2014-2018 data
PersonSeed_PUMS _TRM.bin	Person seed needed for IPF to generate synthetic population.	ACS 2014-2018 data
/tod
directionality _factors.csv	Percentage of trips that start from home for each purpose and TOD.	Household survey
directionality_ skim_factors.csv	Directional factors used to average skims. Note: a simple 50/50 split was used for NHB trips and all off-peak trips.	Household survey
hov3_occ_factors _hb.csv	Factors used to convert HB PA to OD for HOV3 trips.	Household survey
hov3_occ_factors _nhb.csv	Factors used to convert NHB PA to OD for HOV3 trips.	Household survey
other_shares_hb.csv	Shares of SOV, HOV2, HOV3 for HB trips by trip type.	Household survey
other_shares _nhb.csv	Shares of SOV, HOV2, HOV3 for NHB trips by tour type.	Household survey
time_of_day _factors.csv	Resident TOD factors or the percentage of each trip type into each of the four time periods.	Household survey
TAZs
scenario_tazs.dbd	Geographic layer with all TAZs including county, mpo, tract and BG	TRM Team ; grouping similar land uses taking into account barriers like major roadways, rivers, lakes
University
university_ attraction_rates.csv	Attraction rates (intercept, retail_emp, student_off) for uho_on, uho_off and uco.
university_ directionality.csv	P&A direction factors for university trips by TOD.
university_ gravity.csv	P&A rates for each major university and trip type.
university_ production_rates.csv	Trip production rates by university trip type	Trip production rates were developed based on the NCSU survey and were compared for reasonableness to the Virginia university surveys.
university_ tod.csv	University TOD factors for each trip type.
university_ trip_rates_other.csv	Trip rates for other modes (auto, w_lb, walk, bike).
univ_mc_***.csv	University mode choice coefficients for four modes and five trip types