Dynamic transit assignment tool
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tests Rename and re-organize tests. Jun 24, 2018


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Fast-Trips is a Dynamic Transit Passenger Assignment tool written in Python and supplemented by code in C++. For more information about this visit the following links:

Use Cases
Fast-Trips can be used for analyzing short-term effects as a stand-along tool as well as long range planning when linked up with a travel demand modeling tool:

  • An analyst who wants to study the effect of a on service reliability of a schedule change.
  • An analyst who wants to evaluate a service plan for a special event.
  • A modeler who wants to include capacity constraints and reliability as a performance metric for long-range planning investments as evaluated in a long range transportation plan.



Follow the steps below to setup up fast-trips:

1 - Setup your Python environment

Right now Fast-Trips requires Python 2.7.

One option is to install a data analytics Python 2.7 distribution which bundles these, like Anaconda. If you have Anaconda installed, you can create a virtual environment with the command below for Fast-Trips.

  conda create -q -y -n fast-trips-env python=2.7 numpy pandas>=0.22 psutil pytest
  source activate fast-trips-env
  pip install partridge==0.6.0.dev1

Note that Fast-Trips currently uses a development version of Partridge that is required in order to read unzipped GTFS files.

Another option is to install the requirements in an existing Python 2.7 environment:

   pip install numpy pandas >=0.22 psutil pytest partridge==0.6.0.dev1

Please note: Pandas 0.21.x has known issues, and it is not compatible with Fast-Trips.

2 - Get and Install Fast-Trips

Option 1 - From Source (Good for Developers or if you want "the latest")

  • Install Git and if desired, a GUI for git like GitHub Desktop
  • Clone or fork-and-clone the fast-trips repository (https://github.com/BayAreaMetro/fast-trips.git) to a local directory: <fast-trips-dir>. If the user plans on making changes to the code, it is recommended that the repository be forked before cloning.
  • Switch to the branch of the repository that you want to use by either using git from the command line (git checkout master or using a GUI). The master branch should be the latest stable branch and the develop branch has the latest. Features are developed on feature-branches.
  • If compiling on Windows, install Microsoft Visual C++ Compiler for Python 2.7. On Linux, install the python-dev package.
  • Set the PYTHONPATH environment variable to the location of your fast-trips repo, which we're calling <fast-trips-dir>.
  • To build, in the fast-trips directory <fast-trips-dir>, run the following in a command prompt: python setup.py build_ext --inplace.

Option 2 - Latest Release

We occassionally put the latest release versions on PyPI, the Python Package Index

   pip install fasttrips

3 - Test the Install

  • To run an example to make sure it is installed correctly, run from the <fast-trips-dir>:
   python fasttrips\Examples\Bunny_Hop\run_bunny_hop.py

(remember to use file separators appropriate for your operating system).


The input to fast-trips consists of:

  • A Transit Network directory, including schedules, access, egress and transfer information, specified by the GTFS-Plus data standard
  • A Transit Demand directory, including persons, households and trips, specified by the dyno-demand (Demand Data) standard
  • Fast-Trips Configuration, specified below

Configuration is specified in the following files:


This is a required configuration file that where a number of configuration parameters are specified for running FastTrips.

The configuration files are parsed by python's ConfigParser module and therefore adhere to that format, with two possible sections: fasttrips and pathfinding. (See Bunny Hop example and Springfield examples )

Configuration Options: fasttrips

Option Name Type Default Description
bump_buffer float 5 Not really used yet.
bump_one_at_a_time bool False
capacity_constraint bool False Hard capacity constraint. When True, fasttrips forces everyone off overcapacity vehicles and disallows them from finding a new path using an overcapacity vehicle.
create_skims bool False Not implemented yet.
debug_num_trips int -1 If positive, will truncate the trip list to this length.
debug_trace_only bool False If True, will only find paths and simulate the person ids specified in trace_person_ids.
debug_output_columns bool False If True, will write internal & debug columns into output.
fare_zone_symmetry bool False If True, will assume fare zone symmetry. That is, if fare_id X is configured from origin zone A to destination zone B, and there is no fare configured from zone B to zone A, we'll assume that fare_id X also applies.
max_iterations int 1 Maximum number of pathfinding iterations to run.
number_of_processes int 0 Number of processes to use for path finding.
output_passenger_trajectories bool True Write chosen passenger paths? TODO: deprecate. Why would you ever not do this?
output_pathset_per_sim_iter bool False Output pathsets for each simulation iteration? If false, just outputs once per path-finding iteration.
prepend_route_id_to_trip_id bool False This is for readability in debugging; if True, then route ids will be prepended to trip ids.
simulation bool True Simulate transit vehicles? After path-finding, should fast-trips update vehicle times and put passengers on vehicles? If False, fast-trips still calculates costs and probabilities and chooses paths, but the vehicle times will not be updated from those read in from the input network, and passengers will not be loaded onto vehicles. This is useful for debugging path-finding and verifying that pathfinding calculations are consistent with cost/fare calculations done outside pathfinding.
skim_start_time string 5:00 Not implemented yet.
skim_end_time string 10:00 Not implemented yet.
skip_person_ids string 'None' A list of person IDs to skip.
trace_ids string 'None' A list of tuples, (person ID, person trip ID), for whom to output verbose trace information.

Configuration Options: pathfinding

Option Name Type Default Description
max_num_paths int -1 If positive, drops paths after this IF probability is less than ``
min_path_probability float 0.005 Paths with probability less than this get dropped IF max_num_paths specified AND hit.
min_transfer_penalty float 0.1 Minimum transfer penalty. Safeguard against having no transfer penalty which can result in terrible paths with excessive transfers.
overlap_chunk_size int 500 How many person's trips to process at a time in overlap calculations in python simulation (more means faster but more memory required.)
overlap_scale_parameter float 1 Scale parameter for overlap path size variable.
overlap_split_transit bool False For overlap calcs, split transit leg into component legs (A to E becomes A-B-C-D-E)
overlap_variable string 'count' The variable upon which to base the overlap path size variable. Can be one of None, count, distance, time.
pathfinding_type string 'stochastic' Pathfinding method. Can be stochastic, deterministic, or file.
pathweights_fixed_width bool False If true, read the pathweights file as a fixed width, left-justified table (as opposed to a CSV, which is the default).
stochastic_dispersion float 1.0 Stochastic dispersion parameter. TODO: document this further.
stochastic_max_stop_process_count int -1 In path-finding, how many times should we process a stop during labeling? Specify -1 for no max.
stochastic_pathset_size int 1000 In path-finding, how many paths (not necessarily unique) determine a pathset?
time_window float 30 In path-finding, the max time a passenger would wait at a stop.
transfer_fare_ignore_pathfinding bool False In path-finding, suppress trying to adjust fares using transfer rules. For performance.
transfer_fare_ignore_pathenum bool False In path-enumeration, suppress trying to adjust fares using transfer rules. For performance.
user_class_function string 'generic_user_class' A function to generate a user class string given a user record.
depart_early_allowed_min float 0.0 Allow passengers to depart before their departure time time target by this many minutes.
arrive_late_allowed_min float 0.0 Allow passengers to arrive after their arrival time target by this many minutes.

More on Overlap Path Size Penalties

The path size overlap penalty is formulated by Ramming and discussed in Hoogendoorn-Lanser et al. (see References ).

When the pathsize overlap is penalized (pathfinding overlap_variable is not None), then the following equation is used to calculate the path size overlap penalty:

Path Overlap Penalty Equation


  • i is the path alternative for individual n
  • Γi is the set of legs of path alternative i
  • la is the value of the overlap_variable for leg a. So it is either 1, the distance or the time of leg a depending on whether overlap_scale_parameter is count, distance or time, respectively.
  • *Li is the total sum of the overlap_variable over all legs la that make up path alternative i
  • *Cin is the choice set of path alternatives for individual n that overlap with alternative i
  • γ is the overlap_scale_parameter
  • δai = 1 and δaj = 0 ∀ ji

From Hoogendoor-Lanser et al.:

Consequently, if leg a for alternative i is unique, then [the denominator is equal to 1] and the path size contribution of leg a is equal to its proportional length la/Li. If leg la is also used by alternative j, then the contribution of leg la to path size PSi is smaller than la/Li. If γ = 0 or if routes i and j have equal length, then the contribution of leg a to PSi is equal to la/2Li. If γ > 0 and routes i and j differ in length, then the contribution of leg a to PSi depends on the ratio of Li to Lj. If route i is longer than route j and γ > 1, then the contribution of leg a to PSi is larger than la/2Li; otherwise, the contribution is smaller than la/2Li. If γ > 1 in the exponential path size formulation, then long routes are penalized in favor of short routes. The use of parameter γ is questionable if overlapping routes have more or less equal length and should therefore be set to 0. Overlap between those alternatives should not affect their choice probabilities differently. The degree to which long routes should be penalized might be determined by estimating γ. If γ is not estimated, then an educated guess with respect to γ should be made. To this end, differences in route length between alternatives in a choice set should be considered.


This is an optional python file containing functions that are evaluated to ascertain items such as user classes. This could be used to programmatically define user classes based on person, household and/or trip attributes.

The function name for user class is specified in the pathfinding input parameter user_class_function


def user_class(row_series):
    Defines the user class for this trip list.

    This function takes a single argument, the pandas.Series with person, household and
    trip_list attributes, and returns a user class string.
    if row_series["hh_id"].lower() in ["simpson","brady","addams","jetsons","flintstones"]:
        return "fictional"
    return "real"


The pathweight file is a required file that tells Fast-Trips how much to value each attribute of a path. This will be used for the stop-labeling stage but also the path selection, which is done in a logit model. Therefore, the weights should be consistent with with utility.

A good rule of thumb to consider is that typical in-vehicle-time coefficients for mode choice logit models range from 0.01 to 0.08. If you consider route choice to be a nest of mode choice, you would divide whatever the in-vehicle-time coefficient is for mode choice by whatever that nesting coefficient is. One assumption is that the nesting coefficient for route choice should have a smaller value than a typical mode choice model, meaning that people are more likely to switch routes than modes. So, if a mode-choice utility coefficient for in-vehicle time is 0.02 and an assumed nesting coefficient is 0.2, the value for route choice would be 0.10 (0.02 / 0.2).

The file can be a csv or fixed-format. If you use a fixed-format, make sure pathweights_fixed_width = True in the run configuration file (e.g., config_ft.txt).

pathweight_ft.txt must have the following columns:

Column Name Type Description
user_class Str Config functions can use trip list, person, and household attributes to return a user class string to the trip.

The string that is returned determines the set of path weights that are used.

( ??? is default if no user class function ? )
purpose Str This should match the trip purpose as specified in trip_list.txt
demand_mode_type Str One of transfer,access,egress or transit
demand_mode Str One of: transfer, a string specified as access/egress mode in the trip_list.txt demand file (i.e. walk, PNR), or a string specified as a transit mode in trip_list.txt demand file (i.e. local_bus, commuter_rail)
supply_mode Str
  • For demand_mode_type=transit, corresponds to the transit mode as defined in the GTFS-Plus input network.
  • For demand_mode_type=transfer and demand_mode=transfer, is one of walk, wait, or transfer_penalty.
  • For demand_mode_type = access, is one of walk_access, bike_access, pnr_access, or knr_access.
  • For demand_mode_type = egress, is one of walk_egress, bike_egress, pnr_egress, or knr_egress.
weight_name Str An attribute of the path link. See below for more details.
weight_value Float The multiplier for the attribute named weight_name


  1. If demand mode X has supply mode Y, that means a trip specified as transit mode X in the trip_list.txt may use a transit link specified as Y in the network. Moreover, if the trip list were to specify that someone takes commuter_rail (like if the ABM chooses the primary mode for them as commuter rail), then they can still take a local bus or any lesser mode on their trip in addition to commuter rail. Often in this case, the weights are assumed to be higher for non-commuter rail modes and lower for commuter rail to induce them to ride. For example:
demand_mode supply_mode weight_name weight_value
commuter_rail local_bus in_vehicle_time_min 1.5
commuter_rail heavy_rail in_vehicle_time_min 1.0

weight_name Values

The following is an example of a minimally specified pathweight_ft.txt :

demand_mode_type demand_mode supply_mode weight_name weight_value
access walk walk_access time_min 2
egress walk walk_egress time_min 2
transit transit local_bus wait_time_min 2
transit transit local_bus in_vehicle_time_min 1
transfer transfer transfer transfer_penalty 5
transfer transfer transfer time_min 2

For most of the weights prefix mode is not needed. E.g. there is no need to label weight_name time_min for supply_mode walk_access as walk_time_min, because the fact that the supply_mode is walk_access means it is only assessed on walk links. The drive option (PNR/KNR access/egress), however, should have walk_ and drive_ prefixes, because the access can have both components: driving to the station from the origin and walking from the lot to the station. So for example, for supply_mode pnr_access there will be two weights associated with travel time: walk_time_min and drive_time_min.

The following is a partial list of possible weight names based on the demand mode / supply mode combinations.

demand_mode_type = access / demand_mode = walk / supply_mode = walk_access

  • time_min
  • depart_early_min
  • depart_late_min

demand_mode_type = egress / demand_mode = walk / supply_mode = walk_egress

  • time_min
  • arrive_early_min
  • arrive_late_min

demand_mode_type = access / demand_mode = PNR / supply_mode = pnr_access

  • walk_time_min
  • drive_time_min
  • arrive_early_min
  • arrive_late_min

demand_mode_type = transfer / demand_mode = transfer / supply_mode = transfer

  • transfer_penalty
  • time_min
  • wait_time_min

demand_mode_type = transit / demand_mode = transit / supply_mode = <pick a transit mode>

  • in_vehicle_time_min
  • wait_time_min

Note that the cost component is handled at the path level using the value of time column in trip_list.txt.

Weight Qualifiers

By default, Fast-Trips will apply all weights as a constant on the appropriate variable. Fast-Trips also supports weight qualifiers which allow for the weights to be applied using more complex models. The supported qualifiers are listed below. Certain qualifiers also require modifiers to shape the cost function.

If no qualifier is specified, constant will be assumed.

Qualifier Formulation Required Modifiers
constant (default) Constant Weight Equations N/A
exponential Exponential Weight Equations N/A
logarithmic Logarithmic Weight Equations log_base
logistic Logistic Weight Equations logistic_max


#Pathweights_ft.txt snippet
user_class purpose demand_mode_type demand_mode    supply_mode  weight_name                                   weight_value
# default constant
all        other   transit          transit        rapid_bus    wait_time_min                                 1.77

# Explicitly constant
all        other   transit          transit        rapid_bus    wait_time_min.constant                        1.77

all        other   access           walk           walk_access  depart_early_min.logistic                     0.2
all        other   access           walk           walk_access  depart_early_min.logistic.logistic_max        10
all        other   access           walk           walk_access  depart_early_min.logistic.logistic_mid        9

all        other   egress           walk           walk_egress  arrive_late_min.logarithmic                   0.3
all        other   egress           walk           walk_egress  arrive_late_min.logarithmic.log_base          2.71828

# Exponential
all        work    access           walk           walk_access  depart_early_min.exponential                  0.02

# Logarithmic
all        other   egress           walk           walk_egress  arrive_late_min.logarithmic                   0.3
all        other   egress           walk           walk_egress  arrive_late_min.logarithmic.log_base          2.71828


GTFS-PLUS fare inputs are similar to GTFS fare inputs but with additional fare periods for time period-based fares.

However, because the columns route_id, origin_id, destination_id and contains_id are all optional in fare_rules.txt and therefore may be specified in different combinations, fast-trips implements fares with the following rules:

  • contains_id is not implemented in fast-trips, and its inclusion will result in an error
  • Specifying origin_id and not destination_id or vice versa will result in an error. Each fare rule must specify both or neither.
  • These combinations of route_id, origin_id, and destination_id will be used to match a fare_id to a transit trip, in this order. The first match will win.
    • Matching route_id, origin_id and destination_id
    • Matching route_id only (no origin_id or destination_id specified)
    • Matching origin_id and destination_id only (no route_id specified)
    • No match (e.g. fare_id specified with no other columns)

Discount and free transfers specified in fare_transfer_rules_ft.txt are applied to transfers from one fare period to another fare period, and these links need to be back-to-back. So if a passenger transfers from A to B to C and the discount is specified for fare period A to fare period C, they will not receive the discount.

Free transfers are also specified within fare periods (possibly time-bounded) in fare_attributes_ft.txt. These free transfers are applied after the discounts from fare_transfer_rules_ft.txt and they do not need to be back-to-back. So if a passenger transfers from A to B to A and fare period A has 1 free transfer specified, but a transfer from B to A has a transfer fare of $.50, the passenger will receive the free transfer since these rules are applied last (and override).

There are four places where fares factor into fast-trips.

  1. During path-finding (C++ extension), fares get assessed as a cost onto links, which translate to generalized cost (minutes) via the traveler's value of time. Fare transfer rules here are complicated, because we don't know which is the next/previous fare, and we can only guess based on probabilities. The fare is estimated using Hyperlink::getFareWithTransfer().

    Free transfers as configured in fare attributes are implemented here in a simplistic way; that is, a free transfer is assumed if the fare attributes have granted any free transfers without looking at transfer_duration or the number of transfers. Also, this transfer is required to be back-to-back also. A future enhancement could include keeping a transfer count for each fare period so that the back-to-back requirement is not imposed, and also so that a certain number of free fares could be tallied, but at this time, a simpler approach is used because it's not clear if this kind of detail is helpful.

    Turn this off using configuration option transfer_fare_ignore_pathfinding.

  2. During path-enumeration (C++ extension), when the paths are being constructed by choosing links from the hyperpath graph, at the point where each link is added to the path, the fare transfer rules are applied to adjust fares with more certainty of the the path so far. This is done in Hyperlink::setupProbabilities() which calls Hyperlink::updateFare() and updates the link cost as well if the fare is affected. Free transfers as configured in fare attributes are looked at here as well, but without the transfer duration component.

  3. During path-enumeration (C++ extension), after the path is constructed, the trip cost is re-calculated at the end using Path::calculateCost(). At this moment in the process, the path is complete and final, so the fare transfer rules are relatively easy to apply given that links are certain. The initial fare and cost are saved and passed back to python to show the effect of step 1.

    Free transfers as configured in fare attributes are also addressed here.

    Turn this off using configuration option transfer_fare_ignore_pathenum.

  4. During simulation (python), while the path is being adjusted due to vehicle times, the fares are calculated via Route.add_fares(). This is unlikely to change anything unless the fare periods changed due to the slow-down of vehicles -- so consider deprecating this in favor of using the pathfinding results? For now, it's a good test that the C++ code is working as expected; running with simulation off should result in identical fare and cost results from pathfinding and the (non-vehicle-updating) python simulation.

Running Fast-Trips

Fast-Trips can be run from the command line or by calling it from within a Python script or an iPython notebook using the Run.run_fasttrips() function.

There are six required parameters that need to either be passed from the command line or the function call:

  • input_network_dir = directory for input networks can be found
  • input_demand_dir = directory where input demand can be found
  • input_weights = file where path weights can be found
  • run_config = file where run configurations can be found
  • iters = Number of global iterations
  • output_dir = directory where output folder is created
  • pathfinding_type = either deterministic or stochastic

All the other parameters described in the configuration options can also be passed as keywords.

NOTE: Any parameters passed in at run-time from the command line or via the script will overwrite any parameters read in from the run_config file.

Running the Springfield Example

Sample input files have been provided in <fast-trips-dir>\Examples\Springfield to test the setup and also assist with the creation of new fast-trips runs. The input files include network files created from a small hypothetical network and also example transit demand data.

From a Script

# Examples\Springfield\run_springfield.py

import os
from fasttrips import Run

EXAMPLE_DIR         = os.path.abspath(os.path.dirname(__file__))

INPUT_NETWORK       = os.path.join(EXAMPLE_DIR, 'networks', 'vermont')
INPUT_DEMAND        = os.path.join(EXAMPLE_DIR, 'demand', 'general')
INPUT_CONFIG        = os.path.join(EXAMPLE_DIR, 'configs', 'A')
OUTPUT_DIR          = os.path.join(EXAMPLE_DIR, 'output')
OUTPUT_FOLDER       = "general_run"

CONFIG_FILE         = os.path.join(INPUT_CONFIG, 'config_ft.txt')
INPUT_WEIGHTS       = os.path.join(INPUT_CONFIG, 'pathweight_ft.txt')

print "Running Fast-Trips in %s" % (ex_dir.split(os.sep)[-1:])

    input_network_dir= INPUT_NETWORK,
    input_demand_dir = INPUT_DEMAND,
    run_config       = CONFIG_FILE,
    input_weights    = INPUT_WEIGHTS,
    output_dir       = OUTPUT_DIR,
    output_folder    = OUTPUT_FOLDER,
    pathfinding_type = "stochastic",
    overlap_variable = "count",
    overlap_split_transit = True,
    iters            = 1,
    dispersion       = 0.50)

To run the example:

  • Make sure your <fast-trips-dir> is in your PYTHONPATH environment variable in Advanced system settings [Win] or terminal [OSX].
  • Run python Examples/Springfield/run_springfield.py from within <fast-trips-dir>\scripts in a command prompt [ Win ] or terminal [ OSX ].

Output files from running fast-trips with the sample input data provided can be found in the Springfield/output directory.

From Command Line

The same example can be run from the command line by using the command from within the <fast-trips-dir> directory:

C:\Users\lzorn\Documents\fast-trips>rem See usage and forgive my use of windows
C:\Users\lzorn\Documents\fast-trips>rem If using installed version, use 'run_fasttrips' instead of 'python fasttrips\Run.py'
C:\Users\lzorn\Documents\fast-trips>python fasttrips\Run.py -h

  Run Fast-Trips from the command line with required inputs as command line parameters.

positional arguments:
                        Type of pathfinding
  iters                 Number of iterations to run
  run_config            The run configuration file
  input_network_dir     Location of the input network
  input_demand_dir      Location of the input demand
  input_weights         Location of the pathweights file
  output_dir            Location to write fasttrips output

optional arguments:
  -h, --help            show this help message and exit
  -t, --trace_only      Run only the trace persons?
  -n NUM_TRIPS, --num_trips NUM_TRIPS
                        Number of person trips to run, to run a subset of the
                        whole demand.
  -d DISPERSION, --dispersion DISPERSION
                        Stochastic dispersion parameter
  -m MAX_STOP_PROCESS_COUNT, --max_stop_process_count MAX_STOP_PROCESS_COUNT
                        Max times to process a stop in stochastic pathfinding
  -c, --capacity        Enable capacity constraint
  -o OUTPUT_FOLDER, --output_folder OUTPUT_FOLDER
                        Directory within output_loc to write fasttrips
                        outtput. If none specified, will construct one.
                        Include debug columns in output
  --overlap_variable {None,count,distance,time}
                        Variable to use for overlap penalty calculation
                        Split transit for path overlap penalty calculation
                        In path-finding, suppress trying to adjust fares using
                        transfer rules. For performance.
                        In path-enumeration, suppress trying to adjust fares
                        using transfer rules. For performance.

C:\Users\lzorn\Documents\fast-trips>rem Run it with Springfield Example scenario
C:\Users\lzorn\Documents\fast-trips>rem If using installed version, use 'run_fasttrips' instead of 'python fasttrips\Run.py'

C:\Users\lzorn\Documents\fast-trips>python fasttrips\Run.py stochastic 1 fasttrips\Examples\Springfield\configs\A\config_ft.txt fasttrips\Examples\Springfield\networks\vermont fasttrips\Examples\Springfield\demand\general fasttrips\Examples\Springfield\configs\A\pathweight_ft.txt fasttrips\Examples\test_scenario\output

Example Scenarios

Fast-Trips comes with a handful of scenarios in the fasttrips/Examples directory to use as examples or get you started. They can be viewed at a high-level using the jupyter notebooks contained in that directory. Note that these notebooks may require you to install additional Python packages such as jupyter, ipywidgets, and bokeh.


The Springfield scenario is what many of our tests use and is meant to be a generic example with enough complexity and modes to flex Fast-Trips muscles, but not too complex to understand what is going on.

Springfield Network

The hypothetical 5-zone example network was developed to help code development. It has a total of three transit routes (one rail and two bus) with two or three stops each. There are also two park-and-ride (PnR) locations.

alt text

Transit vehicles commence at 3:00 PM and continue until 6:00 PM. There are 152 transit trips that make a total of 384 station stops. The input folder contains all the supply-side/network input files prepared from the test network. More information about network input file standards can be found in the GTFS-Plus Data Standards Repository.

Springfield Demand

Two versions of sample demand have been prepared:

  • general contains regular demand that consists only of a transit trip list. Demand starts at 3:15 PM and ends at 5:15 PM.One trip occurs every 10 seconds. More information is available in documentation.
  • simpson_zorn represents demand for two user classes that can use different sets of path weights. Household and person attribute files are present in addition to the trip list to model user heterogeneity and multiple user classes.

Similar to network data standards, there also exists a Demand Data Standards Repository.

Springfield Configs

There are several configurations for the Springfield setup, which are generally grouped as:

  • A which doesn't use user classes, and
  • B which uses user classes and thus needs to use the simpson_zorn demand


There are a couple dozen tests that are stored in \tests. They can be run by installing the PyTest library (pip install pytestand executing the command pytest from the command line within your <fast-trips-dir>.

Most of the tests use test scenarios that can be found in the fasttrips/Examples directory.

Many (but not all) of the tests can be individually run by giving the command pytest tests/test_<TESTNAME>.py.

Test output defaults to the folder fasttrips/Examples/output

Continuous Integration

We use the Travis-CI continuous integration service as follows:

  • Every push to GitHub will run tests denoted by the @pytest.mark.test.basic function decorator, which is a small subset of system level tests.
  • Every push to master or develop branches will run tests denoted by the @pytest.mark.test.travis function decorator.

These subsets were created to limit the time it takes for Travis to run all the tests. When doing invasive development, they are not a substitute for running the entire test suite locally using the py.test command.

Additionally, it is important to understand that most of the tests are system-level tests that do not guarantee correct results so much as they make sure the system runs without an error.

For documentation-only commits, put skip ci somewhere in your commit message to not trigger the Travis testing.

Some regression tests have regression output that needs to be refreshed an thus have a function decorator @pytest.mark.skip so that they are skipped.

Test Descriptions

  • Assignment Type: test_assignment_type.py
    • Tests both deterministic and stochastic shortest path and hyperpaths.
  • Simple Bunny Hop Scenario: test_bunny.py
    • Tests forward and backward stochastic hyperpaths as well as a sensitivity test with a different network. Has the basic and travis label, so it runs with every push.
  • Calculate Cost: test_calculate_cost.py
    • Regression tests of cost calculations.
  • Convergence: test_convergence.py
    • Tests convergence.
    • Status: SKIP
  • Cost Symmetry: test_calculate_cost.py
    • Tests that the costs from the c++ pathfinding and the python calculate cost functions return the same values.
    • Status: Manual
  • Dispersion Levels: test_dispersion.py
    • Runs dispersion levels at .0, 0.5, 0.1
    • Status: Run on develop and master branch commits
  • Distance Calculation: test_distance.py
    • Status: Out of date
  • Fares: test_fares.py
    • Tests shortcuts in fare calculations
    • Ignore Pathfinding
    • Ignore Pathfinding and Path Enumeration
    • Status: Run on develop and master branch commits
  • Feedback: test_feedback.py
    • Runs demand for three iterations with and without capacity constraint
    • Status: Run on develop and master branch commits
  • GTFS: test_gtfs_objects.py
    • Tests that we can read and process GTFS-PLUS.
    • Status: Manual
  • Max Stop Process Count: test_maxStopProcessCount.py
    • Tests 10, 50, and 100 for the value of max stop process count – the maximum number of times you will re-processe a node (default: None)
    • Status: Manual
  • Overlap Functions: test_overlap.py
  • Tests both overlap variables (count, distance and time) and whether or not each transit segment is broken and compared to each of its parts.
  • Status: Run on develop and master branch commits
  • Flexible Departure/Arrival Windows: test_pat_variation.py
    • Tests that flexible departure and arrival window penalties are working.
    • Status: Run on develop and master branch commits
  • Penalty Functions: test_penalty_functions.py
    • Tests that penalty functions for flexible departure and arrival windows work.
    • Status: Run on develop and master branch commits
  • Regional Network: test_psrc.py
    • Tests that things work on a large, regional network.
    • Status: Run on develop and master branch commits
  • User Classes: test_user_classes.py
    • Uses multiple user classes as defined in config_ft.py
    • Status: Manual
  • Function Transformations: test_weight_qualifiers.py
    • Uses multiple user classes as defined in config_ft.py
    • Status: Run on develop and master branch commits

Note: Multiprocessing is not tested because it is incompatible with PyTest

Summarizing Results

Fast-Trips will output results in the dyno-path format. If there exists a survey with transit path details that can be converted to the dyno-path format as well, Fast-Trips results can be summarized and validated against the survey observations. One such effort has been made to validate Fast-Trips output against two surveys in San Francisco Bay Area - Transit On-Board Survey (OBS) and California Household Travel Survey (CHTS).

Creating Validation Metrics Tableau Dashboard

The scripts and detailed instructions to process the surveys and generate summary dashboards in Tableau can be found in the fast-trips-validation repository.

Creating Path Visualizer Tableau Dashboard


Frequently Asked Questions

  • How do I restart a run after pathfinding?

Use the option pathfinding_type=file, via runTest.py or in the configuration. Then, drop the pathsfound_paths.csv and pathsfound_links.csv files in the output directory for your run, and they'll be read in instead of generated.


  • Ramming, M. S. Network Knowledge and Route Choice. Ph.D. Thesis. Massachusetts Institute of Technology, Cambridge, Mass., 2002.

  • Hoogendoorn-Lanser, S., R. Nes, and P. Bovy. Path Size Modeling in Multinomial Route Choice Analysis. 27 In Transportation Research Record: Journal of the transportation Research Board, No 1921, 28 Transportation Research Board of the National Academies, Washington, D.C., 2005, pp. 27-34.


Major changes to fast-trips since the original FAST-TrIPs (https://github.com/MetropolitanTransportationCommission/FAST-TrIPs-1)

To be filled in further but including:

  • Added pathfinding iterations to looping (so pathfinding_iteration=1 finds paths for everyone, and subsequently just find paths for people who don't have a valid path. Break when max or we don't find anymore)
  • Added time-period based drive access links (10/2016)
  • Added link distance to extension as part of StopState (10/2016)
  • Implemented overlap pathsize correction (8/2016)
  • Add purpose segmentation to cost weighting (7/2016)
  • Output pathsets in addition to chosen paths (4/2016)
  • Update transit trip vehicle times based on boards, alights and vehicle-configured accleration, deceleration and dwell formulas (4/2016)
  • Output performance measures (pathfinding and path enumeration times, number of stops processed) (3/2016)
  • Stop order update to pathfinding: when a stop state is updated, mark other reachable stops for reprocessing (3/2016) details
  • Support KNR and PNR access (11/2015)
  • Read user-class based cost weighting (11/2015)
  • Switch input format to GTFS-plus network (10/2015)
  • Move path finding to C++ extension (9/2015)
  • Parallelized path finding with multiprocessing (7/2015)
  • Port original FAST-TrIPs codebase to python with debug tracing (5/2015)