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Input

Input File List

The table below contains brief descriptions of the input files required to execute the travel model.

Directory File Description
hwy\ complete_network.net Highway, bike, walk network
hwy\ tolls.csv Contains toll prices for all facilities and all time periods
hwy\ interchange_nodes.csv Identifies nodes connected to interchanges
landuse\ mazData.csv Micro zone data
landuse\ tazData.csv Travel analysis zone data
nonres\ truckFF.dat Friction factors for the commercial vehicle distribution models
nonres\ truck_kfactors_taz.csv "K-factors" for the commercial vehicle distribution models
nonres\ ixDaily2015.tpp Internal-external fixed trip table for year 2015
nonres\ ixDaily2015_totals.dbf Internal-external total trips table for year 2015
nonres\ YYYY_fromtoAAA.csv Airport passenger fixed trips for year YYYY and airport AAA
nonres\ ixex_config.dbf Station-specific growth rates and commute shares for each forecast year
popsyn\ households.csv Synthetic population household file
popsyn\ persons.csv Synthetic population person file
trn\ transitLines.lin Transit lines
trn\ station_attribute_data_input.csv Station attributes
trn\ vehtype.pts Vehicle types
trn\ roadway-assignment-names-helper.csv Names for model links
trn\ fareMatrix.txt Matrix containing transit fares
trn\ fares.far Used to run fare calculations for EMME scenario

Time Periods

Time periods in Travel Model Two are consistent with Travel Model One:

Time Period Times Duration
EA (early AM) 3 am to 6 am 3 hours
AM (AM peak period) 6 am to 10 am 4 hours
MD (mid-day) 10 am to 3 pm 5 hours
PM (PM peak period) 3 pm to 7 pm 4 hours
EV (evening) 7 pm to 3 am 8 hours

Roadway Network

The all streets highway network, walk network, and bicycle network were developed from OpenStreetMap. The projection is NAD 1983 StatePlane California VI FIPS 0406 Feet.

County Node Numbering System

The highway network uses a numbering system whereby each county has a reserved block of nodes. Within each county’s block, nodes 1 through 9,999 are reserved for TAZs, 10,001 through 89,999 are for MAZs, and 90,001 through 99,999 for transit access points or TAPs. The blocks are assigned to the nine counties per MTC’s numbering scheme, as shown in the table below.

Roadway, walk, bicycle, and transit network nodes are numbered by county as well and range from 1,000,000 to 10,000,000 as shown below.

Code County TAZs MAZs TAPs Network Node HOV Lane Node
1 San Francisco 1 - 9,999 10,001 – 89,999 90,001 – 99,999 1,000,000 – 1,500,000 5,500,000 – 6,000,000
2 San Mateo 100,001 – 109,999 110,001 – 189,999 190,001 – 199,999 1,500,000 – 2,000,000 6,000,000 – 6,500,000
3 Santa Clara 200,001 – 209,999 210,001 – 289,999 290,001 – 299,999 2,000,000 – 2,500,000 6,500,000 – 7,000,000
4 Alameda 300,001 – 309,999 310,001 – 389,999 390,001 – 399,999 2,500,000 – 3,000,000 7,000,000 – 7,500,000
5 Contra Costa 400,001 – 409,999 410,001 – 489,999 490,001 – 499,999 3,000,000 – 3,500,000 7,500,000 – 8,000,000
6 Solano 500,001 – 509,999 510,001 – 589,999 590,001 – 599,999 3,500,000 – 4,000,000 8,000,000 – 8,500,000
7 Napa 600,001 – 609,999 610,001 – 689,999 690,001 – 699,999 4,000,000 – 4,500,000 8,500,000 – 9,000,000
8 Sonoma 700,001 – 709,999 710,001 – 789,999 790,001 – 799,999 4,500,000 – 5,000,000 9,000,000 – 9,000,000
9 Marin 800,001 – 809,999 810,001 – 889,999 890,001 – 899,999 5,000,000 – 5,500,000 9,500,000 – 9,999,999
External 900,001 - 999,999

Node Attributes

The following node attributes are included in the master network.

Field Description Data Type
N Node Number Integer (see Node Numbering)
X X coordinate (feet) Float
Y Y coordinate (feet) Float
OSM_NODE_ID OpenStreetMap node identifier Integer
COUNTY County Name String
DRIVE_ACCESS Node is used by automobile and/or bus links Boolean
WALK_ACCESS Node is used by pedestrian links Boolean
BIKE_ACCESS Node is used by bicycle links Boolean
RAIL_ACCESS Node is used by rail links Boolean
FAREZONE Unique sequential fare zone ID for transit skimming and assignment Integer
TAP_ID Transit access point (TAP) associated connected to this node Integer

External Nodes

N Gateway
900001 State Route 1 (Sonoma)
900002 State Route 28 (Sonoma)
900003 U.S. Route 101 (Sonoma)
900004 State Route 29 (Napa)
900005 State Route 128 (Solano)
900006 Interstate 505 (Solano)
900007 State Route 113 (Solano)
900008 Interstate 80 (Solano)
900009 State Route 12 (Solano)
900010 State Route 160 (Contra Costa)
900011 State Route 4 (Contra Costa)
900012 County Route J-4 (Contra Costa)
900013 Interstate 205 + Interstate 580 (Alameda)
900014 State Route 152 (Santa Clara/East)
900015 State Route 156 (Santa Clara)
900016 State Route 25 (Santa Clara)
900017 U.S. Route 101 (Santa Clara)
900018 State Route 152 (Santa Clara/West)
900019 State Route 17 (Santa Clara)
900020 State Route 9 (Santa Clara)
900021 State Route 1 (San Mateo)

The following link attributes are included on the master network.

Field Description Data Type Source
A from node Integer (see Node Numbering)
B to node Integer (see Node Numbering)
MODEL_LINK_ID Unique link identifier Integer
SHSTGEOMERTRYID Unique link shape identifier per SharedStreets approach String
ASSIGNABLE Is link used for assignment (1=True, 0=False) Integer
DRIVE_ACCESS Link is used by automobiles and/or buses (1=True, 0=False) Integer
BIKE_ACCESS Link is used by bicycles (1=True, 0=False) Integer
WALK_ACCESS Link is used by pedestrians (1=True, 0=False) Integer
BUS_ONLY Link is used by buses, but not automobiles (1=True, 0=False) Integer
RAIL_ONLY Link is used by rail vehicles (1=True, 0=False) Integer
DRIVE_ACCESS Link is used by automobiles and/or buses (1=True, 0=False) Integer
MANAGED Link has a parallel managed lane (1=True, 0=False) Integer
SEGMENT_ID Parallel managed lane unique segment identifier (on managed and general purpose lanes) Integer
COUNTY County name String
CNTYPE Link connector type{::nomarkdown}
  • BIKE - bike link
  • CRAIL - commuter rail
  • FERRY- ferry link
  • HRAIL - heavy rail link
  • LRAIL- light rail link
  • MAZ - MAZ connector link
  • PED - ped link
  • TANA - regular network link
  • TAP - TAP link
  • TAZ - TAZ connector link
  • USE - HOV (user class) link
{:/}		 String
TRANSIT Is Transit link Integer
TOLLBOOTH Toll link. See TOLLBOOTH & TOLLSEG table below. Links with values less than 11 are bridge tolls; 11 or above are value tolls. Integer
TOLLSEG See TOLLBOOTH & TOLLSEG table below. Integer
FT Facility type{::nomarkdown}
  • 1: Freeway
  • 2: Expressway
  • 3: Ramp
  • 4: Divided Arterial
  • 5: Undivided Arterial
  • 6: Collector
  • 7: Local
  • 8: Connector (MAZ, TAZ, TAP)
{:/}		 Integer
LANES_[EA,AM,MD,PM,EV] Model number of lanes by time period Integer
USECLASS_[EA,AM,MD,PM,EV] Link user class by time period{::nomarkdown}
  • 0 - NA; link open to everyone
  • 2 - HOV 2+
  • 3 - HOV 3+
  • 4 - No combination trucks
{:/}		 Integer

TOLLBOOTH & TOLLSEG

TOLLBOOTH TOLLSEG Definition Opening Year (or anticipated) for Toll Collection Project Card
1 Benicia-Martinez Bridge 1962 year_2015_hov_lane_benicia_bridge_toll_plaza.yml
2 Carquinez Bridge 1958 year_2015_carquinez_bridge_toll_plaza.yml
3 Richmond Bridge 1956 year_2015_richmond_bridge_toll_plaza.yml
4 Golden Gate Bridge 1937 year_2015_golden_gate_bridge_toll_plaza.yml
5 San Francisco/Oakland Bay Bridge 1936 year_2015_hov_lane_bay_bridge_toll_plaza_segment_02.yml, year_2015_hov_lane_bay_bridge_toll_plaza_segment_03.yml
6 San Mateo Bridge 1929 year_2015_san_mateo_bridge_toll_plaza.yml
7 Dumbarton Bridge 1927 year_2015_dumbarton_bridge_toll_plaza.yml
8 Antioch Bridge 1989 year_2015_antioch_bridge.yml
25 I-680 Sunol Express Lanes Phase 1 Southbound
25 1 Andrade Rd to Washington Blvd September 2010
25 2 Washington Blvd to Mission Blvd September 2010
25 3 Mission Blvd to SR 237 September 2010
28 I-580 Express Lanes Eastbound
28 1 Hacienda Dr to Airway Blvd February 2016 year_2016_managed_lane_i580e_segment_01_hacienda_drive_to_airway_blvd.yml, year_2016_managed_lane_i580e_segment_02_hacienda_drive_to_airway_blvd.yml
28 2 Airway Blvd to Livermore Ave February 2016 year_2016_managed_lane_i580e_airway_blvd_to_livermore_ave.yml
28 3 Livermore Ave to Vasco Rd February 2016 year_2016_managed_lane_i580e_segment_01_livermore_ave_to_vasco_road.yml, year_2016_managed_lane_i580e_segment_02_livermore_ave_to_vasco_road.yml
28 4 Vasco Rd to Greenville Rd February 2016 year_2016_managed_lane_i580e_vasco_road_to_greenville_road.yml
29 I-580 Express Lanes Westbound
29 1 Greenville Rd to Springtown Blvd February 2016 year_2016_managed_lane_i580w_greenville_road_to_springtown_blvd.yml
29 2 Springtown to Isabel Ave February 2016 year_2016_managed_lane_i580w_springtown_blvd_to_isabel_ave.yml
29 3 Isabel Ave to Fallon Rd February 2016 year_2016_managed_lane_i580w_isabel_ave_to_fallon_road.yml
29 4 Fallon Rd to Hacienda Dr February 2016 year_2016_managed_lane_i580w_fallon_road_to_hacienda_drive.yml
29 5 Hacienda Dr to San Ramon Rd February 2016 year_2016_managed_lane_i580w_hacienda_drive_to_san_ramon_road.yml
31 SR-237 Express Lanes Phase 1 Southbound
31 1 Dixon Landing Rd to N First Ave (Westbound) March 2012
32 SR-237 Express Lanes Phase 1 Northbound
32 1 N First Ave to Dixon Landing Rd (Eastbound) March 2012
33 I-680 Contra Costa Express Lanes Southbound
33 1 Rudgear to Crow Canyon (Crow Canyon SB pricing zone) October 2017 year_2017_managed_lane_i680n_acosta_blvd_to_livorna_road.yml
33 2 Crow Canyon to Alcosta (Alcosta pricing zone) October 2017
34 I-680 Contra Costa Express Lanes Northbound October 2017
34 1 Alcosta to Crow Canyon (Crow Canyon NB pricing zone) October 2017 year_2017_managed_lane_i680s_benicia_bridge_to_acosta_blvd.yml
34 2 Crow Canyon to Livorna (Livorna pricing zone) October 2017

Transit Network

The transit network is made up of three core components: transit lines, transit modes, and transit fares. The transit lines were built GTFS feeds from raound 2015. The lines are coded with a mode (see below) and serve a series of stop nodes. Transit fares are coded according to Cube's Public Transport program (see below).

Transit trips are assigned between transit access points (TAPs), which represent individual or collections of transit stops for transit access/egress. TAPs are essentially transit specific TAZs that are automatically coded based on the transit network. See the Level of Service Information.

Field Description Data Type
trip_id unique identifier for each trip Integer
is_stop_A if node A is a transit stop boolean
access_A
stop_sequence_A stop sequence of node A, if node A is a stop Integer
shape_pt_sequence_B sequence of node A in the route Integer
shape_model_node_id_B model_node_id of node B Integer
NAME name of route with TOD string
agency_id transit agency id string
TM2_line_haul_name 'Commuter rail', 'Express bus', 'Local bus', 'Light rail', 'Ferry service', 'Heavy rail' string
TM2_mode see mode dictionary Integer
faresystem faresystem (1-50) Integer
tod time of day (1, 2, 3, 4, 5) Integer
HEADWAY transit service headway in minute Integer
A A of link Integer
B B of link Integer
model_link_id model_link_id Integer
shstGeometryId the shstGeometryId of the link Integer

Transit Modes

The following transit modes are defined based on the Open511 attributes (but not completely, since they came from the GTFS database predecessor, the Regional Transit Database). These modes represent combinations of operators and technology.

TM2_operator agency_name TM2_mode TM2_line_haul_name faresystem
30 AC Transit 84 Express bus 9
30 AC Transit 30 Local bus 9
30 AC Transit 30 Local bus 11
5 ACE Altamont Corridor Express 133 Commuter rail 1
26 Bay Area Rapid Transit 120 Heavy rail 2
3 Blue & Gold Fleet 103 Ferry service 13
3 Blue & Gold Fleet 103 Ferry service 14
3 Blue & Gold Fleet 103 Ferry service 12
17 Caltrain 130 Commuter rail 3
23 Capitol Corridor 131 Commuter rail 4
19 Cloverdale Transit 63 Local bus 7
17 Commute.org Shuttle 14 Local bus 46
15 County Connection 86 Express bus 16
15 County Connection 42 Local bus 15
15 County Connection 42 Local bus 17
10 Emery Go-Round 12 Local bus 18
28 Fairfield and Suisun Transit 92 Express bus 10
28 Fairfield and Suisun Transit 52 Local bus 10
35 Golden Gate Transit 87 Express bus 8
20 Golden Gate Transit 101 Ferry service 19
20 Golden Gate Transit 101 Ferry service 20
35 Golden Gate Transit 70 Local bus 8
99 MVgo Mountain View 16 Local bus 21
39 Marin Transit 71 Local bus 23
39 Marin Transit 71 Local bus 24
21 Petaluma Transit 68 Local bus 47
13 Rio Vista Delta Breeze 52 Local bus 5
6 SamTrans 80 Express bus 6
6 SamTrans 24 Local bus 6
25 San Francisco Bay Ferry 101 Ferry service 28
25 San Francisco Bay Ferry 101 Ferry service 30
25 San Francisco Bay Ferry 101 Ferry service 31
25 San Francisco Bay Ferry 101 Ferry service 32
25 San Francisco Bay Ferry 101 Ferry service 29
22 San Francisco Municipal Transportation Agency 110 Light rail 25
22 San Francisco Municipal Transportation Agency 20 Local bus 25
22 San Francisco Municipal Transportation Agency 21 Local bus 26
1 Santa Rosa CityBus 66 Local bus 33
12 SolTrans 91 Express bus 35
12 SolTrans 49 Local bus 34
12 SolTrans 49 Local bus 35
19 Sonoma County Transit 63 Local bus 7
7 Stanford Marguerite Shuttle 13 Local bus 22
4 Tri Delta Transit 95 Express bus 36
4 Tri Delta Transit 44 Local bus 37
4 Tri Delta Transit 44 Local bus 36
36 Union City Transit 38 Local bus 38
31 VTA 81 Express bus 40
31 VTA 81 Express bus 41
31 VTA 111 Light rail 41
31 VTA 28 Local bus 41
31 VTA 28 Local bus 39
14 Vacaville City Coach 56 Local bus 48
38 Vine (Napa County) 94 Express bus 43
38 Vine (Napa County) 60 Local bus 42
38 Vine (Napa County) 60 Local bus 44
37 WestCat (Western Contra Costa) 90 Express bus 49
37 WestCat (Western Contra Costa) 90 Express bus 50
37 WestCat (Western Contra Costa) 46 Local bus 49
24 Wheels Bus 17 Local bus 45

Transit Fares

Transit fares are modeled in Cube's Public Transport (PT) program as follows: * Each transit mode is assigned a fare system in the Cube factor files * Each fare system is defined in the fares.far fare file * Each fare system is either FROMTO (fare zone based) or FLAT (initial + transfer in fare) * For FROMTO fare systems: 1. Each stop node is assigned a FAREZONE ID in the master network 1. The fare is looked up from the fare matrix (fareMatrix.txt), which is a text file with the following columns: MATRIX ZONE_I ZONE_J VALUE 1. The fare to transfer in from other modes is defined via the FAREFROMFS array (of modes) in the fares.far file * For FLAT fare systems: 1. The initial fare is defined via the IBOARDFARE in the fares.far file 1. The fare to transfer in from other modes is defined via the FAREFROMFS array (of modes) in the fares.far file

Micro Zonal Data

Column Name Description Used by Source
MAZ_ORIGINAL Original micro zone number. It's original because these will get renumbered during the model run assuming the node numbering conventions
TAZ_ORIGINAL Original TAZ number. It's original because these will get renumbered during the model run assuming the node numbering conventions
CountyID County ID Number MAZAutoTripMatrix via MgraDataManager
CountyName County name string
DistID District ID Number (TODO: link district map) TourModeChoice.xls District system definition
DistName District Name (TODO: link district map) District system definition
ACRES MAZ acres createMazDensityFile.py Calculated from shapefile
HH Total number of households MgraDataManager
POP Total population MgraDataManager
Employment Industry Categories
ag Employment in agriculture: NAICS 11 Accessibilities
art_rec Employment in arts, entertainment and recreation: NAICS 71 Accessibilities
const Employment in construction: NAICS 23 Accessibilities
eat Employment in food services and drinking places: NAICS 722 Accessibilities
ed_high Employment in junior colleges, colleges, universities: NAICS 6112, 6113, 6114, 6115 Accessibilities
ed_k12 Employment in K-12 schools: NAICS 6111 Accessibilities
ed_oth Employment in other schools, libraries and educational services: NAICS 6116, 6117 Accessibilities
fire Employment in FIRE (finance, insurance and real estate): NAICS 52, 53 not in leasing Accessibilities
gov Employment in government: NAICS 92 Accessibilities
health Employment in health care: NAICS 62 except those in serv_soc Accessibilities
hotel Employment in hotels and other accomodations: NAICS 721 Accessibilities
info Employment in information-based services: NAICS 51 Accessibilities
lease Employment in leasing: NAICS 532 Accessibilities
logis Employment in logistics/warehousing and distribution: NAICS 42, 493 Accessibilities
man_bio Employment in biological/drug manufacturing: NAICS 325411, 325412, 325313, 325414 Accessibilities
man_hvy Employment in heavy manufacturing: NAICS 31-33 subset Accessibilities
man_lgt Employment in light manufacturing: NAICS 31-33 subset Accessibilities
man_tech Employment in high-tech manufacturing: NAICS 334 Accessibilities
natres Employment in mining and resource extraction: NAICS 21 Accessibilities
prof Employment in professional and technical services: NAICS 54 Accessibilities
ret_loc Employment in local-serving retail: NAICS 444130, 444190, 444210, 444220, 445110, 445120, 445210, 445220, 445230, 445291, 445292, 445299, 445310, 446110, 446120, 446130, 446191, 446199, 447110, 447190, 448110, 448120, 448130, 448140, 448150, 448190, 448210, 448310, 448320, 451110, 451120, 451130, 451140, 451211, 451212, 452910, 452990, 453110, 453220, 453310, 453910, 453920, 453930, 453991, 453998, 454111, 454112, 454113 Accessibilities
ret_reg Employment in regional retail: NAICS 441110, 441120, 441210, 441222, 441228, 441310, 441320, 442110, 442210, 442291, 442299, 443141, 443142, 444110, 444120, 452111, 452112, 453210, 454210, 454310, 454390 Accessibilities
serv_bus Employment in managerial services, administrative and business services: NAICS 55,56 Accessibilities
serv_pers Employment in personal and other services: NAICS 53, 81 Accessibilities
serv_soc Employment in social services and childcare: NAICS 624 Accessibilities
transp Employment in transportation: NAICS 48 (most of it), 49 (not in logis) Accessibilities
util Employment in utilities: NAICS 22, 56 Accessibilities
unclass Employment not classified is this used?
emp_total Total employment Accessibilities
School Enrollment Categories
publicenrollgradekto8 Public school grade K-8 enrollment Accessibilities
privateenrollgradekto8 Private school grade K-8 enrollment Accessibilities
publicenrollgrade9to12 Public high school grade 9-12 enrollment Accessibilities
privateenrollgrade9to12 Private high school grade 9-12 enrollment Accessibilities
comm_coll_enroll Community college enrollment Accessibilities
EnrollGradeKto8 Total high school grade 9-12 enrollment MgraDataManager
EnrollGrade9to12 Total high school grade 9-12 enrollment MgraDataManager
collegeEnroll Major College enrollment MgraDataManager
otherCollegeEnroll Other College enrollment MgraDataManager
AdultSchEnrl Adult School enrollment MgraDataManager
ech_dist Elementary school district MgraDataManager
hch_dist High school district MgraDataManager
Parking
parkarea {::nomarkdown}
  • 1: Trips with destinations in this MAZ may choose to park in a different MAZ, parking charges apply (downtown)
  • 2: Trips with destinations in parkarea 1 may choose to park in this MAZ, parking charges might apply (quarter mile buffer around downtown)
  • 3: Only trips with destinations in this MAZ may park here, parking charges apply (outside downtown paid parking, only show cost no capacity issue)
  • 4: Only trips with destinations in this MAZ may park here, parking charges do not apply (outside downtown, free parking)
{:/}		 MgraDataManager
hstallsoth Number of stalls allowing hourly parking for trips with destinations in other MAZs MgraDataManager
hstallssam Number of stalls allowing hourly parking for trips with destinations in the same MAZ MgraDataManager
hparkcost Average cost of parking for one hour in hourly stalls in this MAZ, dollars MgraDataManager
numfreehrs Number of hours of free parking allowed before parking charges begin in hourly stalls MgraDataManager
dstallsoth Stalls allowing daily parking for trips with destinations in other MAZs MgraDataManager
dstallssam Stalls allowing daily parking for trips with destinations in the same MAZ MgraDataManager
dparkcost Average cost of parking for one day in daily stalls, dollars MgraDataManager
mstallsoth Stalls allowing monthly parking for trips with destinations in other MAZs MgraDataManager
mstallssam Stalls allowing monthly parking for trips with destinations in the same MAZ MgraDataManager
mparkcost Average cost of parking for one day in monthly stalls, amortized over 22 workdays, dollars MgraDataManager
Other
park_area Area of park space, in square meters Accessibilities
Calculated land use measures
TotInt Total intersections MgraDataManager, AutoOwnership createMazDensityFile.py
DUDen Dwelling unit density MgraDataManager createMazDensityFile.py
EmpDen Employment density MgraDataManager createMazDensityFile.py
PopDen Population density AutoOwnership createMazDensityFile.py
RetEmpDen Retail employment density AutoOwnership createMazDensityFile.py
TotIntBin Total intersection bin is this used? createMazDensityFile.py
EmpDenBin Employment density bin AtWorkSubtourFrequency createMazDensityFile.py
DuDenBin Dwelling unit density bin AtWorkSubtourFrequency createMazDensityFile.py

Zonal Data

Field Description Used by
TAZ_ORIGINAL Original TAZ number. It's original because these will get renumbered during the model run assuming the node numbering conventions
AVGTTS Average travel time savings for transponder ownership model TazDataManager
DIST Distance for transponder ownership model TazDataManager
PCTDETOUR Percent detour for transponder ownership model TazDataManager
TERMINALTIME Terminal time TazDataManager

Synthetic Population

The synthetic population is generated by populationsim.

Households

Column Name Description Used by
HHID Unique household ID HouseholdDataManager
TAZ TAZ of residence HouseholdDataManager
MAZ MAZ of residence HouseholdDataManager
MTCCountyID County of residence HouseholdDataManager
HHINCADJ Household income in 2010 dollars HouseholdDataManager
NWRKRS_ESR Number of workers. A count of EMPLOYED persons in the household, ranges from 0 to 20. HouseholdDataManager
VEH Number of vehicles owned by the household. From PUMS, ranges from 0 (no vehicles) to 6 (6 or more vehicles), with N/A recoded as -9 for group quarters HouseholdDataManager
NP Number of persons in household. From PUMS. Ranges from 1 to 20 HouseholdDataManager
HHT Household type. From PUMS. {::nomarkdown}
  • 1=Married-couple family household
  • 2=Other family household, Male householder, no wife present
  • 3=Other family household, Female householder, no husband present
  • 4=Nonfamily household, Male householder, Living alone
  • 5=Nonfamily household, Male householder, Not living alone
  • 6=Nonfamily household, Female householder, Living alone
  • 7=Nonfamily household, Female householder, Not living alone
  • -9=N/A recoded for group quarters
{:/}		 HouseholdDataManager
BLD Units in structure. From PUMS. {::nomarkdown}
  • 1=Mobile home or trailer
  • 2=One-family house detached
  • 3=One-family house attached
  • 4=2 Apartments
  • 5=3-4 Apartments
  • 6=5-9 Apartments
  • 7=10-19 Apartments
  • 8=20-49 Apartments
  • 9=50 or more apartments
  • 10=Boat, RV, van, etc.
  • -9=N/A recoded for group quarters
{:/}		 HouseholdDataManager
TYPE Type of unit (housing, institutional or noninstitutional group quarters). From PUMS. 1=Housing unit, 2=Institutional group quarters (shouldn't be included in this input data set), 3=Noninstitutional group quarters. HouseholdDataManager

Persons

Column Name Description Used by
HHID Unique household ID HouseholdDataManager
PERID Unique person ID HouseholdDataManager
AGEP Age of person. From PUMS, ranges from 0 to 99. HouseholdDataManager
SEX Sex of person. From PUMS. 1=Male, 2=Female HouseholdDataManager
SCHL Education attainment of person. From PUMS. {::nomarkdown}
  • -9=N/A recoded for less than 3 years old
  • 1=No schooling completed
  • 2=Nursery school to grade 4
  • 3=Grade 5 or grade 6
  • 4=Grade 7 or grade 8
  • 5=Grade 9
  • 6=Grade 10
  • 7=Grade 11
  • 8=12th grade, no diploma
  • 9=High school graduate
  • 10=Some college, but less than 1 year
  • 11=One or more years of college, no degree
  • 12=Associate's degree
  • 13=Bachelor's degree
  • 14=Master's degree
  • 15=Professional school degree
  • 16=Doctorate degree
      • {:/}
 HouseholdDataManager
OCCP Occupation, based on recoding of SOCP00 or SOCP10 from PUMS. Recoding done in create_seed_population.py {::nomarkdown}
  • -999 is N/A recode (less than 16 years old/Not in labor force who last worked more than 5 years ago or never worked)
  • 1=Management
  • 2=Professional
  • 3=Services
  • 4=Retail
  • 5=Manual
  • 6=Military
{:/} HouseholdDataManager
WKHP Usual hours worked per week past 12 months. From PUMS. -9=N/A recoded for persons less than 16 years old or who did not work during the past 12 months. Otherwise ranges from 1 to 99. HouseholdDataManager
WKW Weeks worked during past 12 months. From PUMS. {::nomarkdown}
  • -9=N/A recoded for persons less than 16 years old or who did not work during the past 12 months
  • 1=50 to 52 weeks
  • 2=48 to 49 weeks
  • 3=40 to 47 weeks
  • 4= 27 to 39 weeks
  • 5=14 to 26 weeks
  • 6=13 weeks or less
{:/}		 HouseholdDataManager
EMPLOYED Based on ESR below. 1=Employed, set if ESR is one of [1,2,4,5]. 0=Unemployed. HouseholdDataManager
ESR Employment status recode. From PUMS {::nomarkdown}
  • 0=N/A recoded for persons less than 16 years old
  • 1=Civilian employed, at work
  • 2=Civilian employed, with a job but not at work
  • 3=Unemployed
  • 4=Armed forces, at work
  • 5=Armed forces, with a job but not at work
  • 6=Not in labor force
{:/}		 HouseholdDataManager
SCHG Grade level attending. From PUMS {::nomarkdown}
  • -9=N/A (not attending school) recoded
  • 1=Nursery school./preschool
  • 2=Kindergarten
  • 3=Grade 1 to grade 4
  • 4=Grade 5 to grade 8
  • 5=Grade 9 to grade 12
  • 6=College undergraduate
  • 7=Graduate or professional school
{:/}		 HouseholdDataManager

Truck Distribution

MTC uses a simple three-step (generation, distribution, and assignment) commercial vehicle model to generate estimates of four types of commercial vehicles. The four vehicle types are very small (two-axle, four-tire), small (two-axle, six-tire), medium (three-axle), and large or combination trucks (four-or-more-axle).

Friction Factors

The trip distribution step uses a standard gravity model with a blended travel time impedance measure. This file sets the friction factors, which are vehicle type specific, using an ASCII fixed format text file with the following data:

  • Travel time in minutes (integer, starting in column 1, left justified);
  • Friction factors for very small trucks (integer, starting in column 9, left justified);
  • Friction factors for small trucks (integer, starting in column 17, left justified);
  • Friction factors for medium trucks (integer, starting in column 25, left justified); and,
  • Friction factors for large trucks (integer, starting in column 33, left justified).

K-Factors

The trip distribution step also uses a matrix of K-factors to adjust the distribution results to better match observed data. This matrix contains a unit-less adjustment value; the higher the number, the more attractive the production/attraction pair.

Fixed Demand

MTC uses representations of internal/external and air passenger demand that is year-, but not scenario-, specific -- meaning simple sketch methods are used to estimate this demand from past trends. This demand is then fixed for each forecast year and does not respond to changes in land use or the transport network.

Internal/External

So-called internal/external demand is travel that either begins or ends in the nine county Bay Area. This demand is based on Census journey-to-work data and captures all passenger (i.e. non-commercial) vehicle demand. This demand is introduced to the model via a matrix that contains the following four demand tables in production-attraction format:

  • Daily single-occupant vehicle flows;
  • Daily two-occupant vehicle flows;
  • Daily three-or-more occupant vehicle flows; and,
  • Daily vehicle flows, which is the sum of the first three tables and not used by the travel model.

Air Passenger

Air passenger demand is based on surveys of air passenger and captures demand from the following travel modes: passenger vehicles, rental cars, taxis, limousines, shared ride vans, hotel shuttles, and charter buses. This demand is introduced to the model via Main.TimePeriods specific matrices that contain the following six flow tables:

  • Single-occupant vehicles;
  • Two-occupant vehicles;
  • Three-occupant vehicles;
  • Single-occupant vehicles that are willing to pay a high-occupancy toll lane fee;
  • Two-occupant vehicles that are willing to pay a high-occupancy toll lane fee; and,
  • Three-occupant vehicles that are willing to pay a high-occupancy toll lane fee.