CTRAMP Output Files

TM2 produces the following tour and trip microsimulation trip lists for both individual and joint travel. The files output are listed below. The modes are defined in Tour and Trip Modes below.

Important Note: It is important to consider occupancy when reviewing these tables. The model generates person trips. A mode is chosen for each person trip, for personal vehicle travel this could be SOV, HOV2, or HOV3+. At an aggregate level, 100 HOV2 person trips must result in 50 vehicle trips. While this is true at an aggregate level, it may not be possible to pair up all HOV2 person trips produced in the model. But the model produces enough HOV2 person trips to match the target mode share. When reviewing the joint table remember that these records include all travelers. So only one row per trip, as opposed to one row per person or traveler.

Main Output Files

• Accessibilities - ctramp_output/accessibilities.csv
• PreAutoOwnership - ctramp_output/aoResults_pre.csv
• AutoOwnership - ctramp_output/aoResults.csv
• Household Data - ctramp_output/householdData_[iteration].csv
• Person Data - ctramp_output/personData_[iteration].csv
• Work and School Location Choice - ctramp_output/wsLocResults_[iteration].csv
• Shadow Pricing - ctramp_output/ShadowPricingOutput_[work/school]_0.csv
• Individual Tours - ctramp_output/indivTourData_[iteration].csv
• Individual Trips - ctramp_output/indivTripData_[iteration].csv
• Joint Tours – ctramp_output/jointTourData_[iteration].csv
• Joint Trips - ctramp_output/jointTripData_[iteration].csv
• Resimulated Transit Trips - ctramp_output/indivTripDataResim_[iteration]_[inner_iteration].csv
• Unconstrained Parking Demand - ctramp_output/unconstrainedPNRDemand_[iteration]0.csv
• Constrained Parking Demand - ctramp_output/constrainedPNRDemand_[iteration]1.csv

Core Output Files

Individual Tours File (indivTourData_[iteration].csv)

Contains detailed information about individual (non-joint) tours generated by the model. Tours represent round-trip travel from home to a primary destination and back.

Key Fields:

Field	Description
hh_id	Household ID
person_id	Person ID
person_num	Person number in household
person_type	Person type classification
tour_id	Unique tour identifier
tour_category	Tour category: INDIVIDUAL_NON_MANDATORY, MANDATORY, AT_WORK
tour_purpose	Tour purpose: Work, School, University, Shop, Maintenance, Eating Out, Visiting, Discretionary, Escort, Work-Based
orig_mgra	Origin MAZ (Micro Analysis Zone)
dest_mgra	Destination MAZ
start_period	Tour start period (30-min periods from 3:00 AM, see Time Period Codes)
end_period	Tour end period (30-min periods from 3:00 AM, see Time Period Codes)
tour_mode	Tour mode (see Tour and Trip Mode Codes)
tour_distance	Tour distance in miles
tour_time	Tour time in minutes
atWork_freq	At-work stop frequency
num_ob_stops	Number of outbound stops
num_ib_stops	Number of inbound stops
out_btap	Outbound boarding TAP (Transit Access Point)
out_atap	Outbound alighting TAP
in_btap	Inbound boarding TAP
in_atap	Inbound alighting TAP
out_set	Outbound transit set
in_set	Inbound transit set
sampleRate	Iteration sample rate
dcLogsum	Destination choice logsum
util_[1-17]	Utility for each tour mode
prob_[1-17]	Probabilities for each tour mode

Individual Trips File (indivTripData_[iteration].csv)

Detailed trip-level information for individual travel. Trips are the building blocks of tours, including intermediate stops.

Key Fields:

• All tour-level identification fields (hh_id, person_id, tour_id)
• trip_id: Unique trip identifier
• stop_id: Stop sequence number within tour
• inbound: Direction indicator (0=outbound, 1=inbound)
• trip_purpose: Specific trip purpose
• orig_purpose: Origin activity purpose
• dest_purpose: Destination activity purpose
• orig_mgra, dest_mgra: Origin/destination MAZ
• trip_mode: Transportation mode for this trip
• trip_period: Trip departure time period
• trip_distance: Trip distance in miles
• trip_time: Trip time in minutes
• parking_mgra: Parking location MAZ (for auto modes)

Joint Tour Data (jointTourData_[iteration].csv)

Contains information about joint household tours where multiple household members travel together. Uses similar structure to individual tours but represents the entire travel party.

Special Considerations:

• One record represents the entire travel group
• All participating household members are identified
• Represents coordinated household travel decisions
• Important for understanding family travel patterns

Joint Trip Data (jointTripData_[iteration].csv)

Trip-level data for joint household travel. Important: These records include all travelers in the travel party, so there is only one row per trip (not per person), unlike individual trip files.

Household and Person Model Results

Household Data (householdData_[iteration].csv)

Comprehensive household-level model results capturing key household characteristics and choice model outcomes.

Key Model Results:

• hh_id: Household identifier
• home_mgra: Home location MAZ
• income: Household income category
• hhsize: Household size
• workers: Number of workers
• autos: Number of household vehicles
• age_of_head: Age of household head
• transponder: Transponder ownership model result
• cdap_pattern: Coordinated Daily Activity Pattern (CDAP) model result
• jtf_choice: Joint tour frequency model result

CDAP Patterns: Represent coordinated daily activity patterns for all household members:

• M: Mandatory activity (work/school)
• N: Non-mandatory activity only
• H: Home-based (no out-of-home activity)

Person Data (personData_[iteration].csv)

Person-level model outputs capturing individual characteristics and choice outcomes.

Key Model Results:

• hh_id, person_id: Household and person identifiers
• person_num: Person number within household
• age: Person age
• gender: Person gender
• type: Person type (worker, student, etc.)
• value_of_time: Individual value of time ($/hour)
• transitSubsidy_choice: Transit subsidy usage choice (0/1)
• transitSubsidy_percent: Transit subsidy discount percentage
• transitPass_choice: Transit pass usage choice (0/1)
• preTelecommuteCdap: Individual CDAP results before considering telecommuting
• telecommute: Telecommute choice (-1: non-workers, 1: Does not telecommute, 2: Telecommutes)
• cdap: Individual CDAP result (M/N/H)
• imf_choice: Individual mandatory tour frequency choice
• inmf_choice: Individual non-mandatory tour frequency choice
• fp_choice: Free parking availability choice
• reimb_pct: Parking reimbursement percentage
• workDCLogsum: Work destination choice logsum
• schoolDCLogsum: School destination choice logsum

Accessibility and Location Choice Results

Accessibilities (accessibilities.csv)

Comprehensive accessibility measures by micro-zone including: - Employment accessibility by industry sector - Population accessibility by demographic group - Transportation system accessibility measures - Relative accessibility compared to regional average

Auto Ownership Results

Two-Stage Process:

• Pre-Location Choice: aoResults_pre.csv - Auto ownership before work/school location choice
• Post-Location Choice: aoResults.csv - Final auto ownership after location choice adjustments

Key Fields:

• Household ID and characteristics
• Auto ownership level (0, 1, 2, 3+ vehicles)
• Ownership probabilities
• Utility values for each ownership level

Work and School Location Choice (wsLocResults_[iteration].csv)

Detailed results from the work and school location choice models.

Employment Categories:

• 1: Employed full-time
• 2: Employed part-time
• 3: Not employed
• 4: Under age 16

Student Categories:

• 1: Student in grade or high school
• 2: Student in college or higher
• 3: Not a student

Work Segments:

• -1: Not a worker
• 0: Management, business, science, and arts
• 1: White collar (office and administrative)
• 2: Blue collar (natural resources, construction, maintenance)
• 3: Sales and related occupations
• 4: Natural resources, construction, and maintenance
• 5: Production, transportation, and material moving
• 99999: Works from home

School Segments:

• -1: Non-student
• 0: Pre-schooler
• 1-8: Elementary/middle school districts
• 9-16: High school districts
• 17-18: University students (typical and non-typical)
• 88888: Home-schooled

Location Results:

• WorkLocation: Work location MAZ
• WorkLocationDistance: Distance to work (miles)
• WorkLocationLogsum: Work location choice logsum
• SchoolLocation: School location MAZ
• SchoolLocationDistance: Distance to school (miles)
• SchoolLocationLogsum: School location choice logsum

Shadow Pricing Results (ShadowPricingOutput_[work/school]_0.csv)

Shadow price adjustments by microzone and market segment to ensure model convergence between trip productions and attractions.

Transit-Specific Outputs

Resimulated Transit Trips (indivTripDataResim_[iteration]_[inner_iteration].csv)

Transit trips that have been resimulated for capacity constraint modeling and crowding effects.

Purpose:

• Account for transit vehicle capacity constraints
• Incorporate crowding and comfort effects
• Update path choices based on congested transit conditions
• Model passenger loading and transit reliability

Additional Fields:

• Transit access and egress information
• Capacity utilization indicators
• Crowding penalty factors
• Alternative route information

Park & Ride Demand Files

Unconstrained PNR Demand (unconstrainedPNRDemand_[iteration]0.csv)

• Park-and-ride demand without parking capacity constraints
• Shows latent demand for P&R facilities

Constrained PNR Demand (constrainedPNRDemand_[iteration]1.csv)

• Park-and-ride demand after applying parking capacity limits
• Shows actual utilization given supply constraints
• Fields include TAP ID, time period, demand, capacity, utilization rate

Mode and Time Period Reference

Tour and Trip Mode Codes

Code	Mode	Description
1	DRIVEALONEFREE	Drive alone, non-toll
2	DRIVEALONEPAY	Drive alone, toll
3	SHARED2GP	Shared ride 2 person, general purpose lanes (non-toll)
4	SHARED2HOV	Shared ride 2 person, HOV-eligible (non-toll)
5	SHARED2PAY	Shared ride 2 person, toll (HOV and toll eligible)
6	SHARED3GP	Shared ride 3+ person, general purpose lanes (non-toll)
7	SHARED3HOV	Shared ride 3+ person, HOV-eligible (non-toll)
8	SHARED3PAY	Shared ride 3+ person, toll (HOV and toll eligible)
9	WALK	Walk
10	BIKE	Bicycle
11	WALK_SET	Walk to transit
12	PNR_SET	Drive to transit (Park & Ride)
13	KNR_PERS	Kiss & Ride (personal vehicle drop-off to transit)
14	KNR_TNC	Kiss & Ride (TNC drop-off to transit)
15	TAXI	Taxi
16	TNC	Transportation Network Company (Uber/Lyft)

Time Period Codes

CT-RAMP uses 40 time periods of 30 minutes each, starting at 3:00 AM:

Period	Time Range	Period	Time Range
1	03:00-05:00 AM	21	02:30-03:00 PM
2	05:00-05:30 AM	22	03:00-03:30 PM
3	05:30-06:00 AM	23	03:30-04:00 PM
4	06:00-06:30 AM	24	04:00-04:30 PM
5	06:30-07:00 AM	25	04:30-05:00 PM
6	07:00-07:30 AM	26	05:00-05:30 PM
7	07:30-08:00 AM	27	05:30-06:00 PM
8	08:00-08:30 AM	28	06:00-06:30 PM
9	08:30-09:00 AM	29	06:30-07:00 PM
10	09:00-09:30 AM	30	07:00-07:30 PM
11	09:30-10:00 AM	31	07:30-08:00 PM
12	10:00-10:30 AM	32	08:00-08:30 PM
13	10:30-11:00 AM	33	08:30-09:00 PM
14	11:00-11:30 AM	34	09:00-09:30 PM
15	11:30-12:00 PM	35	09:30-10:00 PM
16	12:00-12:30 PM	36	10:00-10:30 PM
17	12:30-01:00 PM	37	10:30-11:00 PM
18	01:00-01:30 PM	38	11:00-11:30 PM
19	01:30-02:00 PM	39	11:30-12:00 AM
20	02:00-02:30 PM	40	12:00-03:00 AM

Five-Period Aggregation:

• EA (Early AM): Periods 1-4 (3:00-6:00 AM)
• AM (AM Peak): Periods 5-10 (6:00-9:00 AM)
• MD (Midday): Periods 11-22 (9:00 AM-3:00 PM)
• PM (PM Peak): Periods 23-28 (3:00-6:30 PM)
• EV (Evening): Periods 29-40 (6:30 PM-3:00 AM)

Tour Purpose Hierarchy

Mandatory Tours (highest priority):

• Work: Work-based tours
• School: K-12 school tours
• University: Higher education tours

Non-Mandatory Tours:

• Escort: Escorting other household members
• Shop: Shopping and errands
• Maintenance: Personal business, medical, etc.
• Eating Out: Restaurant meals
• Visiting: Social/recreational visits
• Discretionary: Other discretionary activities

At-Work Tours:

• Work-Based: Tours originating from workplace during work hours

Trip Destination Purposes

The following destination purposes are found in the individual trip files (dest_purpose field), showing the activity at trip destinations:

Purpose	Description	Typical Share
Home	Return to home location	~33%
Maintenance	Personal business, medical appointments, banking, etc.	~11%
Escort	Dropping off/picking up household members	~10%
Work	Primary work location	~10%
Shop	Shopping for goods and services	~9%
Discretionary	Recreation, entertainment, personal activities	~7%
School	K-12 educational activities	~5%
Eating Out	Restaurant meals and food services	~5%
Visiting	Social visits, family/friend gatherings	~5%
Work-Based	Work-related activities during work hours	~1%
University	Higher education activities	~1%
work related	Other work-related activities	<1%

Notes:

• Home is the most common destination as most tours are round-trips
• Personal activities (Maintenance, Shop, Discretionary, Eating Out) represent major trip attractors
• Work-related destinations include both primary work locations and work-based activities
• Escort trips reflect household coordination and dependency relationships
• Percentages are approximate and vary by scenario and geographic area

Model Integration and Validation

Quality Control Fields

Logsum Values:

• Destination choice logsums measure accessibility
• Mode choice logsums measure level-of-service
• Higher values indicate better options/accessibility

Utility and Probability Values:

• util_[1-17]: Raw utility values for each mode alternative
• prob_[1-17]: Choice probabilities (should sum to 1.0)
• Used for model validation and sensitivity analysis

Sample Rate Fields:

• Different sampling rates used across model iterations
• Early iterations may use 10-50% samples for computational efficiency
• Final iteration typically uses 100% sample

Distance and Time Consistency

Output files include multiple distance/time calculations for validation:

• Tour-level: Aggregated metrics for entire tours
• Trip-level: Detailed measurements for individual trip segments
• Skim-based: Values from network level-of-service matrices
• Modeled: Values from actual path assignments

Data Quality Checks

Required Validations:

1. Population Consistency: Person/household counts match synthetic population
2. Trip Rate Validation: Compare against observed survey data
3. Mode Share Accuracy: Validate against census and local survey benchmarks
4. Temporal Distribution: Verify peak spreading and time-of-day patterns
5. Spatial Patterns: Check geographic trip distribution and length
6. Occupancy Ratios: Verify shared ride occupancy factors
7. Transit Ridership: Compare against transit operator data

File Naming and Management

Naming Conventions

• [iteration]: Model global iteration number (typically 1-4)
• [inner_iteration]: Inner feedback iteration for capacity constraints
• _pre: Files created before work/school location choice
• _0: Indicates base scenario or initial iteration

File Management

• Backup Strategy: Keep previous iteration files for comparison
• Storage Requirements: Full population files can exceed 1GB each
• Compression: Consider compression for long-term storage
• Sampling: Use sampling techniques for large-scale analysis

Advanced Usage Guidelines

Post-Processing Workflows

Trip-based Analysis:

1. Use individual trip files for detailed routing analysis
2. Apply expansion factors for sampled iterations
3. Aggregate trips to desired geographic/temporal resolution

Tour-based Analysis:

1. Use tour files for activity pattern analysis
2. Calculate tour-level statistics (duration, complexity, mode consistency)
3. Analyze tour generation rates by person/household characteristics

Mode Choice Analysis:

1. Use probability fields for elasticity analysis
2. Calculate mode-specific accessibility measures
3. Analyze choice model performance via utility functions

Transit Analysis:

1. Combine individual and resimulated transit trip files
2. Use TAP-level data for capacity utilization analysis
3. Analyze park-and-ride demand patterns

Performance Monitoring

Convergence Indicators:

• Shadow pricing convergence by zone and segment
• Mode share stability across iterations
• Trip length distribution consistency
• Accessibility measure stability

Computational Efficiency:

• Monitor sample rates and processing times
• Track memory usage for large populations
• Optimize file I/O for repeated analysis

Tour and Trip Modes Codes

Code	Mode	Description
1	DRIVEALONEFREE	Drive alone, non-toll
2	DRIVEALONEPAY	Drive alone, toll
3	SHARED2GP	Shared ride 2 person, general purpose lanes (non-toll, non-HOV)
4	SHARED2HOV	Shared ride 2 person, HOV-eligible (non-toll)
5	SHARED2PAY	Shared ride 2 person, toll (HOV and toll eligible)
6	SHARED3GP	Shared ride 3+ person, general purpose lanes (non-toll, non-HOV)
7	SHARED3HOV	Shared ride 3+ person, HOV-eligible (non-toll)
8	SHARED3PAY	Shared ride 3+ person, toll (HOV and toll eligible)
9	WALK	Walk
10	BIKE	Bicycle
11	WALK_SET	Walk to transit
12	PNR_SET	Drive to transit (Park & Ride)
13	KNR_PERS	Kiss & Ride (personal vehicle drop-off to transit)
14	KNR_TNC	Kiss & Ride (TNC drop-off to transit)
15	TAXI	Taxi
16	TNC	Transportation Network Company (Uber/Lyft)

Time Period Codes

Period	Time Range
1	03:00 AM to 05:00 AM
2	05:00 AM to 05:30 AM
3	05:30 AM to 06:00 AM
4	06:00 AM to 06:30 AM
5	06:30 AM to 07:00 AM
6	07:00 AM to 07:30 AM
7	07:30 AM to 08:00 AM
8	08:00 AM to 08:30 AM
9	08:30 AM to 09:00 AM
10	09:00 AM to 09:30 AM
11	09:30 AM to 10:00 AM
12	10:00 AM to 10:30 AM
13	10:30 AM to 11:00 AM
14	11:00 AM to 11:30 AM
15	11:30 AM to 12:00 PM
16	12:00 PM to 12:30 PM
17	12:30 PM to 01:00 PM
18	01:00 PM to 01:30 PM
19	01:30 PM to 02:00 PM
20	02:00 PM to 02:30 PM
21	02:30 PM to 03:00 PM
22	03:00 PM to 03:30 PM
23	03:30 PM to 04:00 PM
24	04:00 PM to 04:30 PM
25	04:30 PM to 05:00 PM
26	05:00 PM to 05:30 PM
27	05:30 PM to 06:00 PM
28	06:00 PM to 06:30 PM
29	06:30 PM to 07:00 PM
30	07:00 PM to 07:30 PM
31	07:30 PM to 08:00 PM
32	08:00 PM to 08:30 PM
33	08:30 PM to 09:00 PM
34	09:00 PM to 09:30 PM
35	09:30 PM to 10:00 PM
36	10:00 PM to 10:30 PM
37	10:30 PM to 11:00 PM
38	11:00 PM to 11:30 PM
39	11:30 PM to 12:00 AM
40	12:00 AM to 03:00 AM

This comprehensive output specification enables detailed analysis of travel behavior patterns, model validation, and policy scenario evaluation using the CT-RAMP modeling framework.