The Person Transport (PT) module generates travel for all residents in Oregon and the halo region. The PT module consists of two jointly run sub-components: Short-Distance Travel (SDT) and Long-Distance Travel (LDT).
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Overview
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Short-Distance Travel (SDT)
- Accessibilities (Tour Mode Choice and Destination Choice Logsums)
- Auto Ownership Model
- Workplace Location Choice Model
- Day Pattern Models
- Tour Scheduling Model
- Primary Tour Destination Choice Model
- Tour Primary Mode Choice Model
- Intermediate Stop Location Model
- Intermediate Stop Duration Model
- Work-Based Activity Duration Model
- Trip Mode Choice Model
- Long-Distance Travel (LDT)
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Short-Distance Travel (SDT)
- SDT Value-of-time Update
- Running PT
- Inputs
- Outputs
PT consists of two jointly run sub-components:
- Short-Distance Travel (SDT) - predicts all regular work commutes regardless of length and non-commute travel patterns less than or equal to 50 miles in length.
- Long-Distance Travel (LDT) - predicts non-commute travel patterns greater than 50 miles.
PT returns a list of short- and long-distance tours and trips with attributes including origin and destination alphazone, start time, duration and mode. PT also provides zone-to-zone O-D matrices for person auto and (inter- and intra-city) transit trips by time period and short distance tour mode choice and destination choice logsums by household category. More details about the outputs are provided in Outputs.
A flow chart illustrating the PT module is shown below.
Both SDT and LDT components operate in a micro-simulation framework and rely on the attributes of the synthetic population (from SPG2) and travel skims (from Traffic Assignment-TA and Transit Assignment-TR). The SDT component also uses labor flows from the Activity Allocation (AA) module.
First, the SDT pre-calculates mode choice and destination choice logsums as measures of travel accessibilities, expressed in utiles. Mode choice logsums represent the utility of travel across all modes of transportation for a given combination of purpose, auto sufficiency and household income for each origin and destination alpha zone. Mode choice logsums for certain combinations of purpose, auto sufficiency and income are ‘squeezed’, or collapsed, from an alpha zone level to a beta zone level for use in AA. This is done by simply averaging all alpha zone pairs within each beta zone pair, and adding a fixed dis-utility for travel to beta zones representing world markets.
The destination choice logsums represent the overall ability of travelers to access all potential destination zones for a given purpose, auto sufficiency, and income across all transportation modes from each origin zone. Destination choice utilities reflect accessibility as a utility value from each origin zone to all destinations, where the accessibility is weighted by the cost of travel across all modes of transportation and the number of activities (or size) of each destination zone.
The SDT Auto Ownership Model uses destination choice accessibilities and household attributes to predict the total number of vehicles owned by each household in the population. Next, the SDT Workplace Location Choice Model assigns a workplace location for every worker in the population using the labor flows from the AA module and the relevant mode choice logsum for the worker.
The destination choice logsums calculated by SDT are also input to the LDT Binary Choice Model to predict the probability of engaging in long-distance travel. The accessibilities allow the model to capture the effect that people who have access to many destinations within a short distance are less likely to need to make long distance trips. Given the long-term choice of travel, the LDT Pattern Choice Model then determines if travel occurs on the simulation day and if so what type. The long-distance travel choice patterns are provided back to the SDT to determine eligibility to generate short-distance travel; persons who are making out-of-town long-distance travel are prohibited from generating short-distance travel.
At this stage, short-distance and long-distance tours are identified and are modeled independently by SDT and LDT.
For the short-distance tours, first, the SDT constructs a daily activity pattern by predicting the number, purpose, and sequence of activities (SDT Day Pattern Choice Model). Next, for all tours in a day pattern, departure-from-home and arrival-back-home times are determined (SDT Tour Scheduling Model). A location (alpha zone) of the primary destination of a non-work tour is assigned in the SDT Primary Tour Destination Choice Model. The SDT Tour Mode Choice Model then chooses a primary mode to the entire tour. Next, the alpha zone location and the duration of intermediate stops are predicted in the SDT Intermediate Stop Location Model and the SDT Intermediate Stop Duration Model respectively. The SDT Work-Based Activity Duration Model then forecasts duration of the work-based sub-tour activities. Lastly, contingent on the tour mode, trip modes are chosen in the SDT Trip Model Choice Model. The SDT person trips are written out in the “Trips_SDTPerson.csv” file.
For the long-distance tours, first, the LDT Scheduling Model assigns departure time, arrival time, and duration to fully define tours’ schedule. Next, the LDT Internal-External Binary Choice Model predicts whether a tour’s destination will be within or beyond the bound of the model area. Tours’ destination zones are then selected in the LDT Destination Choice Model. Lastly, the LDT Mode Choice Model chooses trips modes. The LDT person trips are written out in the “Trips_LDTPerson.csv” file. The person trips out of the two PT components are assigned together to the model network in the Traffic Assignment (TA) and Transit Assignment (TR) modules.
The two PT components are described in more detail below.
The SDT first pre-calculates tour model choice and tour destination choice logsums as measures of travel accessibility. The tour model choice logsum represents travel cost between two zones and are calculated by activity purpose and household market segment (auto sufficiency and household income - see the table below). The destination choice logsum represents the overall ability of travelers to access any destination and are function of origin and destination attributes. The accessibilities are also input to the LDT component.
| Segment | Income (2009 dollars) | Auto Sufficiency |
|---|---|---|
| 0 | Low (<$37.4k) | autos=0 |
| 1 | Low (<$37.4k) | autos<workers |
| 2 | Low (<$37.4k) | autos>=workers |
| 3 | Medium ($37.4k-$74.8k) | autos=0 |
| 4 | Medium ($37.4k-$74.8k) | autos<workers |
| 5 | Medium ($37.4k-$74.8k) | autos>=workers |
| 6 | High ($74.8k+) | autos=0 |
| 7 | High ($74.8k+) | autos<workers |
| 8 | High ($74.8k+) | autos>=workers |
In the LDT component, the destination choice accessibilities are used in the Binary Choice Model to predict the probability of engaging in long-distance travel. The people who have access to many destinations within a short distance are less likely to make long-distance trips. In the SDT component, the accessibilities influence the Auto Ownership Model, the Day Pattern Model, the Tour Destination Choice Model, and the Tour Scheduling Model.
Since the Synthetic Population (SPG2) does not contain auto ownership information, the SDT Auto Ownership Model predicts the total number of vehicles owned by each household in the population. It is a discrete choice multinomial logit model applied to each resident household in the synthetic population. A household is assigned one of the following values:
| Value | Description |
|---|---|
| 0 | No autos |
| 1 | 1 auto |
| 2 | 2 auto |
| 3 | 3 or more autos |
The utility of an alternative includes following variables:
- household attributes (household size, employed person, household income)
- accessibilities (tour mode choice and destination choice logsums)
Once the auto ownership information is generated, the SDT Workplace Location Choice Model assigns a workplace location (alpha zone) to every worker in the synthetic population.
A workplace location is determined based on:
- Labor dollars flows by occupation between beta zones (output of AA)
- Quantity of labor produced and consumed in each alpha zones (output of AA)
- Mode choice logsum
First, based on the three variables above, the labor flows by occupation between beta zones are converted to flows between alpha zones. The resulting flows are then used to compute flow probabilities between alpha zones. Lastly, a Monte Carlo process selects a workplace location by sampling from these flow probabilities.
The model outputs a zonal summary of person’s employment by occupation in “Employment.csv”
For persons eligible for short-distance travel patterns (see LDT Binary Choice Model), this model generates a day pattern of activities by predicting the number, purpose, and sequence of activities for each person in the population. A day pattern consists of sequence of activities and a trip is required between each pair of activities in the pattern.
Following seven activity types are considered:
| Code | Description |
|---|---|
| H | Home |
| W | Work (including second job), without a work-based sub-tour |
| B | Work (including second job), with a work-based sub-tour |
| C | School |
| S | Shop |
| R | Social/recreation |
| O | Other (including pickup/drop-off) |
A day pattern is chosen from a choice set consisting of the unique day patterns observed (Ohio Home Interview Survey) for each person type. Five person-type categories are created based on age and student/worker status.
| Person Type | Description |
|---|---|
| Pre-school | All persons less than 6 years old |
| Grade/High School | All persons older than 5 and younger than 18 |
| Worker | All persons older 17 and workers but not students |
| College student | All persons older than 17 |
| Non-worker | All persons older than 17 and not students nor workers |
The unique day patterns are combination of number of tours, number of intermediate stops, and activity types. This results in a large number of choices (day patterns) in the choice set for each person-type. The model controls the size of the choice set by generalizing day patterns as below:
- One tour with intermediate stops and activity purpose
- Two tours with intermediate stops and no activity purpose
- Three or more tours with no intermediate stops and no activity purpose
Day patterns with two or more tours are reconstructed later by determining number of intermediate stops (for three or more tours only) and activity purpose.
Overall, a person day pattern is generated using three sub-models (discrete choice multinomial logit models):
- Generalized day pattern model
- Intermediate stop number choice model
- Intermediate stop purpose choice model
First, this model selects a generalized day pattern alternative from the reduced choice set. The model is a discrete choice multinomial logit model. The utility of an alternative is consists of:
- Activity attributes (number and purpose of activities; sequence of tours and/or activities; number and purpose of tours; number, purpose, and presence/absence of intermediate stops)
- Traveler attributes (person – age, and gender; household – household size, number of workers, auto ownership, income, and presence of young children)
- Transport attribute (home to work distance, and destination choice logsum)
Five generalized day pattern models are estimated, one for each person type.
The generalized day patterns with three or more tours from the previous model are assigned with the number of intermediate stops in this model. The model is a discrete choice multinomial logit model with a choice set of stops consisting of four alternatives: no stops, inbound, outbound, and both.
The choice model is applied by tour purpose and the number of stops are determined based on:
- Day pattern composition (i.e. number of tours, presence of tours by purpose)
- Person type (i.e. worker, presence of children)
- Household variables (i.e. auto ownership, income)
Five intermediate stop number choice models are estimated, one for each [tour purpose] with work and work-based are combined.
This model assigns an activity purpose to intermediate stops in each tour on the day patterns with two and more tours. The choice set of activity purpose consists of three alternatives: shop, recreation, and other.
The model calculates stop purpose probabilities based on the expanded Ohio Home Interview Survey data. A Monte Carlo simulation selects an activity purpose using the empirical distribution of activity purposes. The distributions are constructed based on:
- Person type
- Tour purpose
- Tour position (first, middle, or last)
- Stop position (inbound or outbound)
- Tour number (first or second)
After determining a generalized day pattern, this model calculates simultaneously departure-from-home time and arrival-back-home time for all home-based tours in the pattern. The departure and arrival times are selected in a time of day resolution of one hour with 11:00 PM – 5:00 AM period as a single choice. It is a discrete choice multinomial logit model and the choice set consists of 190 possible schedules (19 departure and 19 arrival hours) satisfying a constraint that the arrival time is always greater or equal to the departure time.
A time window (choice) availability is determined using tour purpose hierarchy and day-pattern sequence. The model first schedules work and school tours, followed by shop tours, then recreational tours, and finishing with other tours.
A tour schedule is determined based on:
- Tour and day pattern
- Traveler attributes
- Transport attributes (i.e. mode choice logsum for work)
This model selects the location (alpha zone) of the primary destination of a tour (non-work tours and work-based tours). It is a discrete choice multinomial logit model and applied by tour purpose. The choice set of destinations consists of all alpha-zones in the model system that are within 50 miles of home or work location.
A tour destination is selected based on:
- Travel distance
- Model choice logsum
- Employment by type
This model determines a primary travel mode to the entire tour. Available tour mode choice alternatives are:
| Tour Mode | Description |
|---|---|
| Auto Driver | Any trip on tour is auto driver, no trips are school bus |
| Auto Passenger | All trips on tour are passenger or non-motorized (including school bus for school tours) |
| Passenger–Transit | Any trip outbound is auto passenger and any trip inbound is walk transit |
| Transit–Passenger | Any trip outbound is walk transit and any trip inbound is auto passenger |
| Walk | Only walk trips on tour |
| Bike | Bike trips on tour, no motorized trips |
| Walk Access Transit | Walk transit trip on tour, no drive transit trips on tour. Includes tours with passenger and transit on same half tour |
| Drive Access Transit | Any trip on tour is drive transit |
The mode choice model is a discrete choice nested logit model and is applied by tour purpose and auto sufficiency. The nesting of the alternatives is as below:
For a given tour purpose and auto sufficiency, a tour mode is selected based on:
- Level-of-service of the mode (in-vehicle time, cost, wait time, access time)
- Tour pattern (number of stops)
- Traveler attributes (household size, income)
This model selects an alpha zone location for each intermediate stop on a tour. It is a discrete choice multinomial logit model and is applied by tour purpose. The choice set is defined by model accessibility. An intermediate stop located farther away from home and the primary destination are discouraged as the amount of out-of-direction travel is the additional travel time required to reach the intermediate stops using the tour’s primary mode. For transit tours, zones that are not reachable by transit are not considered as alternatives for stops.
An intermediate stop location is determined based on:
- Origin and primary destination of the tour
- Tour mode
- Characteristics of each alternative alpha zone location for the stop
This model predicts the duration of intermediate stops on tours. The intermediate stop duration model is a discrete choice multinomial logit model and is applied by tour purpose. The choice set includes a total of 12 possible activity duration of 1-hour resolution. The choice set is constrained by the total tour duration.
A stop duration is determined based on:
- Daily activity pattern (number of tours, tour schedule, number of stops)
- Traveler attribute (worker)
- Stop attribute (inbound or outbound, deviation distance)
This model selects the duration of the three activities that comprise a work-based sub-tour:
- First at-work activity
- Primary activity
- Last at-work activity
First, the duration of primary and first at-work activity are calculated using the following proportions, drawn (Monte Carlo sampling) from a set of empirical distribution functions obtained from the distribution of activity durations observed in the Oregon Home Interview Survey data:
- Proportion of the total tour duration spent at the primary activity
- Proportion of the total work activity spent at the first at-work activity.
Given that the duration of the tour is available, the duration of the two activities are calculated.
Then, the last at-work activity duration is calculated by subtracting the duration of the two activities from the total tour duration.
This model predicts mode for each trips on a tour. Available trip mode alternatives are:
| Trip Mode | Description |
|---|---|
| Drive-Alone | Single-occupant auto |
| Shared-Ride 2 | 2 person occupant auto |
| Shared-Ride 3+ | 3+ person occupant auto |
| Walk | Walk |
| Bicycle | Bicycle |
| Walk-Transit | Walk-Access Transit |
| Drive-Transit | Auto-Access Transit |
| School bus | School bus (not assigned to the network) |
The trip mode selection is constrained by tour mode, as below.
If the tour mode is auto driver, the available trip mode choices are: drive-alone, shared ride 2 and shred ride 3+. If the tour mode is auto passenger, the available trip mode choices are: shared ride 2, shared ride 3 and walk. If the tour mode is walk, all trips on the tour are walk trips. And so on.
Where the trip mode is not uniquely defined by the tour mode nor determined by the transit path builder, the mode is determined using multinomial discrete choice logit model.
This model predicts the probability of travelers to engage in long-distance travel. As long-distance travel is generally planned on time scale longer than one day, the LDT tours are generated over a two-week period. The model predicts the presence (not the quantity) of long-distance travel for each of the following three purposes:
| Purpose | Description |
|---|---|
| Household | Travel in which entire household participates |
| Work-related | Individual business travel |
| Other | Individual travel for non-work purposes |
An individual can have presence of long-distance travel for multiple purposes occurring during the two-week time window. The presence of long-distance travel is determined based on the linear utility for the binary choice of travel for each purpose. The utility is calculated based on:
- Household attributes (size, income, workers, autos, presence of students, single family home)
- Person attributes (worker occupation, student, sex, age)
- Accessibility (destination choice logsum)
Given the presence of travel (LDT Binary Choice Model), the LDT Day Pattern Choice Model determines the type of travel on the simulation day. The model draws a day pattern from the observed frequency (the Ohio Home Interview Survey data) of the following five travel patterns:
| Pattern | Description |
|---|---|
| Complete Tour | Entire tour is complete on the simulation day |
| Begin Tour | Tour departs on the simulation day |
| End Tour | Tour returns on the simulation day |
| Away | Person is out-of-town on the simulation day |
| No Tour | Travel occurs on a different day |
While deciding day pattern for a LDT travel purpose, the decision-making agent for household travel is household and the decision making agent for work-related and other travel is the person.
The LDT model predicts behavior on a typical weekday, Monday through Thursday and as more than one long-distance tours on a single day are rare, the model does not allow multiple long-distance tours on the simulation day.
This model schedules long-distance tours to a time-of-day with one-hour resolution. “Begin Tour” are assigned a departure time, “End Tour” are assigned an arrival time, and “Complete Tour” are assigned a departure and duration.
For begin and end tours, the model draws a schedule from observed frequency distributions constructed from the Ohio Home Interview Survey data. For the complete tours, the model determines a schedule using a constant-only logit model, with constants on departure time and duration.
This model predicts a binary choice of whether a tour will have a destination within or beyond the bounds of the model area. It is a binary choice model and applied by tour purpose. A choice of external destination is predicted based on:
- Household income
- Person attributes (occupation, worker binary, age)
- Complete travel in one day
- Auto travel time to external station
This model selects a destination alpha zone for a long-distance tour. It is a logit model and applied separately for internal and external destinations.
The utility of an internal destination choice is calculated using:
- Mode choice logsum
- Auto travel time (if complete travel in one day)
- Zone attributes (total households, total employment, hotel employment, higher education, government employment, employment in worker’s own industry)
- Distance
Due to unavailability of a national network and extensive external zones in the SWIM model, the external destination trips are assigned to external stations at the edge of the model area. An external station is selected using a simple logit destination choice model and the choice is based on:
- Highway travel time
- Total traffic volume at the external station
This model assigns a mode to long-distance trips. The mode choice models are applied by long-distance tour purpose (see LDT Binary Choice Model) and include four alternatives in the base year and two optional future year transit options, as below.
The mode for an internal long-distance trip is determined using a nested logit model and the choice is based on:
- In-vehicle time
- Access time
- Wait time
- Service frequency (Air)
- Travel cost by household income segment
External long-distance trips are applied with a simplified model choice model which applies fixed mode splits (Derived from ATS data) with increase in Air mode share as a function of distance and household income.
Cost coefficients in SDT were updated to be more consistent with prevailing literature on value-of-time for travel (for further information, see https://www.transportation.gov/sites/dot.gov/files/docs/Value%20of%20Travel%20Time%20Memorandum.pdf). The cost coefficients for work travel were calculated by assuming a value-of-time equal to one-half of the implied hourly wage for a one-worker household for each income group. The cost coefficients for non-work travel were calculated by assuming a value-of-time equal to one-third of the implied hourly wage for a one-worker household in each income group. The average income within each income group ( $16,590 for $0-$30k, $43,564 for $30-$60k, and $111,765 for $60k+) was calculated using the Public Use Microdata Sample for Oregon. The cost coefficients used in SDT were updated based on the values-of-times by the following formula:
Cost coefficient (utiles per dollar) = in-vehicle time coefficient (utiles per minute)/[value-of-time (dollars per hour) * 60 (minutes per hour)]
| Purpose Number | Purpose | $0-$37.4k | $37.4-$74.8k | $74.8k+ |
|---|---|---|---|---|
| 1 | Work | $3.99 | $10.47 | $26.87 |
| 2 | Work Based | $3.99 | $10.47 | $26.87 |
| 3 | Grade School | $3.99 | $10.47 | $26.87 |
| 4 | College | $3.99 | $10.47 | $26.87 |
| 5 | Shop | $2.66 | $6.98 | $17.91 |
| 6 | Recreate | $2.66 | $6.98 | $17.91 |
| 7 | Other | $2.66 | $6.98 | $17.91 |
To run the PT module, the user needs to perform two steps:
- Set up PT
- Run PT
The PT module is set up through the tsteps.csv (under “/[scenario_name]/model/config”) file by turning on the “PT” property in the desired year.
To enable the property, edit the tsteps.csv file to contain a ‘1’ in the PT column for each year where the user would like to run the PT. For example, following setup runs PT in the year 20 and year 23. The AA, SPG2, and TA & TR (in a previous year) need to be run before running the PT in each year.
To run the PT module, first run the “/[scenario name]/build_run.bat” script. The script creates all of the necessary output folders and configuration files, as well as the batch files “/[scenario name]/run_model.bat” and “/[scenario name]/run_model_python.bat”.
Next, run the “run_model.bat” to start a model run. Running either of the two batch files will start the model run. Both batch files have identical functionality, only one is purely in batch form and the other runs through a Python layer. The reason both exist is that the former is simpler, but the latter may be needed if certain use cases arise in the future.
Inputs to the PT module can be found in “scenario_name/inputs/glabalTemplate.properties”. Lists of model settings and input files to each PT component are provided below.
| Parameter | Value | Description |
|---|---|---|
| pt.sample.rate | 2 | Household sample rate (1 in x) |
| sdt.walk.mph | 3.0 | Walk speed (mph) |
| sdt.bike.mph | 12.0 | Bike speed (mph) |
| sdt.drive.transit.mph | 25.0 | Drive to transit speed (mph) |
| auto.operating.cost | 0.18 | Auto operating cost ($/mile) |
| sdt.first.wait.segment | 60 | minutes at which to segment wait time |
| pt.low.max.income | 37408 | Maximum income of low income category ($2009) |
| pt.med.high.max.income | 74814 | Maximum income of medium income category ($2009) |
| sdt.non.work.parking.cost.factor | 2.5 | Average hourly duration for non-work activities, used to scale hourly parking costs for tour mode choice logsum calculations |
| sdt.labor.flow.intrazonal.parameter | 0.0 | labor flow dispersion parameter and distance factors |
| sdt.labor.flow.dispersion.parameter | 0.54 | labor flow dispersion parameter |
| sdt.labor.flow.distance_0_5.parameter | 0.0 | labor flow distance (0 mile to 5 mile) factor |
| sdt.labor.flow.distance_5_15.parameter | 0.0 | labor flow distance (5 mile to 15 mile) factor |
| sdt.labor.flow.distance_15_30.parameter | 0.0 | labor flow distance (15 mile to 30 mile) factor |
| sdt.labor.flow.distance_30_50.parameter | 0.0 | labor flow distance (30 mile to 50 mile) factor |
| sdt.labor.flow.distance_50Plus.parameter | 0.0 | labor flow distance (>50 mile) factor |
| sdt.auto.ownership.distance.parameter | -0.01835 | Auto ownership distance parameter |
| sdt.auto.ownership.time.parameter | -0.025 | Auto ownership time parameter |
| sdt.max.block.size | 5000 | size of household block sent to each worker (changes depending on pop. size) |
| sdt.start.hour | 5 | Start hour for simulation |
| sdt.end.hour | 23 | End hour for simulation |
| pt.car.peak.skims | pkautotime, pkautodist, pkautotoll | TA previous year car travel cost peak skims: time, distance, and toll |
| pt.car.offpeak.skims | opautotime, opautodist, opautotoll | TA previous year car travel cost off-peak skims: time, distance, and toll |
| sdt.wt.peak.names | Ivt, Fwt, Twt, Brd, Far, Awk, Xwk, Ewk, Ovt | TR previous year walk-to transit peak travel cost matrix names: Ivt - in-vehicle travel time (minutes); Fwt - first wait time (minutes); Twt - transfer wait time (minutes); Brd – boardings (persons); Far - fare ($); Awk - access walk -- walk transit only; Xwk - transfer walk; Ewk - egress walk; Ovt – out-of vehicle time (minutes) |
| sdt.wt.peak.skims | pkwtivt, pkwtfwt, pkwttwt, pkwtbrd, pkwtfar, pkwtawk, pkwtxwk, pkwtewk, pkwltfovt | TR previous year -to transit peak travel cost matrices |
| sdt.wt.offpeak.names | Ivt, Fwt, Twt, Brd, Far, Awk, Xwk, Ewk, Ovt | TR previous year walk-to transit off-peak travel cost matrix names: Ivt - in-vehicle travel time (minutes); Fwt - first wait time (minutes); Twt - transfer wait time (minutes); Brd – boardings (persons); Far - fare ($); Awk - access walk -- walk transit only; Xwk - transfer walk; Ewk - egress walk; Ovt – out-of vehicle time (minutes) |
| sdt.wt.offpeak.skims | opwtivt, opwtfwt, opwttwt, opwtbrd, opwtfar, opwtawk, opwtxwk, opwtewk, opwltfovt | TR previous year walk-to transit off-peak travel cost matrices |
| File | Description |
|---|---|
| Inputs from Outputs ([scenario_name]/outputs) | |
| Employment.csv | AO alpha zone employment by industry |
| laborDollarProductionSum.csv | AA productions by alpha zone |
| laborDollarConsumptionSum.csv | AA consumptions by alpha zone |
| selling_*.zmx | AA labor flow beta matrices by SCGT group |
| SynPopH.csv | SPG2 household data |
| SynPopP.csv | SPG2 person data |
| SynPop_Taz_Summary.csv | SPG2 population summary by alpha zone |
| Model Inputs ([scenario_name]/inputs/parameters) | |
| AutoOwnershipParameters.csv | PT SDT parameters for Auto ownership model |
| firstStopDestinationParameters.csv | PT SDT parameters for 1st stop destination choice |
| secondStopDestinationParameters.csv | PT SDT parameters for 2nd stop destination choice |
| intermediateStopChoiceParameters.csv | PT SDT parameters for intermediate stop choice model |
| pctWorkBasedDuration.csv | PT SDT parameters for work based trip duration |
| patternAttributes.csv | PT SDT pattern attributes for pattern choice model |
| patternParameters.csv | PT SDT parameters for pattern choice model |
| stopdestinationparameters.csv | PT SDT parameters for Stop Destination Choice model |
| stopDurationParameters.csv | PT SDT parameters for Stop Duration Choice model |
| stopPurpose2tParameters.csv | PT SDT parameters for Stop Purpose Choice model-2t |
| stopPurpose3ptParameters.csv | PT SDT parameters for Stop Purpose Choice model-3pt |
| tourDestinationParameters.csv | PT SDT parameters for Tour Destination Choice model |
| tourmodeparameters.csv | PT SDT parameters for Tour Mode Choice model |
| tourScheduleParameters.csv | PT SDT parameters for Tour Schedule model |
| tripModeParameters.csv | PT SDT parameters for trip mode choice model |
| Parameter | Value | Description |
|---|---|---|
| pt.sample.rate | 2 | Household sample rate (1 in x) |
| ldt.threshold.distance.in.miles | 50.0 | distance at which travel becomes long-distance |
| ldt.rental.car.cost.cents.per.day | 71.6042234 | cost to rent a car, in dollars (cents in title is legacy). Multiplied by average days (ldt.average.duration.multi-day.trip.by.purpose) and used in auto mode utility |
| ldt.taxi.rate.per.minute.in.cents | 2.1481267 | cost of a taxi, in dollars per minute (cents in title is legacy). Used in air travel mode utility calculations, which assumes that 0-auto households would take a taxi between home and the airport |
| ldt.airport.parking.cost.in.cents | 16.3666796 | cost to park at the airport, in dollars per day (cents in title is legacy) Multiplied by average number of days (ldt.average.duration.multi-day.trip.by.purpose) and used in air travel mode utility calculations, which assumes that 1+ auto households would drive to the airport and park |
| ldt.average.duration.multi-day.trip.by.purpose | 2.4,4.6,2.6 | average duration of a multi-day trip, in days, by purpose |
| ldt.average.auto.occupancy.by.purpose | 2.81, 1.22, 1.91 | average auto occupancy, by purpose |
| ldt.airport.zones | 904, 1549, 1734, 2874, 3055, 2130, 2905 | Taz IDs with major airports |
| pt.air.peak.skims | pkdairivt, pkdairfar, pkdairfwt, pkdairdrv | Air mode peak travel cost matrices |
| pt.air.offpeak.skims | opdairivt, opdairfar, opdairfwt, opdairdrv | Air mode off-peak travel cost matrices |
| pt.icwt.peak.skims | pkwicrivt, pkwicrfar, pkwicrfwt, pkwicrtwt, pkwicrawk, pkwicrxwk, pkwicrewk | Intercity walk to transit peak travel cost matrices |
| pt.icwt.offpeak.skims | opwicrivt, opwicrfar, opwicrfwt, opwicrtwt, opwicrawk, opwicrxwk, opwicrewk | Intercity walk to transit off-peak travel cost matrices |
| File | Description |
|---|---|
| Model Inputs ([scenario_name]/inputs/parameters) | |
| LDExternalDestinationChoiceParameters.csv | PT LDT frequencies for External Destination choice model |
| LDExternalModeShare.csv | PT LDT parameters for External Mode choice model |
| LDInternalDestinationChoiceParameters.csv | PT LDT parameters for Internal Destination choice model |
| LDInternalExternalParameters.csv | PT LDT parameters for Internal External choice model |
| LDInternalModeChoiceParameters.csv | PT LDT parameters for Internal Mode choice model |
| LDPatternModelFrequencies.csv | PT LDT frequencies for Pattern model |
| LDTourBinaryChoiceParameters.csv | PT LDT parameters for binary travel choice model (LDT vs. SDT) |
| LDTourScheduleFrequencies.csv | PT LDT frequencies for Tour Schedule model |
| LDTourScheduleParameters.csv | PT LDT parameters for Tour Schedule model |
| ExternalStationVolumes.csv | Volumes at external stations |
Outputs from the PT module can be found in “scenario_name/inputs/glabalTemplate.properties”. The outputs are stored in “scenario_name]/outputs”. A list of outputs files from each PT component is provided below.
| File | Description |
|---|---|
| Trips_SDTPerson.csv | PT SDT Person Trips and attributes |
| Tours_SDT.csv | PT SDT Person Tours and attributes |
| Patterns_SDT.csv | PT SDT Activity patterns and attributes |
| sdtTODTrips.csv | Table listing SDT VMT by trip start time period |
| householdData.csv | PT summary of household SynPopH data |
| personData.csv | PT summary of person SynPopP data |
| Employment.csv | PT SDT Alpha zone employment by industry summary |
| workPlaceLocations.csv | |
| dcLogsums.csv | PT SDT destination choice logsums between Alpha Zones for market segment (purpose, HHincome, auto sufficiency) |
| *mcls.zmx | PT SDT mode choice logsums between Alpha Zones for market segment (purpose, HHincome, auto sufficiency) |
| *mcls_beta.zmx | PT SDT mode choice logsums between Beta Zones for market segment (purpose, HHincome, auto sufficiency): b4, b5, b8, c4, o4, s4, w1, w4, w7, w8 |
The above outputs are described in more detail below:
| Field | Description |
|---|---|
| hhID | Household ID |
| memberID | Member ID |
| weekdayTour(yes/no) | Is weekday tour (1-yes, 0-no) |
| tour# | Tour number |
| subTour(yes/no) | Is subtour (1-yes, 0-no) |
| tourPurpose | Tour purpose |
| tourSegment | Segment number within tour |
| tourMode | Tour mode |
| origin | Trip origin |
| destination | Trip destination |
| time | Trip time |
| distance | Trip distance |
| tripStartTime | Trip start time |
| tripEndTime | Trip end time |
| tripPurpose | Trip purpose |
| tripMode | Trip mode |
| income | Income |
| age | Person age |
| enroll | School/college enrollment status |
| esr | Employment status |
| Field | Description |
|---|---|
| hhID | Household ID |
| memberID | Member ID |
| personAge | Person Age |
| weekdayTour(yes/no) | Is weekday tour (1-yes, 0-no) |
| initialTourString | Initial chain of activity purposes in tour (ex. HBH, HSCH, HSWSH) |
| completedTourString | Complete chain of activity purposes in tour (ex. HBH, HSCH, HSWSH) |
| tour# | Tour Number |
| departDist | |
| activityPurpose | Tour primary activity purpose (Home or Work) |
| startTime | Tour Start time |
| endTime | Tour End Time |
| timeToActivity | Travel time to activity |
| distanceToActivity | Distance to activity |
| tripMode | Trip mode? |
| location | Location |
| activityPurpose.1 | First activity purpose |
| startTime.1 | Start time |
| endTime.1 | End time |
| timeToActivity.1 | Travel time to first activity |
| distanceToActivity.1 | Distance to first activity |
| tripMode.1 | Trip mode |
| location.1 | Activity location zone |
| activityPurpose.2 | Second activity purpose |
| startTime.2 | Start time |
| endTime.2 | End time |
| timeToActivity.2 | Travel time to second activity |
| distanceToActivity.2 | Distance to second activity |
| tripMode.2 | Trip mode |
| location.2 | Activity location zone |
| activityPurpose.3 | Third activity purpose |
| startTime.3 | Start time |
| endTime.3 | End time |
| timeToActivity.3 | Travel time to third activity |
| distanceToActivity.3 | Distance to third activity |
| tripMode.3 | Trip mode |
| location.3 | Activity location zone |
| activityPurpose.4 | Fourth activity purpose |
| startTime.4 | Start time |
| endTime.4 | End time |
| timeToActivity.4 | Travel time to fourth activity |
| distanceToActivity.4 | Distance to fourth activity |
| tripMode.4 | Trip mode |
| location.4 | Activity location zone |
| primaryMode | Primary tour mode |
| Field | Description |
|---|---|
| hhID | Household ID |
| memberID | Member ID |
| personAge | Person age |
| dayPatternLogsum | Day pattern logsum value |
| dayPattern | Person day pattern - activity chain (ex. HBH, HSCH, HSWSH) |
| nWeekdayTours | Number of weekday tours |
| nWorkTours | Number of work tours |
| nSchoolTours | Number of school tours |
| nShopTours | Number of shopping tours |
| nRecreateTours | Number of recreation tours |
| nOtherTours | Number of other tours |
| Field | Description |
|---|---|
| TIME | Trip start time |
| VMT | total vehicle miles traveled |
| Field | Description |
|---|---|
| HH_ID | Household ID |
| TAZ | Alpha zone |
| PERSONS | Number of persons |
| SINGLE_FAMILY | Is single family (1-yes, 0-no) |
| AUTOS | Number of autos owned |
| HINC | Household income |
| LD_HOUSEHOLD_TOUR | Is long distance household travel tour? (1-yes, 0-no) |
| LD_HOUSEHOLD_PATTERN | Long distance household travel day pattern (0-No Tour, 1-Complete Tour, 2-Begin Tour, 3-End Tour, 4-Away) |
| Field | Description |
|---|---|
| HH_ID | Household ID |
| memberID | Member ID |
| home_taz | Home location alpha zone |
| SEX | Gender (1-male, 2-female) |
| AGE | Person age |
| ENROLL | Enrolled in school? (1-No or 3-Yes |
| ESR | Employment status (0-not employed, 1-employed) |
| SW_SPLIT_IND | SWIM2 split industry - based on census occupation and industry (0-52) |
| SW_OCCUP | SWIM2 occupation (NONE, HEALTH, MANAGER, NON_OFFICE, PROFESSIONAL, RETAIL, OTHER_ED, OTHER) |
| WORK_TAZ | Work location alpha zone |
| LD_INDICATOR_HOUSEHOLD | Is long distance travel purpose Household? (1-yes, 0-no) |
| LD_INDICATOR_WORKRELATED | Is long distance travel purpose Work-related? (1-yes, 0-no) |
| LD_INDICATOR_OTHER | Is long distance travel purpose Other? (1-yes, 0-no) |
| LD_TOUR_PATTERN_HOUSEHOLD | long distance travel day pattern if travel purpose is Household (0-No Tour, 1-Complete Tour, 2-Begin Tour, 3-End Tour, 4-Away) |
| LD_TOUR_PATTERN_WORKRELATED | long distance travel day pattern if travel purpose is Work-Related (0-No Tour, 1-Complete Tour, 2-Begin Tour, 3-End Tour, 4-Away) |
| LD_TOUR_PATTERN_OTHER | long distance travel day pattern if travel purpose is Other (0-No Tour, 1-Complete Tour, 2-Begin Tour, 3-End Tour, 4-Away) |
| generalPattern | General day pattern - activity chain (ex. HSWSH) |
| completePattern | Complete day pattern - activity chain (ex. HswsH) |
| nWeekdayTours | Number of weekday tours |
| nWorkTours | Number of work tours |
| nSchoolTours | Number of school tours |
| nShopTours | Number of shopping tours |
| nRecreateTours | Number of recreation tours |
| nOtherTours | Number of other tours |
| Field | Description |
|---|---|
| Azone | Alpha zone |
| MFG_wdppr_hi | |
| MFG_hvtw_li | |
| WHSL_offc_off | |
| HLTH_hosp_hosp | |
| SERV_tech_off | |
| CNST_othr_xxx | |
| HLTH_othr_off_li | |
| SERV_nonp_off_inst | |
| MFG_hvtw_hi | |
| UTL_othr_off | |
| SERV_home_xxx | |
| CNST_nres_xxx | |
| no_industry | |
| CNST_main_xxx | |
| INFO_info_off_li | |
| HOSP_acc_acc | |
| CNST_offc_off | |
| FIRE_real_off | |
| MFG_lvtw_hi | |
| UTL_othr_off_li | |
| INFO_info_off | |
| RES_offc_off | |
| K12_k12_off | |
| SERV_bus_off | |
| MFG_htec_li | |
| ENT_ent_ret | |
| RET_auto_ret | |
| CNST_res_xxx | |
| GOV_admn_gov | |
| WHSL_whsl_ware | |
| MFG_htec_hi | |
| HLTH_care_inst | |
| RET_stor_off | |
| TRNS_trns_ware | |
| HIED_hied_off_inst | |
| RET_stor_ret | |
| ENGY_offc_off | |
| SERV_stor_ret | |
| MFG_food_li | |
| TRNS_trns_off | |
| RES_forst_log | |
| SERV_site_li | |
| ENGY_elec_hi | |
| RES_agmin_ag | |
| ENGY_ptrl_hi | |
| MFG_food_hi | |
| GOV_offc_off | |
| K12_k12_k12 | |
| HOSP_eat_ret_acc | |
| ENGY_ngas_hi | |
| RET_nstor_off | |
| FIRE_fnin_off | |
| MFG_offc_off | |
| Total |
| Field | Description |
|---|---|
| Azone | Alpha zone |
| GRADESCHOOL0 | Grade School logsums, Low (<$30k) income, autos=0 |
| GRADESCHOOL1 | Grade School logsums, Low (<$30k) income, autos<workers |
| GRADESCHOOL2 | Grade School logsums, Low (<$30k) income, autos>=workers |
| GRADESCHOOL3 | Grade School logsums, Medium ($30k-$60k) income, autos=0 |
| GRADESCHOOL4 | Grade School logsums, Medium ($30k-$60k) income, autos<workers |
| GRADESCHOOL5 | Grade School logsums, Medium ($30k-$60k) income, autos>=workers |
| GRADESCHOOL6 | Grade School logsums, High ($60k+) income, autos=0 |
| GRADESCHOOL7 | Grade School logsums, High ($60k+) income, autos<workers |
| GRADESCHOOL8 | Grade School logsums, High ($60k+) income, autos>=workers |
| COLLEGE0 | College logsums, Low (<$30k) income, autos=0 |
| COLLEGE1 | College logsums, Low (<$30k) income, autos<workers |
| COLLEGE2 | College logsums, Low (<$30k) income, autos>=workers |
| COLLEGE3 | College logsums, Medium ($30k-$60k) income, autos=0 |
| COLLEGE4 | College logsums, Medium ($30k-$60k) income, autos<workers |
| COLLEGE5 | College logsums, Medium ($30k-$60k) income, autos>=workers |
| COLLEGE6 | College logsums, High ($60k+) income, autos=0 |
| COLLEGE7 | College logsums, High ($60k+) income, autos<workers |
| COLLEGE8 | College logsums, High ($60k+) income, autos>=workers |
| SHOP0 | Shop logsums, Low (<$30k) income, autos=0 |
| SHOP1 | Shop logsums, Low (<$30k) income, autos<workers |
| SHOP2 | Shop logsums, Low (<$30k) income, autos>=workers |
| SHOP3 | Shop logsums, Medium ($30k-$60k) income, autos=0 |
| SHOP4 | Shop logsums, Medium ($30k-$60k) income, autos<workers |
| SHOP5 | Shop logsums, Medium ($30k-$60k) income, autos>=workers |
| SHOP6 | Shop logsums, High ($60k+) income, autos=0 |
| SHOP7 | Shop logsums, High ($60k+) income, autos<workers |
| SHOP8 | Shop logsums, High ($60k+) income, autos>=workers |
| RECREATE0 | Recreate logsums, Low (<$30k) income, autos=0 |
| RECREATE1 | Recreate logsums, Low (<$30k) income, autos<workers |
| RECREATE2 | Recreate logsums, Low (<$30k) income, autos>=workers |
| RECREATE3 | Recreate logsums, Medium ($30k-$60k) income, autos=0 |
| RECREATE4 | Recreate logsums, Medium ($30k-$60k) income, autos<workers |
| RECREATE5 | Recreate logsums, Medium ($30k-$60k) income, autos>=workers |
| RECREATE6 | Recreate logsums, High ($60k+) income, autos=0 |
| RECREATE7 | Recreate logsums, High ($60k+) income, autos<workers |
| RECREATE8 | Recreate logsums, High ($60k+) income, autos>=workers |
| OTHER0 | Other logsums, Low (<$30k) income, autos=0 |
| OTHER1 | Other logsums, Low (<$30k) income, autos<workers |
| OTHER2 | Other logsums, Low (<$30k) income, autos>=workers |
| OTHER3 | Other logsums, Medium ($30k-$60k) income, autos=0 |
| OTHER4 | Other logsums, Medium ($30k-$60k) income, autos<workers |
| OTHER5 | Other logsums, Medium ($30k-$60k) income, autos>=workers |
| OTHER6 | Other logsums, High ($60k+) income, autos=0 |
| OTHER7 | Other logsums, High ($60k+) income, autos<workers |
| OTHER8 | Other logsums, High ($60k+) income, autos>=workers |
| WORK_BASED0 | Work-based logsums, Low (<$30k) income, autos=0 |
| WORK_BASED1 | Work-based logsums, Low (<$30k) income, autos<workers |
| WORK_BASED2 | Work-based logsums, Low (<$30k) income, autos>=workers |
| WORK_BASED3 | Work-based logsums, Medium ($30k-$60k) income, autos=0 |
| WORK_BASED4 | Work-based logsums, Medium ($30k-$60k) income, autos<workers |
| WORK_BASED5 | Work-based logsums, Medium ($30k-$60k) income, autos>=workers |
| WORK_BASED6 | Work-based logsums, High ($60k+) income, autos=0 |
| WORK_BASED7 | Work-based logsums, High ($60k+) income, autos<workers |
| WORK_BASED8 | Work-based logsums, High ($60k+) income, autos>=workers |
| File | Description |
|---|---|
| Tours_LDT.csv | PT LDT Person Tours and attributes |
| Trips_LDTPerson.csv | PT LDT Person Trips by mode |
| Trips_LDTVehicle.csv | PT LDT Vehicle Trips and attributes |
The above outputs are described in more detail below:
| Field | Description |
|---|---|
| hhID | Household ID |
| memberID | Member ID |
| tourID | Tour ID |
| income | Household income |
| tourPurpose | Tour purpose (HOUSEHOLD, WORKRELATED, OTHER) |
| tourMode | Tour mode (AUTO, TRANSIT_WALK, AIR) |
| patternType | Day pattern type (COMPLETE_TOUR, BEGIN_TOUR, END_TOUR) |
| destinationType | Tour destination type (INTERNAL or EXTERNAL) |
| home | Home location alpha zone |
| destination | Tour destination alpha zone |
| distance | Tour distance |
| outboundTravelTime | Outbound trip travel time |
| inboundTravelTime | Inbound trip travel time |
| departureTime | Tour departure time (mins from midnight) |
| arrivalTime | Tour arrival time (mins from midnight) |
| durationTime | Tour duration |
| partySize | Number of people involved in the tour |
| tripMode | Trip mode (DA, SR2, SR3P, TRANSIT_WALK, AIR) |
| Field | Description |
|---|---|
| hhID | Household ID |
| memberID | Member ID |
| tourID | Tour ID |
| income | Household income |
| tourPurpose | Tour purpose (HOUSEHOLD, WORKRELATED, OTHER) |
| tourMode | Tour mode (AUTO, TRANSIT_WALK, AIR) |
| origin | Trip origin alpha zone |
| destination | Trip destination alpha zone |
| distance | Trip distance |
| time | Trip time |
| tripStartTime | Trip start time |
| tripPurpose | Trip purpose (HOUSEHOLD, WORKRELATED, OTHER) |
| tripMode | Trip mode (DA, SR2, SR3P, TRANSIT_WALK, AIR) |
| vehicleTrip | Is vehicle trip? (TRUE or FALSE) |
| Field | Description |
|---|---|
| hhID | Household ID |
| memberID | Member ID |
| tourID | Tour ID |
| income | Household Income |
| tourPurpose | Tour purpose (HOUSEHOLD, WORKRELATED, OTHER) |
| tourMode | Tour mode (AUTO, TRANSIT_WALK, AIR) |
| origin | Trip origin alpha zone |
| destination | Trip destination alpha zone |
| distance | Trip distance |
| time | Trip time |
| tripStartTime | Trip start time |
| tripPurpose | Trip purpose (HOUSEHOLD, WORKRELATED, OTHER) |
| tripMode | Trip mode (DA, SR2, SR3P, TRANSIT_WALK, AIR) |
| vehicleTrip | Is vehicle trip? (TRUE or FALSE) |