Configuring OpenTripPlanner

Base Directory

On the OTP2 command line you must always specify a single directory after all the switches. This tells OTP2 where to look for any configuration files, as well as the input files to build a graph (GTFS, OSM, elevation, and base street graphs) or the graph.obj file to load when starting a server.

A typical OTP2 directory for a New York City graph might include the following:

otp-config.json
build-config.json
router-config.json
new-york-city-no-buildings.osm.pbf
nyc-elevation.tiff
long-island-rail-road.gtfs.zip
mta-new-york-city-transit.gtfs.zip
port-authority-of-new-york-new-jersey.gtfs.zip
graph.obj

You could have more than one of these directories if you are building separate graphs for separate regions. Each one should contain one or more GTFS feeds (which are just zip files full of comma-separated text tables), a PBF OpenStreetMap file, some JSON configuration files, and any output files such as graph.obj. For convenience, especially if you work with only one graph at a time, you may want to place your OTP2 JAR file in this same directory.

Three Scopes of Configuration

OTP is configured via three configuration JSON files which are read from the directory specified on its command line. We try to provide sensible defaults for every option, so all three of these files are optional, as are all the options within each file. Each configuration file corresponds to options that are relevant at a particular phase of OTP usage.

Options and parameters that are taken into account during the graph building process will be "baked into" the graph, and cannot be changed later in a running server. These are specified in build-config.json. Other details of OTP operation can be modified without rebuilding the graph. These run-time configuration options are found in router-config.json. Finally, otp-config.json contains simple switches that enable or disable system-wide features.

Configuration types

The OTP configuration files use the JSON file format. OTP allows comments and unquoted field names in the JSON configuration files to be more human-friendly. OTP supports all the basic JSON types: nested objects {...}, arrays [], numbers 789.0 and boolean true | false. In addition to these basic types some configuration parameters are parsed with some restrictions. In the documentation below we will refer to the following types:

Type Description Examples
boolean This is the Boolean JSON type. true or false
number This is the Number JSON type. 1, 5, 3.14
string A quoted string. This is the String JSON type. "This is a string!"
Type[] Array of of given Type. This is the Array JSON type. [ 1, 2, 3 ]
double A decimal floating point number. 64 bit. 3.14
integer A decimal integer number. 32 bit. 1, -7, 2100200300
long A decimal integer number. 64 bit. -1234567890123456789
enum A fixed set of string literals. BicycleOptimize: "QUICK", "SAFE" ...
enum-map List of key/value pairs, where the key is a enum and the value can be any given type. { RAIL: 1.2, BUS: 2.3 }
enum-set List of enum string values [ "RAIL", "TRAM" ]
locale Language[\_country[\_variant]]. A Locale object represents a specific geographical, political, or cultural region. For more information see the Java 11 Locale. en_US, nn_NO
date Local date. The format is YYYY-MM-DD (ISO-8601). 2020-09-21
date or period A local date, or a period relative to today. The local date has the format YYYY-MM-DD and the period has the format PnYnMnD or -PnYnMnD where n is a integer number. P1Y is one year from now, -P3M2D means 3 months and 2 days ago, and P1D means tomorrow.
regexp pattern A regular expression pattern used to match a sting. "$^" matches an empty string. "gtfs" matches "A-*gtfs*-file.zip". "$\w{3})-.*\.xml^" matches a filename with 3 alpha-numeric characters in the beginning of the filename and .xml as file extension.
uri An URI path to a resource like a file or a URL. "gs://bucket/path/a.obj" "http://foo.bar/" `"file:///Users/x/local/file"
linear function A linear function with one input parameter(x) used to calculate a value. Usually used to calculate a limit. For example to calculate a limit in seconds to be 1 hour plus 2 times the value(x) use: 3600 + 2.0 x, to set an absolute value(3000) use: 3000 + 0x "600 + 2.0 x"

System-wide Configuration

Using the file otp-config.json you can enable or disable different APIs and experimental Sandbox Extensions. By default, all supported APIs are enabled and all sandbox features are disabled. So for most OTP2 use cases it is not necessary to create this file. Features that can be toggled in this file are generally only affect the routing phase of OTP2 usage, but for consistency all such "feature flags", even those that would affect graph building, are managed in this one file. See the OTPFeature Java class for an enumeration of all available features and their default settings. Here is an example:

// otp-config.json
{
    otpFeatures : {
        APIBikeRental : false,
        SandboxExampleAPIGraphStatistics : true
    }
}

Graph Build Configuration

This table lists the possible settings that can be defined in a build-config.json file. These will be stored in the graph itself, and affect any server that subsequently loads that graph. Sections follow that describe particular settings in more depth.

config key description value type value default notes
`` TODO boolean false
areaVisibility Perform visibility calculations. If this is true OTP attempts to calculate a path straight through an OSM area using the shortest way rather than around the edge of it. (These calculations can be time consuming). boolean false
banDiscouragedWalking should walking should be allowed on OSM ways tagged with foot=discouraged" boolean false
banDiscouragedBiking should walking should be allowed on OSM ways tagged with bicycle=discouraged" boolean false
dataImportReport Generate nice HTML report of Graph errors/warnings boolean false
distanceBetweenElevationSamples TODO OTP2 double 10
elevationBucket If specified, download NED elevation tiles from the given AWS S3 bucket object null provide an object with accessKey, secretKey, and bucketName for AWS S3
elevationUnitMultiplier Specify a multiplier to convert elevation units from source to meters double 1.0 see Elevation unit conversion
embedRouterConfig Embed the Router config in the graph, which allows it to be sent to a server fully configured over the wire boolean true
extraEdgesStopPlatformLink add extra edges when linking a stop to a platform, to prevent detours along the platform edge boolean false
fares A specific fares service to use object null see fares configuration
fetchElevationUS Download US NED elevation data and apply it to the graph boolean false
islandWithStopsMaxSize Pruning threshold for islands with stops. Any such island under this size will be pruned int 5
islandWithoutStopsMaxSize Pruning threshold for islands without stops. Any such island under this size will be pruned int 40
matchBusRoutesToStreets Based on GTFS shape data, guess which OSM streets each bus runs on to improve stop linking boolean false
maxDataImportIssuesPerFile If number of data import issues is larger then specified maximum number of issues the report will be split in multiple files int 1,000
maxInterlineDistance Maximal distance between stops in meters that will connect consecutive trips that are made with same vehicle int 200 units: meters
maxTransferDistance Transfers up to this length in meters will be pre-calculated and included in the Graph double 2,000 units: meters
multiThreadElevationCalculations If true, the elevation module will use multi-threading during elevation calculations. boolean false see Elevation Data Calculation Optimizations
osmNaming A custom OSM namer to use object null see custom naming
osmWayPropertySet Custom OSM way properties string default options: default, finland, norway, uk
parentStopLinking Link GTFS stops to their parent stops boolean false
platformEntriesLinking Link unconnected entries to public transport platforms boolean false
readCachedElevations If true, reads in pre-calculated elevation data. boolean true see Elevation Data Calculation Optimizations
staticBikeParkAndRide Whether we should create bike P+R stations from OSM data boolean false
staticBikeRental Whether bike rental stations should be loaded from OSM, rather than periodically dynamically pulled from APIs boolean false
staticParkAndRide Whether we should create car P+R stations from OSM data boolean true
stationTransfers Create direct transfers between the constituent stops of each parent station boolean false
streets Include street input files (OSM/PBF) boolean true
storage Configure access to data sources like GRAPH/OSM/DEM/GTFS/NETEX/ISSUE-REPORT. object null
subwayAccessTime Minutes necessary to reach stops served by trips on routes of route_type=1 (subway) from the street double 2.0 units: minutes
transit Include all transit input files (GTFS) from scanned directory boolean true
transitServiceStart Limit the import of transit services to the given start date. Inclusive. Use an absolute date or a period relative to the day the graph is build. To specify a week before the build date use a negative period like -P1W. date or period −P1Y 2020‑01‑01, −P1M3D, −P3W
transitServiceEnd Limit the import of transit services to the given end date. Inclusive. Use an absolute date or a period relative to the day the graph is build. date or period P3Y 2022‑12‑31, P1Y6M10D, P12W
useTransfersTxt Create direct transfer edges from transfers.txt in GTFS, instead of based on distance boolean false
writeCachedElevations If true, writes the calculated elevation data. boolean false see Elevation Data Calculation Optimizations

This list of parameters in defined in the BuildConfig.java.

Storage

Nested inside storage {...} in build-config.json.

Using other data-sources than the local file system is new in OTP2. This allow for access to cloud based storage as well as using local disk. If you run OTP in the cloud you might get faster start-up and build times if you use the cloud storage instead of copying the files, it also simplefy the deplyment. Nested storage build-config.

See (StorageConfig.java)[https://github.com/opentripplanner/OpenTripPlanner/blob/2.0-rc/src/main/java/org/opentripplanner/standalone/config/StorageConfig.java] for up-to-date detailed description of each config parameter. Here is an overview:

config key description value type value default
gsCredentials Use an environment variable to point to the Google Cloud credentials: "${MY_GOC_SERVICE}". string null
graph Absolute path to the graph file. uri null
streetGraph Absolute path to the street-graph file. uri null
osm List of absolute paths of Open Street Map files to build. uri [] null
dem List of absolute paths of Elevation DEM files to build. uri [] null
gtfs List of GTFS transit data files to build. uri [] null
netex List of NeTEx transit data files to build. uri [] null
buildReportDir Path to directory for the build issue report generated by OTP. uri null
localFileNamePatterns Patterns to use for auto resolving local filenames to input data types. object null

Local Filename Patterns

Nested inside storage : { localFileNamePatterns : { ... } } in build-config.json.

config key description value type value default
osm Pattern used to match Open Street Map files on local disk regexp pattern (?i)(\.pbf)
dem Pattern used to match Elevation DEM files on local disk regexp pattern (?i)\.tiff?$
gtfs Pattern used to match GTFS files on local disk regexp pattern (?i)gtfs
netex Pattern used to match NeTEx files on local disk regexp pattern (?i)netex

Storage example:

storage : {
  // Use the GCS_SERVICE_CREDENTIALS environment variable to locate GCS credentials
  gsCredentials: "${GCS_SERVICE_CREDENTIALS}",
  streetGraph: "file:///Users/kelvin/otp/streetGraph.obj",
  osm: ["gs://bucket-name/shared-osm-file.pbf"]
  localFileNamePatterns: {
    // All filenames that start with "g-" and end with ".zip" is imported as a GTFS file.
    gtfs : "^g-.*\.zip$", 
  }
}

Limit the transit service period

The properties transitServiceStart and transitServiceEnd can be used to limit the service dates. This affects both GTFS service calendars and dates. The service calendar is reduced and dates outside the period are dropped. OTP2 will compute a transit schedule for every day for which it can find at least one trip running. On the other hand, OTP will waste resources if a service end date is unbounded or very large (9999-12-31). To avoid this, limit the OTP service period. Also, if you provide a service with multiple feeds they may have different service end dates. To avoid inconsistent results, the period can be limited, so all feeds have data for the entire period. The default is to use a period of 1 year before, and 3 years after the day the graph is built. Limiting the period will not improve the search performance, but OTP will build faster and load faster in most cases.

The transitServiceStart and transitServiceEnd parameters are set using an absolute date like 2020-12-31 or a period like P1Y6M5D relative to the graph build date. Negative periods is used to specify dates in the past. The period is computed using the system time-zone, not the feed time-zone. Also, remember that the service day might be more than 24 hours. So be sure to include enough slack to account for the this. Setting the limits too wide have very little impact and is in general better than trying to be exact. The period and date format follow the ISO 8601 standard.

Reaching a subway platform

The ride locations for some modes of transport such as subways and airplanes can be slow to reach from the street. When planning a trip, we need to allow additional time to reach these locations to properly inform the passenger. For example, this helps avoid suggesting short bus rides between two subway rides as a way to improve travel time. You can specify how long it takes to reach a subway platform

// build-config.json
{
  "subwayAccessTime": 2.5
}

Stops in GTFS do not necessarily serve a single transit mode, but in practice this is usually the case. This additional access time will be added to any stop that is visited by trips on subway routes (GTFS route_type = 1).

This setting does not generalize well to airplanes because you often need much longer to check in to a flight (2-3 hours for international flights) than to alight and exit the airport (perhaps 1 hour). Therefore there is currently no per-mode access time, it is subway-specific.

Transferring within stations

Subway systems tend to exist in their own layer of the city separate from the surface, though there are exceptions where tracks lie right below the street and transfers happen via the surface. In systems where the subway is quite deep and transfers happen via tunnels, the time required for an in-station transfer is often less than that for a surface transfer. A proposal was made to provide detailed station pathways in GTFS but it is not in common use.

One way to resolve this problem is by ensuring that the GTFS feed codes each platform as a separate stop, then micro-mapping stations in OSM. When OSM data contains a detailed description of walkways, stairs, and platforms within a station, GTFS stops can be linked to the nearest platform and transfers will happen via the OSM ways, which should yield very realistic transfer time expectations. This works particularly well in above-ground train stations where the layering of non-intersecting ways is less prevalent. Here's an example in the Netherlands:

View Larger Map When such micro-mapping data is not available, we need to rely on information from GTFS including how stops are grouped into stations and a table of transfer timings where available. During the graph build, OTP can create preferential connections between each pair of stops in the same station to favor in-station transfers:

// build-config.json
{
  "stationTransfers": true
}

Note that this method is at odds with micro-mapping and might make some transfers artificially short.

Elevation data

OpenTripPlanner can "drape" the OSM street network over a digital elevation model (DEM). This allows OTP to draw an elevation profile for the on-street portion of itineraries, and helps provide better routing for bicyclists. It even helps avoid hills for walking itineraries. DEMs are usually supplied as rasters (regular grids of numbers) stored in image formats such as GeoTIFF.

U.S. National Elevation Dataset

In the United States, a high resolution National Elevation Dataset is available for the entire territory. The US Geological Survey (USGS) delivers this dataset in tiles via a somewhat awkward heavyweight web-based GIS which generates and emails you download links. OpenTripPlanner contains a module which will automatically contact this service and download the proper tiles to completely cover your transit and street network area. This process is rather slow (download is around 1.5 hours, then setting elevation for streets takes about 5 minutes for the Portland, Oregon region), but once the tiles are downloaded OTP will keep them in local cache for the next graph build operation.

To auto-download NED tiles when building your graph, add the following line to build-config.json in your router directory:

// build-config.json
{
  "fetchElevationUS": true
}

You may also want to add the --cache <directory> command line parameter to specify a custom NED tile cache location.

NED downloads take quite a long time and slow down the graph building process. The USGS will also deliver the whole dataset in bulk if you send them a hard drive. OpenTripPlanner contains another module that will then automatically fetch data in this format from an Amazon S3 copy of your bulk data. You can configure it as follows in build-config.json:

{
    "elevationBucket" : {
        "accessKey" : "your-aws-access-key",
        "secretKey" : "corresponding-aws-secret-key",
        "bucketName" : "ned13"
    }
}

Geoid Difference

With some elevation data, the elevation values are specified as relative to the a geoid (irregular estimate of mean sea level). See issue #2301 for detailed discussion of this. In these cases, it is necessary to also add this geoid value onto the elevation value to get the correct result. OTP can automatically calculate these values in one of two ways.

The first way is to use the geoid difference value that is calculated once at the center of the graph. This value is returned in each trip plan response in the ElevationMetadata field. Using a single value can be sufficient for smaller OTP deployments, but might result in incorrect values at the edges of larger OTP deployments. If your OTP instance uses this, it is recommended to set a default request value in the router-config.json file as follows:

// router-config.json
{
    "routingDefaults": {
        "geoidElevation ": true   
    }
}

The second way is to precompute these geoid difference values at a more granular level and include them when calculating elevations for each sampled point along each street edge. In order to speed up calculations, the geoid difference values are calculated and cached using only 2 significant digits of GPS coordinates. This is more than enough detail for most regions of the world and should result in less than one meter of difference in areas that have large changes in geoid difference values. To enable this, include the following in the build-config.json file:

// build-config.json
{
  "includeEllipsoidToGeoidDifference": true
}

If the geoid difference values are precomputed, be careful to not set the routing resource value of geoidElevation to true in order to avoid having the graph-wide geoid added again to all elevation values in the relevant street edges in responses.

Other raster elevation data

For other parts of the world you will need a GeoTIFF file containing the elevation data. These are often available from national geographic surveys, or you can always fall back on the worldwide Space Shuttle Radar Topography Mission (SRTM) data. This not particularly high resolution (roughly 30 meters horizontally) but it can give acceptable results.

Simply place the elevation data file in the directory with the other graph builder inputs, alongside the GTFS and OSM data. Make sure the file has a .tiff or .tif extension, and the graph builder should detect its presence and apply the elevation data to the streets.

OTP should automatically handle DEM GeoTIFFs in most common projections. You may want to check for elevation-related error messages during the graph build process to make sure OTP has properly discovered the projection. If you are using a DEM in unprojected coordinates make sure that the axis order is (longitude, latitude) rather than (latitude, longitude). Unfortunately there is no reliable standard for WGS84 axis order, so OTP uses the same axis order as the above-mentioned SRTM data, which is also the default for the popular Proj4 library.

DEM files(USGS DEM) is not supported by OTP, but can be converted to GeoTIFF with tools like GDAL. Use gdal_merge.py -o merged.tiff *.dem to merge a set of dem files into one tif file.

See Interline PlanetUtils for a set of scripts to download, merge, and resample Mapzen/Amazon Terrain Tiles.

Elevation unit conversion

By default, OTP expects the elevation data to use metres. However, by setting elevationUnitMultiplier in build-config.json, it is possible to define a multiplier that converts the elevation values from some other unit to metres.

// build-config.json
{
  // Correct conversation multiplier when source data uses decimetres instead of metres
  "elevationUnitMultiplier": 0.1
}

Elevation Data Calculation Optimizations

Calculating elevations on all StreetEdges can take a dramatically long time. In a very large graph build for multiple Northeast US states, the time it took to download the elevation data and calculate all of the elevations took 5,509 seconds (roughly 1.5 hours).

If you are using cloud computing for your OTP instances, it is recommended to create prebuilt images that contain the elevation data you need. This will save time because all of the data won't need to be downloaded.

However, the bulk of the time will still be spent calculating elevations for all of the street edges. Therefore, a further optimization can be done to calculate and save the elevation data during a graph build and then save it for future use.

Reusing elevation data from previous builds

In order to write out the precalculated elevation data, add this to your build-config.json file:

// build-config.json
{  
  "writeCachedElevations": true
}

After building the graph, a file called cached_elevations.obj will be written to the cache directory. By default, this file is not written during graph builds. There is also a graph build parameter called readCachedElevations which is set to true by default.

In graph builds, the elevation module will attempt to read the cached_elevations.obj file from the cache directory. The cache directory defaults to /var/otp/cache, but this can be overriden via the CLI argument --cache <directory>. For the same graph build for multiple Northeast US states, the time it took with using this predownloaded and precalculated data became 543.7 seconds (roughly 9 minutes).

The cached data is a lookup table where the coordinate sequences of respective street edges are used as keys for calculated data. It is assumed that all of the other input data except for the OpenStreetMap data remains the same between graph builds. Therefore, if the underlying elevation data is changed, or different configuration values for elevationUnitMultiplier or includeEllipsoidToGeoidDifference are used, then this data becomes invalid and all elevation data should be recalculated. Over time, various edits to OpenStreetMap will cause this cached data to become stale and not include new OSM ways. Therefore, periodic update of this cached data is recommended.

Configuring multi-threading during elevation calculations

For unknown reasons that seem to depend on data and machine settings, it might be faster to use a single processor. For this reason, multi-threading of elevation calculations is only done if multiThreadElevationCalculations is set to true. To enable multi-threading in the elevation module, add the following to the build-config.json file:

// build-config.json
{  
  "multiThreadElevationCalculations": true
}

Fares configuration

By default OTP will compute fares according to the GTFS specification if fare data is provided in your GTFS input. It is possible to turn off this by setting the fare to "off". For more complex scenarios or to handle bike rental fares, it is necessary to manually configure fares using the fares section in build-config.json. You can combine different fares (for example transit and bike-rental) by defining a combinationStrategy parameter, and a list of sub-fares to combine (all fields starting with fare are considered to be sub-fares).

// build-config.json
{
  // Select the custom fare "seattle"
  "fares": "seattle",
  // OR this alternative form that could allow additional configuration
  "fares": {
    "type": "seattle"
  }
}
// build-config.json
{
  "fares": {
    // Combine two fares by simply adding them
    "combinationStrategy": "additive",
    // First fare to combine
    "fare0": "new-york",
    // Second fare to combine
    "fare1": {
      "type": "bike-rental-time-based",
      "currency": "USD",
      "prices": {
          // For trip shorter than 30', $4 fare
          "30":   4.00,
          // For trip shorter than 1h, $6 fare
          "1:00": 6.00
      }
    }
    // We could also add fareFoo, fareBar...
  }
}

Turning the fare service off, this will ignore any fare data in the provided GTFS data.

// build-config.json
{
  "fares": "off"
}

The current list of custom fare type is:

The current list of combinationStrategy is:

OSM / OpenStreetMap configuration

It is possible to adjust how OSM data is interpreted by OpenTripPlanner when building the road part of the routing graph.

Way property sets

OSM tags have different meanings in different countries, and how the roads in a particular country or region are tagged affects routing. As an example are roads tagged with `highway=trunk (mainly) walkable in Norway, but forbidden in some other countries. This might lead to OTP being unable to snap stops to these roads, or by giving you poor routing results for walking and biking. You can adjust which road types that are accessible by foot, car & bicycle as well as speed limits, suitability for biking and walking.

There are currently following wayPropertySets defined;

To add your own custom property set have a look at org.opentripplanner.graph_builder.module.osm.NorwayWayPropertySet and org.opentripplanner.graph_builder.module.osm.DefaultWayPropertySet. If you choose to mainly rely on the default rules, make sure you add your own rules first before applying the default ones. The mechanism is that for any two identical tags, OTP will use the first one.

// build-config.json
{
  osmWayPropertySet: "norway"
}

Custom naming

You can define a custom naming scheme for elements drawn from OSM by defining an osmNaming field in build-config.json, such as:

// build-config.json
{
  "osmNaming": "portland"
}

There is currently only one custom naming module called portland (which has no parameters).

Router configuration

This section covers all options that can be set for each router using the router-config.json file. These options can be applied by the OTP server without rebuilding the graph.

config key description value type value default notes
routingDefaults Default routing parameters, which will be applied to every request object see routing defaults
streetRoutingTimeout maximum time limit for street route queries double null units: seconds; see timeout
requestLogFile Path to a plain-text file where requests will be logged string null see logging incoming requests
transit Transit tuning parameters TransitRoutingConfig see Tuning transit routing
updaters configure real-time updaters, such as GTFS-realtime feeds object null see configuring real-time updaters
transmodelApi configure Entur Transmodel API (Sandbox) object null See the code for parameters, no doc provided.

Routing defaults

There are many trip planning options used in the OTP web API, and more exist internally that are not exposed via the API. You may want to change the default value for some of these parameters, i.e. the value which will be applied unless it is overridden in a web API request.

A full list of them can be found in the RoutingRequest. Any public field or setter method in this class can be given a default value using the routingDefaults section of router-config.json as follows:

{
    "routingDefaults": {
        "walkSpeed": 2.0,
        "stairsReluctance": 4.0,
        "carDropoffTime": 240
    }
}

Tuning itinerary filtering

Nested inside routingDefaults {...} in router-config.json.

OTP2 may produce numerous pareto-optimal results when using time, number-of-transfers and generalized-cost as criteria. Use the parameters listed here to reduce/filter the itineraries return by the search engine before returning the results to client.

config key description value type value default
debugItineraryFilter Enable this to attach a system notice to itineraries instead of removing them. Some filters are not configurable, byt will show up in the system-notice if debugging is enabled. boolean false
groupBySimilarityKeepOne Pick ONE itinerary from each group after putting itineraries that is 85% similar together. double 0.85 (85%)
groupBySimilarityKeepNumOfItineraries Reduce the number of itineraries to the requested number by reducing each group of itineraries grouped by 68% similarity. double 0.68 (68%)
transitGeneralizedCostLimit A relative maximum limit for the generalized cost for transit itineraries. The limit is a linear function of the minimum generalized-cost. The function is used to calculate a max-limit. The max-limit is then used to to filter by generalized-cost. Transit itineraries with a cost higher than the max-limit is dropped from the result set. None transit itineraries is excluded from the filter. To set a filter to be 1 hour plus 2 times the best cost use: 3600 + 2.0 x. To set an absolute value(3000) use: 3000 + 0x linear function null

Group-by-filters

The group-by-filter is a bit complex, but should be simple to use. Set debugItineraryFilter=true and experiment with searchWindow and the two group-by parameters(debugItineraryFilter and groupBySimilarityKeepNumOfItineraries).

The group-by-filter work by grouping itineraries together and then reducing the number of itineraries in each group, keeping the itinerary/itineraries with the best generalized-cost. The group-by function first pick all transit legs that account for more than N% of the itinerary based on distance traveled. This become the group-key. To keys are the same if all legs in one of the keys also exist in the other. Note, one key may have a lager set of legs than the other, but they can still be the same. When comparing to legs we compare the tripId and make sure the legs overlap in place and time. Two legs are the same if both legs ride at least a common subsection of the same trip. The groupBySimilarityKeepOne filter will keep ONE itinerary in each group. The groupBySimilarityKeepNumOfItineraries is a bit more complex, because it uses the numOfItineraries request parameter to estimate a maxLimit for each group. For example, if the numOfItineraries is 5 elements and there is 3 groups, we set the max-limit for each group to 2, returning between 4 and 6 elements depending on the distribution. The max-limit can never be less than 1.

Drive-to-transit routing defaults

When using the "park and ride" or "kiss and ride" modes (drive to transit), the initial driving time to reach a transit stop or park and ride facility is constrained. You can set a drive time limit in seconds by adding a line like maxPreTransitTime = 1200 to the routingDefaults section. If the limit is too high on a very large street graph, routing performance may suffer.

Boarding and alighting times

Sometimes there is a need to configure a longer ride or alighting times for specific modes, such as airplanes or ferries, where the check-in process needs to be done in good time before ride. The ride time is added to the time when going from the stop (offboard) vertex to the onboard vertex, and the alight time is added vice versa. The times are configured as seconds needed for the ride and alighting processes in router-config.json as follows:

{
  "boardTimes": {
    "AIRPLANE": 2700
  },
  "alightTimes": {
    "AIRPLANE": 1200
  }
}

Timeout

In OTP1 path searches sometimes toke a long time to complete. With the new Raptor algorithm this not the case anymore. The street part of the routing may still take a long time if searching very long distances. You can set the street routing timeout to avoid tying up server resources on pointless searches and ensure that your users receive a timely response. You can also limit the max distance to search for WALK, BIKE and CAR. When a search times out, a WARN level log entry is made with information that can help identify problematic searches and improve our routing methods. There are no timeouts for the transit part of the routing search, instead configure a reasonable dynamic search-window. To set the street routing timeout use the following config:

// router-config.json
{
  "streetRoutingTimeout": 5.5
}

This specifies a timeout in (optionally fractional) seconds. The search abort after this many seconds and any paths found are returned to the client.

Logging incoming requests

You can log some characteristics of trip planning requests in a file for later analysis. Some transit agencies and operators find this information useful for identifying existing or unmet transportation demand. Logging will be performed only if you specify a log file name in the router config:

// router-config.json
{
  "requestLogFile": "/var/otp/request.log"
}

Each line in the resulting log file will look like this:

2016-04-19T18:23:13.486 0:0:0:0:0:0:0:1 ARRIVE 2016-04-07T00:17 WALK,BUS,CABLE_CAR,TRANSIT,BUSISH 45.559737193889966 -122.64999389648438 45.525592487765635 -122.39044189453124 6095 3 5864 3 6215 3

The fields separated by whitespace are (in order):

  1. Date and time the request was received
  2. IP address of the user
  3. Arrive or depart search
  4. The arrival or departure time
  5. A comma-separated list of all transport modes selected
  6. Origin latitude and longitude
  7. Destination latitude and longitude

Finally, for each itinerary returned to the user, there is a travel duration in seconds and the number of transit vehicles used in that itinerary.

Tuning transit routing

Nested inside transit {...} in router-config.json.

Some of these parameters for tuning transit routing is only available through configuration and cannot be set in the routing request. These parameters work together with the default routing request and the actual routing request.

config key description value type value default
maxNumberOfTransfers Use this parameter to allocate enough space for Raptor. Set it to the maximum number of transfers for any given itinerary expected to be found within the entire transit network. The memory overhead of setting this higher than the maximum number of transfers is very little so it is better to set it too high then to low. int 12
scheduledTripBinarySearchThreshold The threshold is used to determine when to perform a binary trip schedule search to reduce the number of trips departure time lookups and comparisons. When testing with data from Entur and all of Norway as a Graph, the optimal value was around 50. Changing this may improve the performance with just a few percent. int 50
iterationDepartureStepInSeconds Step for departure times between each RangeRaptor iterations. A transit network usually uses minute resolution for its depature and arrival times. To match that, set this variable to 60 seconds. int 60
searchThreadPoolSize Split a travel search in smaller jobs and run them in parallel to improve performance. Use this parameter to set the total number of executable threads available across all searches. Multiple searches can run in parallel - this parameter have no effect with regard to that. If 0, no extra threads are started and the search is done in one thread. int 0
dynamicSearchWindow The dynamic search window coefficients used to calculate the EDT(earliest-departure-time), LAT(latest-arrival-time) and SW(raptor-search-window) using heuristics. object null
stopTransferCost Use this to set a stop transfer cost for the given TransferPriority. The cost is applied to boarding and alighting at all stops. All stops have a transfer cost priority set, the default is ALLOWED. The stopTransferCost parameter is optional, but if listed all values must be set. enum map null

Tuning transit routing - Dynamic search window

Nested inside transit : { dynamicSearchWindow : { ... } } in router-config.json.

config key description value type value default
minTripTimeCoefficient The coefficient to multiply with minimum travel time found using a heuristic search. This value is added to the minWinTimeMinutes. A value between 0.0 to 3.0 is expected to give ok results. double 0.75
minWinTimeMinutes The constant minimum number of minutes for a raptor search window. Use a value between 20-180 minutes in a normal deployment. int 40
maxWinTimeMinutes Set an upper limit to the calculation of the dynamic search window to prevent exceptionable cases to cause very long search windows. Long search windows consumes a lot of resources and may take a long time. Use this parameter to tune the desired maximum search time. int 180 (3 hours)
stepMinutes The search window is rounded of to the closest multiplication of N minutes. If N=10 minutes, the search-window can be 10, 20, 30 ... minutes. It the computed search-window is 5 minutes and 17 seconds it will be rounded up to 10 minutes. int 10

Tuning transit routing - Stop transfer cost

Nested inside transit : { stopTransferCost : { ... } } in router-config.json.

This cost is in addition to other costs like boardCost and indirect cost from waiting (board-/alight-/transfer slack). You should account for this when you tune the routing search parameters.

If not set the stopTransferCost is ignored. This is only available for NeTEx imported Stops.

The cost is a scalar, but is equivalent to the felt cost of riding a transit trip for 1 second.

config key description value type
DISCOURAGED Use a very high cost like 72 000 to eliminate transfers ath the stop if not the only option. int
ALLOWED Allowed, but not recommended. Use something like 150. int
RECOMMENDED Use a small cost penalty like 60. int
PREFERRED The best place to do transfers. Should be set to 0(zero). int

Use values in a range from 0 to 100 000. All key/value pairs are required if the stopTransferCost is listed.

Transit example section from router-config.json

{
    transit: {
        maxNumberOfTransfers: 12,
        scheduledTripBinarySearchThreshold: 50,
        iterationDepartureStepInSeconds: 60,
        searchThreadPoolSize: 0,
        dynamicSearchWindow: {
            minTripTimeCoefficient: 0.4,
            minTripTimeCoefficient: 0.3,
            minTimeMinutes: 30,
            maxLengthMinutes : 360,
            stepMinutes: 10
        },
        stopTransferCost: {
            DISCOURAGED: 72000,
            ALLOWED:       150,
            RECOMMENDED:    60,
            PREFERRED:       0
        }
    }
}

Real-time data

GTFS feeds contain schedule data that is is published by an agency or operator in advance. The feed does not account for unexpected service changes or traffic disruptions that occur from day to day. Thus, this kind of data is also referred to as 'static' data or 'theoretical' arrival and departure times.

GTFS-Realtime

The GTFS-RT spec complements GTFS with three additional kinds of feeds. In contrast to the base GTFS schedule feed, they provide real-time updates ('dynamic' data) and are are updated from minute to minute.

Bicycle rental systems

Besides GTFS-RT transit data, OTP can also fetch real-time data about bicycle rental networks including the number of bikes and free parking spaces at each station. We support bike rental systems from JCDecaux, BCycle, VCub, Keolis, Bixi, the Dutch OVFiets system, ShareBike, GBFS and a generic KML format. It is straightforward to extend OTP to support any bike rental system that exposes a JSON API or provides KML place markers, though it requires writing a little code.

The generic KML needs to be in format like

<?xml version="1.0" encoding="utf-8" ?>
<kml xmlns="http://www.opengis.net/kml/2.2">
<Document id="root_doc">
<Schema name="citybikes" id="citybikes">
    <SimpleField name="ID" type="int"></SimpleField>
</Schema>
  <Placemark>
    <name>A Bike Station</name>
    <ExtendedData><SchemaData schemaUrl="#citybikes">
        <SimpleData name="ID">0</SimpleData>
    </SchemaData></ExtendedData>
      <Point><coordinates>24.950682884886643,60.155923430488102</coordinates></Point>
  </Placemark>
</Document></kml>

Configuring real-time updaters

Real-time data can be provided using either a pull or push system. In a pull configuration, the GTFS-RT consumer polls the real-time provider over HTTP. That is to say, OTP fetches a file from a web server every few minutes. In the push configuration, the consumer opens a persistent connection to the GTFS-RT provider, which then sends incremental updates immediately as they become available. OTP can use both approaches. The OneBusAway GTFS-realtime exporter project provides this kind of streaming, incremental updates over a websocket rather than a single large file.

Real-time data sources are configured in router-config.json. The updaters section is an array of JSON objects, each of which has a type field and other configuration fields specific to that type. Common to all updater entries that connect to a network resource is the url field.

// router-config.json
{
    // Routing defaults are any public field or setter in the Java class
    // org.opentripplanner.routing.api.request.RoutingRequest
    "routingDefaults": {
        "numItineraries": 6,
        "walkSpeed": 2.0,
        "stairsReluctance": 4.0,
        "carDropoffTime": 240
    },

    "updaters": [

        // GTFS-RT service alerts (frequent polling)
        {
            "type": "real-time-alerts",
            "frequencySec": 30,
            "url": "http://developer.trimet.org/ws/V1/FeedSpecAlerts/appID/0123456789ABCDEF",
            "feedId": "TriMet"
        },

        // Polling bike rental updater.
        // sourceType can be: jcdecaux, b-cycle, bixi, keolis-rennes, ov-fiets,
        // city-bikes, citi-bike-nyc, next-bike, vcub, kml
        {
            "type": "bike-rental",
            "frequencySec": 300,
            "sourceType": "city-bikes",
            "url": "http://host.domain.tld"
        },

        //<!--- San Francisco Bay Area bike share -->
        {
          "type": "bike-rental",
          "frequencySec": 300,
          "sourceType": "sf-bay-area",
          "url": "http://www.bayareabikeshare.com/stations/json"
        },

        //<!--- Tampa Area bike share -->
        {
          "type": "bike-rental",
          "frequencySec": 300,
          "sourceType": "gbfs",
          "url": "http://coast.socialbicycles.com/opendata/"
        },


        // Polling bike rental updater for DC bikeshare (a Bixi system)
        // Negative update frequency means to run once and then stop updating (essentially static data)
        {
            "type": "bike-rental",
            "sourceType": "bixi",
            "url": "https://www.capitalbikeshare.com/data/stations/bikeStations.xml",
            "frequencySec": -1
        },

        // Bike parking availability
        {
            "type": "bike-park"
        },

        // Polling for GTFS-RT TripUpdates)
        {
            "type": "stop-time-updater",
            "frequencySec": 60,
            // this is either http or file... shouldn't it default to http or guess from the presence of a URL?
            "sourceType": "gtfs-http",
            "url": "http://developer.trimet.org/ws/V1/TripUpdate/appID/0123456789ABCDEF",
            "feedId": "TriMet"
        },

        // Streaming differential GTFS-RT TripUpdates over websockets
        {
            "type": "websocket-gtfs-rt-updater"
        }
    ]
}

GBFS Configuration

Steps to add a GBFS feed to a router:

{
     "type": "bike-rental",
     "frequencySec": 60,
     "sourceType": "gbfs",
     "url": "http://coast.socialbicycles.com/opendata/"
}
type: "bike-rental"
frequencySec: frequency in seconds in which the GBFS service will be polled
sourceType: "gbfs"
url: the URL of the GBFS feed (do not include the gbfs.json at the end) *

* For a list of known GBFS feeds see the list of known GBFS feeds

Bike Rental Service Directory configuration (sandbox feature)

To configure and url for the BikeRentalServiceDirectory.

// router-config.json
{
  "bikeRentalServiceDirectoryUrl": "https://api.dev.entur.io/mobility/v1/bikes"
}

Configure using command-line arguments

Certain settings can be provided on the command line, when starting OpenTripPlanner. See the CommandLineParameters class for a full list of arguments.