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Modeling Process

Preprocessing

  1. preprocess\writeZoneSystems.job
  2. Summary: Counts tazs, mazs, taps and external zones based on node number conventions
  3. Input: hwy\mtc_final_network_base.net, the roadway network

  4. Output: zoneSystem.bat, a local batch file that defines TAZ_COUNT, TAZ_EXTS_COUNT, TAP_COUNT, and MAZ_COUNT environment variables

  5. preprocess\zone_seq_net_builder.job

  6. Summary: Builds a sequential zone numbering system for TAZs, MAZs, TAPs and Externals given the node number conventions.
  7. Input: hwy\mtc_final_network_base.net, the roadway network

  8. Output:

    1. hwy\mtc_final_network.net with additional nodeattributes, TAZSEQ, MAZSEQ, TAPSEQ and EXTSEQ
    2. hwy\mtc_final_network_zone_seq.csv, the mapping of CUBE roadway nodes to renumbered TAZs, MAZs and TAPs. Columns are N, TAZSEQ, MAZSEQ, TAPSEQ and EXTSEQ

  9. preprocess\zone_seq_disseminator.py

  10. Summary: Builds other files with zone numbers
  11. Input:
    1. hwy\mtc_final_network_zone_seq.csv, the mapping of CUBE roadway nodes to renumbered TAZs, MAZs and TAPs
    2. landuse\taz_data.csv - Zonal Data
    3. landuse\maz_data.csv - Micro Zonal Data

  12. Output:

    1. landuse\taz_data.csv - Adds (or rewrites) column TAZ (renumbered version of TAZ_ORIGINAL)
    2. landuse\maz_data.csv - Adds (or rewrites) columns TAZ and MTAZ (renumbered versions of TAZ_ORIGINAL and MTAZ_ORIGINAL)
    3. CTRAMP\model\ParkLocationAlts.csv - park location alternatives, TODO: What are these? Move this from CTRAMP
    4. CTRAMP\model\DestinationChoiceAlternatives.csv - destination choice alternatives. TODO: what are these? Move this from CTRAMP
    5. CTRAMP\model\SoaTazDistAlternatives.csv TODO: what are these? Move this from CTRAMP
    6. CTRAMP\model\ParkLocationSampleAlts.csv TODO: what are these? Move this from CTRAMP

  13. preprocessing\maz_densities.job

  14. Summary: Creates intermediate files for calculating maz densities: maz centroid location file and intersection location file.
  15. Input: hwy\mtc_final_network_base.net, the roadway network

  16. Output:

    1. hwy\maz_nodes.csv, the maz centroids and their coordinates
    2. hwy\intersection_nodes.csv, intersection nodes and their coordinates, where intersections are defined by having 5 non-freeway, non-connector links attached

  17. preprocessing\createMazDensityFile.py

  18. Summary: Calculates density-related columns to add to Micro Zonal Data
  19. Input:
    1. hwy\maz_nodes.csv, the maz centroids and their coordinates
    2. hwy\intersection_nodes.csv, intersection nodes and their coordinates
    3. landuse\maz_data.csv - Micro Zonal Data

  20. Output:

    1. landuse\maz_density.csv - Density measures for MAZs (TotInt, EmpDen, RetDen, DUDen, PopDen, intDenBin, empDenBin, duDenBin)
    2. landuse\maz_data_withDensity.csv - Micro Zonal Data joined with density measures

  21. preprocess\CreateNonMotorizedNetwork.job

  22. Summary: Create pedestrian, bicycle and pedestrian TAP (Transit Access Point) to TAP networks. The procedure to create the non-motorized networks (walk and bike) extracts the links from the network which have CNTYPE equal to TANA, PED/BIKE, MAZ, TAZ, or TAP and which are not freeways, or which have the BIKEPEDOK flag set to true (1). For the pedestrian network, any link that is one-way has an opposite direction link generated.
  23. Input:
    1. CTRAMP\scripts\block\maxCosts.block - sets maximum pedestrian distance, maximum bike distances, maximum driving generalized cost, maximum tap-tap pedestrian distance
    2. hwy\mtc_final_network.net, the roadway network

  24. Output:

    1. hwy\mtc_ped_network.net, the pedestrian network. Link attributes are the same as the roadway network, plus SP_DISTANCE, or Shortest Path Distance. This is set to:
      • @max_ped_distance@ for CNTYPE=MAZ links and CNTYPE=TAP links with a TAZ/MAZ/TAP origin or destination,
      • @nomax_bike_distance@ for TAZ links
      • FEET otherwise
    2. hwy\mtc_tap_ped_network.net, the tap-tap pedestrian network. This is the same as the pedestrian network but with SP_DISTANCE for TAP links modified to @max_tap_ped_distance@. This is because the tap-to-tap ped distances are shorter (1/2 mile versus 3 miles).
    3. hwy\mtc_bike_network.net, the bike network. This is extracted in a similar fashion as the pedestrian network, but CNTYPE = 'BIKE' links are included instead of CNTYPE = 'PED'.

  25. preprocess\tap_to_taz_for_parking.job

  26. Summary: Finds shortest pedestrian distance from TAP nodes to TAZ nodes. Max cost = @nomax_bike_distance@ + @max_ped_distance@ + @max_ped_distance@.
  27. Input: hwy\mtc_ped_network.net, the pedestrian network

  28. Output: hwy\tap_to_taz_for_parking.txt, a CSV with columns

    1. origin TAP
    2. destination TAZ
    3. destination TAZ (repeated)
    4. total SP_DISTANCE
    5. total FEET

  29. preprocess\tap_data_builder.py

  30. Summary: Maps TAPs to the closest TAZ for that TAP (via FEET).
  31. Input:
    1. hwy\mtc_final_network_zone_seq.csv, the mapping of CUBE roadway nodes to renumbered TAZs, MAZs and TAPs
    2. hwy\tap_to_taz_for_parking.txt, listing the shortest pedestrian distance from all TAPs to all TAZs

  32. Output: hwy\tap_data.csv (TODO: name this better?), a CSV with columns

    1. TAP - the tap number (in CTRAMP sequential numbering)
    2. TAP_original - the original tap number (in the CUBE network)
    3. lotid - the lot id; this is the same as TAP right now
    4. TAZ - the taz the tap is associated with (see tap_to_taz_for_parking.job)
    5. capacity - the capacity of the lot; this is set to 9999 by default, but could be changed after this process has run

  33. preprocess\SetTolls.JOB

  34. Summary: Converts TOLLBOOTH attribute to toll attributes (by vehicle class and time period) on the network.
  35. Input:
    1. hwy\mtc_final_network.net, the roadway network
    2. hwy\bridge_tolls.csv, maps TOLLBOOTH to bridge tolls. See hwy Readme for details.
    3. hwy\value_tolls.csv, maps TOLLBOOTH to value tolls, which can be distance-based.

  36. Output: hwy\mtc_final_network_with_tolls.net, the roadway network with additional link attributes TOLLXX_YY, where XX is the timeperiod and YY is the vehicle class.

  37. preprocess\SetHovXferPenalties.JOB

  38. Summary: Adds HovXPen attribute to roadway network for HOV to non-HOV in theory, but actually does nothing. TODO Deprecate script or make it do something.
  39. Input: hwy\mtc_final_network_with_tolls.net, the roadway network

  40. Output: same with HovXPen attribute added if this ever comes back. Travel Model One sets it to 0.5.

  41. preprocess\SetCapClass.job

  42. Summary: Adds area type and capcity class to the roadway network based on the weighted population and employment density of the nearby MAZs for the link (via preprocess\codeLinkAreaType.py
  43. Input: hwy\mtc_final_network_with_tolls.net, the roadway network

  44. Output: same with AT attribute added to those links with CNTYPE one of ("TANA","USE","TAZ","EXT"), set to -1 otherwise. CAPCLASS = 10x AT + FT. Area types are as follows:

    • 0: regional core
    • 1: central business district
    • 2: urban business
    • 3: urban
    • 4: suburban
    • 5: rural

  45. preprocess\CreateFiveHighwayNetworks.job

  46. Summary: Creates per-timeperiod roadway networks for assignment and skimming
    1. Deletes links with ASSIGNABLE = 0
    2. Deletes links with CNTYPE = 'TANA' and NUMLANES = 0
    3. Deletes links with CNTYPE not one of ("TANA","MAZ","TAZ","TAP","EXT")
    4. Sets freeflow time (in minutes) FFT based on FEET and FFS, freeflow speed.
    5. Variously deletes/changes USECLASS, NUMLANES, FFT or new field CTIM (congested time, which is set to be equal to FFT here) for special (mostly bridge) links.
    6. TODO: CTIM == FFT so not sure why this is added here? Also, the reversible lanes links and the shared road bypass links with their nodes are all hard-coded into the script... why not configure this?
  47. Input: hwy\mtc_final_network_with_tolls.net, the roadway network

  48. Output: hwy\avgload[EA,AM,MD,PM,EV].net, the per-timeperiod roadway networks

  49. preprocess\BuildTazNetworks.job

    • Summary: Create TAZ-based roadway networks for skimming and assignments by renumbering the nodes so TAZs (and externals TAZs) are sequential and start at 1 and the rest of the nodes are sequential and start at 1,000,001.
  50. Input: hwy\avgload[EA,AM,MD,PM,EV].net, the per-timeperiod roadway networks
  51. Output:
    1. hwy\avgload[EA,AM,MD,PM,EV]_taz.net, the per-timeperiod roadway networks with renumbered nodes. New node attribute OLD_NODE preserves mapping to original node number, as does new link attribues OLD_A and OLD_B.
    2. hwy\avgload[EA,AM,MD,PM,EV]_taz_to_node.txt, text version (TODO: for what?)
    3. hwy\msaload[EA,AM,MD,PM,EV]_taz.net is the same as hwy\avgload[EA,AM,MD,PM,EV]_taz.net but with new link variables vol, vc, vol_[da,s2,s3,sm,hv][T?], and volT initialized to zero.

Shortest Path Skims

  1. skims\NonMotorizedSkims.job

    • Summary: Creates 6 types of non-motorized shortest-path skims using Cube's shortest-path utility.
    • TODO: This creates uses cube to create a bunch of cube scripts which I find pretty awkward. It also could do more parallelizing and is limited to 9 (one per county) at present. Use python to create the cubescript? It would be easier to make flexible and run the pieces on an arbitrary number of nodes.
    • Input:
    • CTRAMP\scripts\block\maxCosts.block
    • hwy\mtc_ped_network.net, the pedestrian network
    • hwy\mtc_bike_network.net, the bike network
    • hwy\mtc_tap_ped_network.net, the tap-tap pedestrian network
    • hwy\mtc_final_network_zone_seq.csv, used to resequence columns in the skims
    • Output:
    • skims\ped_distance_maz_maz.csv, the pedestrian MAZ to MAZ skims
    • skims\ped_distance_maz_tap.csv, the pedestrian MAZ to TAP skims
    • skims\bike_distance_maz_maz.csv, the bicycle MAZ to MAZ skims
    • skims\bike_distance_maz_tap.csv, the bicycle MAZ to TAP skims
    • skims\bike_distance_taz_taz.csv, the bicycle TAZ to TAZ skims
    • skims\ped_distance_tap_tap.csv, the pedestrian TAP to TAP skims

  2. skims\MazMazSkims.job

    • Summary: Creates auto shortest-path skims for MAZ-to-MAZ using Cube's shortest-paths utility.
    • TODO: Same note as NonMotorizedSkims.job. Could share same python scripts for distributing and for creating scripts.
    • Input:
    • CTRAMP\scripts\block\maxCosts.block, which limits the max_drive_gencost
    • CTRAMP\scripts\block\hwyparam.block
    • hwy\autoopcost.properties, auto and truck operating costs
    • hwy\avgload[EA,AM,MD,PM,EV].net, the per-timeperiod roadway networks with renumbered nodes
    • Output: skims\HWYSKIM_MAZMAZ_DA.csv, the auto MAZ to MAZ skims. Columns are: ORIG_MAZ, DEST_MAZ, DEST2_MAZ, GENCOST, FEET, BRIDGE_TOLL

Build Airport Trip Matrices

  1. nonres\BuildAirPax.job
    • Summary: Creates air passenger vehicle trip tables based on 2006 survey data and 2035 predictions of air travel for the three bay area airports.
    • Input: nonres\[2007,2035]_[to,from][OAK,SFO,SJC].csv, trip tables for TAZ origins & destinations for the airports by time-of-day and mode (escort, park, rental car, taxi, limo, shared rid van, hotel shuttle, charter bus).
    • Output: nonres\tripsAirPax[EA,AM,MD,PM,EV].mtx, trip tables for resequenced (consecutive) origin/destination TAZs for modes [DA,SR2,SR3][TOLL]?

Highway and Transit Skims

  1. skims\HwySkims.job

    • Summary: Creates roadway skims.
    • Input:
    • CTRAMP\scripts\block\hwyparam.block for value of time
    • hwy\autoopcost.properties, auto and truck operating costs
    • hwy\avgload[EA,AM,MD,PM,EV]_taz.net, the per-timeperiod roadway networks with renumbered nodes
    • Output:
    • skims\HWYSKM[EA,AM,MD,PM,EV]_taz.tpp, level of service matrices for autos
    • skims\COM_HWYSKIM[EA,AM,MD,PM,EV]_taz.tpp, level of service matrices for commercial vehicles
    • logs\HwySkims.debug, a debug log file for a trace origin/destination

  2. skims\BuildTransitNetworks.job

    • Summary: First, this script runs build_walk_transfer_bypass_links.py, which creates "pseudo TAP" nodes, which are the TAP nodes offset a small distance, and creates links for pseudo TAPs to stops and vice versa, as well as pseudo TAP to pseudo TAP links. It then incorporates these new nodes and links (with CNTYPE=TRWALK), and adds new link attributes WALKDIST, WALKTIME, NTL_MODE, and TRANTIME. The networks are renumbered so that all TAPS are sequential and first, and CNTYPE MAZ, TAZ, PED, BIKE, and EXT are dropped, and TRANTIME is imported from per-time period loaded roadway networks.
    • TODO: TRANTIME is based on assumed speeds by CNTYPE and those speeds are hardcoded into this script.
    • Input:
    • hwy\mtc_final_network.net, the roadway network
    • skims\ped_distance_tap_tap-origN.csv, the pedestrian TAP to TAP skims
    • hwy\avgload[EA,AM,MD,PM,EV].net, the congested network (TODO: Is this generated with real congested times? Currently, it's not, I don't think.)
    • Output:
    • hwy\mtc_transit_network_tap_to_node.txt, mapping of renumbered nodes.
    • hwy\mtc_transit_network_[EA,AM,MD,PM,EV].net, transit network with renumbered nodes, TAP first pseudo TAP nodes and links, and TRANTIME attribute from congested roadway link time.

  3. skims\build_drive_access_skims.py

    • Summary: Called by skims\TransitSkims.job, below, this script creates drive access links from TAZs to TAPs. This process involves:
    • trn\transitLines.lin is read to determine the set of stops that are accessible for each (timeperiod, transit mode (see network node attributes for transit modes).
    • hwy\mtc_final_network_tap_links.csv is read to link those stops with their TAPs and find the set of TAPs we want to access for each (timeperiod, mode)
    • skims\ped_distance_maz_tap.csv is read to find the associate the nearest MAZ to each of those TAPs (along with the walk distance)
    • landuse\maz_data.csv correspondingly assocates each MAZ with its containing TAZ
    • These are all regarded as destination TAP/MAZ/TAZs. For each timeperiod, each origin TAZ needs a path to a TAP for each mode. This is found by combining a generalized driving cost from the driving skim, skims\DA_[EA,AM,MD,PM,EV]_taz_time.csv along with the MAZ to TAP walk time to find the optimal TAZ to MAZ to TAP walk.
    • Input: Those files listed above, plus
    • CTRAMP\scripts\block\hwyParam.block for value of time
    • hwy\autoopcost.properties for auto operating cost, to create generalized cost for drive
    • hwy\mtc_final_network_zone_seq, for translating between TAPs, MAZs, TAZs and their renumbered versions
    • hwy\mtc_final_network_tap_nodes.csv for corresponding TAPs with their transit nodes
    • Output: skims\drive_maz_taz_tap.csv with the following columns:
    • ORIG_TAZ - Origin TAZ (sequential numbering)
    • MODE - The (transit) mode
    • TIMEPERIOD - The time period
    • DEST_TAP - Destination TAP (sequential numbering)
    • DEST_MAZ - Destination MAZ (sequential numbering)
    • DEST_TAZ - Destination TAZ (sequential numbering)
    • DRIVE_TIME - Drive time from ORIG_TAZ to DEST_TAZ (minutes)
    • DRIVE_DIST - Drive distance from ORIG_TAZ to DEST_TAZ (miles)
    • DRIVE_BTOLL- Drive bridge toll from ORIG_TAZ to DEST_TAZ (in year 2010 cents)
    • WALK_DIST - Walk access distance from the MAZ centroid to the TAP (feet)

  4. skims\tap_lines.py

    • Summary: Called by skims\TransitSkims.job, below, this script outputs a list of TAPs and the lines that serve that TAP
    • Input:
    • trn\transitLines.lin, the transit lines
    • hwy\mtc_final_network_tap_links.csv, the TAP to node connectors
    • hwy\mtc_final_network_zone_seq.csv, for translating between TAPs, MAZs, TAZs and their renumbered versions
    • Output: trn\tapLines.csv with the following columns:
    • TAP: TAP (sequential numbering)
    • LINES: space-delimited list of names of lines serving the TAP

  5. skims\TransitSkims.job

    • Summary: In addition to running skims\build_drive_access_skims.py, skims\tap_lines.py, and skims\renumber_transit_line_nodes.py, creates 3 sets of transit skims per time period via Citilabs Public Transport Module
    • Input:
    • hwy\mtc_transit_network_[EA,AM,MD,PM,EV].net, transit network
    • trn\transitLines_new_nodes.lin, the transit lines with nodes renumbered
    • trn\transitSystem.PTS, the Public Transport System data file defines wait curve definitions
    • trn\transitFactors_SET[1,2,3].fac, factors for each skim set
    • trn\fareMatrix.txt, the fare matrix
    • trn\fares.far, definitions of fare systems
    • Output:
    • trn\mtc_transit_network_[EA,AM,MD,PM,EV]_SET[1,2,3]_with_transit.net, transit network (??)
    • skims\transit_skims_[EA,AM,MD,PM,EV]_SET[1,2,3].TPP, the transit skims, with matrices
      1. COMPCOST: composite cost, including walk, wait, boarding transfer and in-vehicle time, and fares
      2. IWAIT: average wait time incurred at the start of the trip for all attractive routes between zones
      3. XWAIT: average actual wait incurred at the transfer points of all attractive routes between zones
      4. XPEN: average actual transfer penalty for all attractive routes between zones
      5. BRDPEN: the boarding penalties associated with transit legs of the trip
      6. XFERS: the average number of transfers used by the attractive routes between zone pairs
      7. FARE: the average fare, in monetary units, for all attractive routes between zones
      8. XWTIME: transfer walk time
      9. AEWTIME: acess/egress walk time
      10. LB_TIME: local bus time
      11. EB_TIME: express bus time
      12. LR_TIME: light rail time
      13. HR_TIME: heavy rail time
      14. CR_TIME: commuter rail time
      15. BEST_MODE: the "highest" mode with positive time, where commuter rail=5, heavy rail=4, light rail=3, express bus=2, local bus=1
    • trn\mtc_transit_lines_[EA,AM,MD,PM,EV]_SET[1,2,3]_with_transit.lin
    • trn\mtc_transit_ntlegs_[EA,AM,MD,PM,EV]_SET[1,2,3]_with_transit.ntl, nontransit legs file
    • trn\mtc_transit_ntlegs_[EA,AM,MD,PM,EV]_SET[1,2,3]_with_transit.rte, enumerated routes
    • trn\mtc_transit_report_[EA,AM,MD,PM,EV]_SET[1,2,3]_with_transit.rpt, reports from the route-enumeration and route-evaluation processes

  6. skims\SkimSetsAdjustment.job

    • Summary: Reads the 3 transit skim sets and if any set times are the same as the times for another set for an origin/destination pair, the second set's values are updated to NA value (0).
    • Input: skims\transit_skims_[EA,AM,MD,PM,EV]_SET[1,2,3].TPP, the transit skims
    • Output: skims\transit_skims_[EA,AM,MD,PM,EV]_SET[1,2,3].TPP, the transit skims with some O/Ds removed if they're duplicates of an earlier set

Core

  • Summary: The series of behavioral models where households and persons make decisions that ultimately result in daily trips.
  • Configuration:
    1. mtctm2.properties is the primary configuration file for all models

Accessibilities

Summary: All accessibility measures for are calculated at the MAZ level. The auto travel times and cost are TAZ-based and the size variables are MAZ-based. This necessitates that auto accessibilities be calculated at the MAZ level.

PreAutoOwnership

This step selects the preliminary auto ownership level for the household, based upon household demographic variables, household ‘4D’ variables, and destination-choice accessibility terms created in the Accessibilities sub-model (see above). This auto ownership level is used to create mode choice logsums for workers and students in the household, which are then used to select work and school locations in model UsualWorkAndSchoolLocationChoice. The auto ownership model is re-run (AutoOwnership) in order to select the actual auto ownership for the household, but this subsequent version is informed by the work and school locations chosen by the UsualWorkAndSchoolLocationChoice model. All other variables and coefficients are held constant between the two models, except for alternativespecific constants.

WorkFromHomeChoice

Summary: The work from hom choice model determines whether each worker works from hom. It is a binary logit model.

UsualWorkAndSchoolLocationChoice

A workplace location choice model assigns a workplace MAZ for every employed person in the synthetic population who does not choose ‘works at home’ from the previous model. Every worker is assigned a regular work location zone (TAZ) and MAZ according to a multinomial logit destination choice model. Size terms in the model vary according to worker occupation, to reflect the different types of jobs that are likely to attract different workers. Each occupation category utilizes different coefficients for categories of employment by industry, to reflect the different likelihood of workers by occupation to work in each industry. Accessibility from the workers home to the alternative workplace is measured by a mode choice logsum taken directly from the tour mode choice model, based on peak period travel (A.M. departure and P.M. return). Various distance terms are also used.

Since mode choice logsums are required for each destination, a two-stage procedure is used for all destination choice models in the CT-RAMP system in order to reduce computational time (it would be computationally prohibitive to compute a mode choice logsum for over 40,000 MAZs and every tour). In the first stage, a simplified destination choice model is applied in which all TAZs are alternatives. The only variables in this model are the size term (accumulated from all MAZs in the TAZ) and distance. This model creates a probability distribution for all possible alternative TAZs (TAZs with no employment are not sampled). A set of alternatives are sampled from the probability distribution and, for each TAZ, a MAZ is chosen according to its size relative to the sum of all MAZs within the TAZ. These sampled alternatives constitute the choice set in the full destination choice model. Mode choice logsums are computed for these alternatives and the destination choice model is applied. A discrete choice of MAZ is made for each worker from this more limited set of alternatives. In the case of the work location choice model, a set of 30 alternatives is sampled.

The application procedure utilizes an iterative shadow pricing mechanism in order to match workers to input employment totals. The shadow pricing process compares the share of workers who choose each MAZ by occupation to the relative size of the MAZ compared to all MAZ. A shadow prices is computed which scales the size of the MAZ based on the ratio of the observed share to the estimated share. The model is re-run until the estimated and observed shares are within a reasonable tolerance. The shadow prices are written to a file and can be used in subsequent model runs to cut down computational time.

  • UEC: TourDestinationChoice.xls, worksheet workLocation
  • uwsl.soa.uec.file = DestinationChoiceAlternativeSample.xls
  • work.soa.uec.file = TourDcSoaDistance.xls
  • univ.soa.uec.file = TourDcSoaDistanceNoSchoolSize.xls
  • hs.soa.uec.file = TourDcSoaDistanceNoSchoolSize.xls
  • gs.soa.uec.file = TourDcSoaDistanceNoSchoolSize.xls
  • ps.soa.uec.file = TourDcSoaDistanceNoSchoolSize.xls
  • Logfile: event-tourDcMan.log

AutoOwnership

* Logfile: `event-ao.log`

TransponderChoice

FreeParking

CoordinatedDailyActivityPattern

IndividualMandatoryTourFrequency

MandatoryTourModeChoice

MandatoryTourDepartureTimeAndDuration

JointTourFrequency

JointTourLocationChoice

JointTourDepartureTimeAndDuration

JointTourModeChoice

IndividualNonMandatoryTourFrequency

IndividualNonMandatoryTourLocationChoice

IndividualNonMandatoryTourDepartureTimeAndDuration

IndividualNonMandatoryTourModeChoice

AtWorkSubTourFrequency

AtWorkSubTourLocationChoice

AtWorkSubTourDepartureTimeAndDuration

AtWorkSubTourModeChoice

StopFrequency

StopLocation

  • Output:

    1. ctramp_output\wsLocResults.csv, work and school location choice results
    2. ...

  • skims\SkimSetsAdjustment.job

  • assign\merge_demand_matrices.s

    • Summary: Consolidates the output matrix files from the core into TAZ- and TAP-level demand matrices.
    • Input:
    • ctramp_output\auto_[EA,AM,MD,PM,EV]_SOV_GP_[EA,AM,MD,PM,EV].mat, auto single occupancy vehicle general purpose lane TAZ-to-TAZ demand matrix
    • ctramp_output\auto_[EA,AM,MD,PM,EV]_SOV_PAY_[EA,AM,MD,PM,EV].mat, auto single occupancy vehicle pay lane TAZ-to-TAZ demand matrix
    • ctramp_output\auto_[EA,AM,MD,PM,EV]_SR2_GP_[EA,AM,MD,PM,EV].mat, auto shared ride 2 general purpose lane TAZ-to-TAZ demand matrix
    • ctramp_output\auto_[EA,AM,MD,PM,EV]_SR2_HOV_[EA,AM,MD,PM,EV].mat, auto shared ride 2 HOV lane TAZ-to-TAZ demand matrix
    • ctramp_output\auto_[EA,AM,MD,PM,EV]_SR2_PAY_[EA,AM,MD,PM,EV].mat, auto shared ride 2 pay lane TAZ-to-TAZ demand matrix
    • ctramp_output\auto_[EA,AM,MD,PM,EV]_SR3_GP_[EA,AM,MD,PM,EV].mat, auto shared ride 3+ general purpose lane TAZ-to-TAZ demand matrix
    • ctramp_output\auto_[EA,AM,MD,PM,EV]_SR3_HOV_[EA,AM,MD,PM,EV].mat, auto shared ride 3+ HOV lane TAZ-to-TAZ demand matrix
    • ctramp_output\auto_[EA,AM,MD,PM,EV]_SR3_PAY_[EA,AM,MD,PM,EV].mat, auto shared ride 3+ pay lane TAZ-to-TAZ demand matrix
    • ctramp_output\nonmotor_[EA,AM,MD,PM,EV]_BIKE_[EA,AM,MD,PM,EV].mat, bike TAZ-to-TAZ demand matrix
    • ctramp_output\nonmotor_[EA,AM,MD,PM,EV]_WALK_[EA,AM,MD,PM,EV].mat, walk TAZ-to-TAZ demand matrix
    • ctramp_output\other_[EA,AM,MD,PM,EV]_SCHLBUS_[EA,AM,MD,PM,EV].mat, school bus TAZ-to-TAZ demand matrix
    • ctramp_output\transit_[EA,AM,MD,PM,EV]_KNR_SET_set[1,2,3]_[EA,AM,MD,PM,EV].mat, kiss-and-ride TAP-to-TAP transit demand matrix sets
    • ctramp_output\transit_[EA,AM,MD,PM,EV]_PNR_SET_set[1,2,3]_[EA,AM,MD,PM,EV].mat, park-and-ride TAP-to-TAP transit demand matrix sets
    • ctramp_output\transit_[EA,AM,MD,PM,EV]_WLK_SET_set[1,2,3]_[EA,AM,MD,PM,EV].mat, walk to transit TAP-to-TAP transit demand matrix sets
    • Output:
    • ctramp_output\TAZ_Demand_[EA,AM,MD,PM,EV].mat, TAZ-to-TAZ demand matrices for the time period with matrices 1-11 direct from input files.
    • ctramp_output\TAP_Demand_set[1,2,3]_[EA,AM,MD,PM,EV].mat, TAP-to-TAP demand matrices for the time period and transit set

Non Residential

  1. nonres\IxForecasts.job

    • Summary: Creates Internal/External production/attraction matrices for the model year. Growth rates are embedded in the script.
    • Input:
    • nonres\ixDaily2006x4.may2208.new.mat, base production/attraction matrix from Census journey-to-work data for 2006
    • %MODEL_YEAR% in the environment
    • Output
    • nonres\ixDaily[%MODEL_YEAR%].tpp, the internal/external daily P/A matrices. Tables are: ix_daily_da, ix_daily_sr2, ix_daily_sr3, ix_daily_total

  2. nonres\IxTimeOfDay.job

    • Summary: Converts Internal/External daily production/attraction matrices to origin/destination trip matrices by time period. Time period factors are embedded in the script.
    • Input: nonres\ixDaily[%MODEL_YEAR%].tpp, the internal/external daily P/A matrices
    • Output: nonres\tripsIx[EA,AM,MD,PM,EV]x.tpp, the internal/external trips for the given time period. Tables are DA, S2, S3

  3. nonres\IxTollChoice.job

    • Summary: Applies a binomial choice model to drive alone, shared ride 2 and shared ride 3+ internal/external travel to determine of the traveler chooses the tolled path (if available) or non-tolled path.
    • Input:
    • nonres\tripsIx[EA,AM,MD,PM,EV]x.tpp, the internal/external trips for the given time period, with no distinction between toll/notoll trips
    • skims\HWYSKM[EA,AM,MD,PM,EV]_taz.tpp, level of service matrices for autos
    • ctramp\scripts\block\hwyparam.block for SR2COSTSHARE and SR3COSTSHARE
    • hwy\autoopcost.properties for auto operating cost
    • Output:
    • nonres\tripIx[EA,AM,MD,PM,EV].tpp, the internal/external trips for the given time period. Tables are DA, SR2, SR3, DATOLL, SR2TOLL, SR3TOLL

Assignment

  1. assign\build_and_assign_maz_to_maz_auto.job

    • Summary: Assigns short auto trips to highway network using MAZ centroids based on shortest paths.
    • Input:
    • hwy\avgload@token_period@.net, TAZ output network for skimming by time period
    • hwy\hwyParam.block, highway assignment generalized cost parameters
    • MAZ_Demand_[MAZSET]_[EA,AM,MD,PM,EV].mat, MAZ to MAZ auto demand for each local network
    • Output:
    • maz_preload_[EA,AM,MD,PM,EV].net, Network by time period with link attribute MAZMAZVOL for copying over to the TAZ to TAZ highway assignment

  2. scripts\assign\HwyAssign.job

    • Summary: Assigns trips to highway network using TAZ centroids and equilibrium capacity restraint assignment. Uses trimmed highway network which excludes lower-functional class links to improve runtime.
    • Input:
    • block\hwyParam.block, highway assignment generalized cost parameters
    • block\SpeedCapacity_1hour.block, speed-capacity table
    • block\FreeFlowSpeed.block, free-flow speed table
    • block\SpeedFlowCurve.block, volume-delay functions
    • hwy\avgload[EA,AM,MD,PM,EV]_taz.net, highway network for assignment
    • ctramp_output\TAZ_Demand_[EA,AM,MD,PM,EV].mat, household travel demand
    • nonres\tripsIx[EA,AM,MD,PM,EV].tpp, internal-external travel demand
    • nonres\tripsTrk[EA,AM,MD,PM,EV].tpp, commercial vehicle travel demand
    • nonres\tripsAirPax[EA,AM,MD,PM,EV].mtx, airport travel demand
    • Output:
    • hwy\load[EA,AM,MD,PM,EV].net, loaded highway network

  3. scripts\assign\AverageNetworkVolumes.job

    • Summary: Compute the weighted average of roadway volumes for successive iterations of the entire model stream. The iteration weights are set in the primary model stream file, RunModel. This script averages the volumes from the current iteration with the averaged volumes for ALL previous iterations. This method of successive averages "forces" model convergence.
    • Input:
    • hwy\msaload[EA,AM,MD,PM,EV]_taz.net, an MSA network used to store averaged volumes for all previous iterations
    • hwy\load[EA,AM,MD,PM,EV].net, the loaded highway network from the current iteration
    • Output:
    • hwy\msaload[EA,AM,MD,PM,EV]_taz.net, the new MSA network with averaged volumes for all iterations including the current iteration

  4. scripts\assign\CalculateAverageSpeed.job

    • Summary: Computes the speeds from a highway network with successively averaged roadway volumes.
    • Input:
    • block\hwyParam.block, highway assignment generalized cost parameters
    • block\SpeedCapacity_1hour.block, speed-capacity table
    • block\FreeFlowSpeed.block, free-flow speed table
    • block\SpeedFlowCurve.block, volume-delay functions
    • hwy\msaload[EA,AM,MD,PM,EV]_taz.net, the new MSA network with averaged volumes for all iterations including the current iteration
    • Output:
    • hwy\avgload[EA,AM,MD,PM,EV]_taz.net, a highway network with congested speeds according to MSA volumes by period

  5. scripts\assign\MergeNetworks.job

    • Summary: Merges time-period-specific assignment results into a single TP+ and CSV network. The combined network is not used by the model stream. Variables are first given time-period-specific names in the first step of the script and then the five networks are merged. Daily volumes, delay, vehicle-miles traveled, and vehicle-hours traveled calculations are performed. Note that delay is computed as the difference between congested time and free-flow time.
    • Input:
    • hwy\msaload[EA,AM,MD,PM,EV]_taz.net, highway networks with congested speeds according to MSA volumes by period
    • hwy\msa[EA,AM,MD,PM,EV]_speeds.csv, a comma-separated value file of speeds
    • Output:
    • hwy\msamerge[ITERATION].net, a merged network with assignment data for all time periods
    • hwy\msamerge[ITERATION].csv, a comma-separated value dump of the above network

  6. scripts\assign\TransitAssign.job

    • Summary: Assigns transit trips for all time periods
    • Input:
    • trn\mtc_transit_network_@TOKEN_PERIOD@_@SKIMSET_NAME@.net, the auto network consistent with transit node numbers
    • trn\transitLines_new_nodes.lin, transit line file
    • trn\transitSystem.PTS, transit system file
    • transitFactors_[SKIMSET=1,2,3].fac, transit assignment factors
    • trn\fareMatrix.txt, transit fare matrix
    • trn\fares.far, transit fares by route
    • ctramp_output\TAP_Demand_set[SKIMSET=1,2,3]_[EA,AM,MD,PM,EV].mat, resident model transit demand matrix (TAP-TAP)
    • Output:
    • trn\mtc_transit_network_[EA,AM,MD,PM,EV]_[SKIMSET=1,2,3]_with_transit_assign.net, auto network with transit assignment results
    • trn\mtc_transit_routes_[EA,AM,MD,PM,EV]_[SKIMSET=1,2,3]_with_transit_assign.rte, transit routes with assignment results
    • trn\mtc_transit_report_[EA,AM,MD,PM,EV]_[SKIMSET=1,2,3]_with_transit_assign.rpt, transit assignment report file