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Base directory

The OTP defaults to . Unless you tell OTP otherwise, all other configuration, input files and storage directories will be sought immediately beneath this one. This prefix follows UNIX conventions so it should work in Linux and Mac OSX environments, but it is inappropriate in Windows and where the user running OTP either cannot obtain permissions to or simply wishes to experiment within his or her home directory rather than deploy a system-wide server. In these cases one should use the basePath switch when starting up OTP to override the default. For example: on a Linux system, in Mac OSX, or in Windows.

Routers

A single OTP instance can handle several regions independently. Each of these separate (but potentially geographically overlapping) services is called a and is referred to by a short unique ID such as 'newyork' or 'paris'. Each router has its own subdirectory in a directory called 'graphs' directly under the OTP base directory, and each router's directory is always named after its router ID. Thus, by default the files for the router 'tokyo' will be located at . Here is an example directory layout for an OTP instance with two routers, one for New York City and one for Portland, Oregon:

You can see that each of these subdirectories contains one or more GTFS feeds (which are just zip files full of comma-separated tables), a PBF street map file, some JSON configuration files, and another file called . On startup, OTP scans router directories for input and configuration files, and can optionally store the resulting combined representation of the transportation network as Graph.obj in the same directory to avoid re-processing the data the next time it starts up. The directory is where OTP will store its local copies of resources fetched from the internet, such as US elevation tiles.

System-wide vs. graph build vs. router configuration

OTP is configured via JSON files. The file is placed in the OTP base directory and contains settings that affect the entire OTP instance. Each router within that instance is configured using two other JSON files placed alongside the input files (OSM, GTFS, elevation data etc.) in the router's directory. These router-level config files are named and . Each configuration option within each of these files is optional, as are all three of the files themselves. If any option or an entire file is missing, reasonable defaults will be applied.

Some parts of the process that loads the street and transit network description are time consuming and memory-hungry. To avoid repeating these slow steps every time OTP starts up, we can trigger them manually whenever the input files change, saving the resulting transportation network description to disk. We call this prepared product a (following mathematical terminology ), and refer to these "heavier" steps as . They are controlled by . There are many other details of OTP operation that can be modified without requiring the potentially long operation of rebuilding the graph. These run-time configuration options are found in .

Reaching a subway platform

The boarding 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

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:

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:

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 in your router directory:

You may also want to add the 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 Vila Mable Long Sleeve Top Women Discount New Arrival jlpSEVz
. 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 :

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 or 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 . Use to merge a set of files into one file.

Fares configuration

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

The current list of custom fare type is:

The current list of 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 2 wayPropertySets defined;

To add your own custom property set have a look at and . 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.

Custom naming

You can define a custom naming scheme for elements drawn from OSM by defining an field in , such as:

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

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

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 class in the Javadoc . Any public field or setter method in this class can be given a default value using the routingDefaults section of as follows:

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 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 boarding or alighting times for specific modes, such as airplanes or ferries, where the check-in process needs to be done in good time before boarding. The boarding 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 boarding and alighting processes in as follows:

Timeouts

Path searches can sometimes take a long time to complete, especially certain problematic cases that have yet to be optimized. Often a first itinerary is found quickly, but it is time-consuming or impossible to find subsequent alternative itineraries and this delays the response. You can set timeouts to avoid tying up server resources on pointless searches and ensure that your users receive a timely response. 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. The simplest timeout option is:

This specifies a single timeout in (optionally fractional) seconds. Searching is aborted after this many seconds and any paths already found are returned to the client. This is equivalent to specifying a array with a single element. The alternative is:

Here, the configuration key is (plural) and we specify an array of times in floating-point seconds. The Nth element in the array applies to the Nth itinerary search, and importantly all values are relative to the beginning of the search for the itinerary. If OTP is configured to find more itineraries than there are elements in the timeouts array, the final element in the timeouts array will apply to all remaining unmatched searches.

This allows you to keep overall response time down while ensuring that the end user will get at least one response, providing more only when it won't hurt response time. The timeout values will typically be decreasing to reflect the decreasing marginal value of alternative itineraries: everyone wants at least one response, it's nice to have two for comparison, but we only care about having three, four, or more options if completing those extra searches doesn't cause annoyingly long response times.

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:

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

The fields are separated by whitespace and are (in order):

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.

Real-time data

GTFS feeds contain 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

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complements GTFS with three additional kinds of feeds. In contrast to the base GTFS schedule feed, they provide updates ( 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

Configuration

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 . The section is an array of JSON objects, each of which has a field and other configuration fields specific to that type. Common to all updater entries that connect to a network resource is the field.

GBFS Configuration

Steps to add a GBFS feed to a router:

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

Alerts are text messages attached to GTFS objects, informing riders of disruptions and changes.

CoverageJSON

WORK-IN-PROGRESS

The following items are (major) outstanding issues to be resolved for the first version:

Contents

CoverageJSON is a format for encoding coverage data like grids, time series, and vertical profiles, distinguished by the geometry of their spatiotemporal domain. A CoverageJSON object represents a domain, a range, a coverage, or a collection of coverages. A range in CoverageJSON represents coverage values. A coverage in CoverageJSON is the combination of a domain, parameters, ranges, and additional metadata. A coverage collection represents a list of coverages.

A complete CoverageJSON data structure is always an object (in JSON terms). In CoverageJSON, an object consists of a collection of name/value pairs – also called members. For each member, the name is always a string. Member values are either a string, number, object, array or one of the literals: true, false, and null. An array consists of elements where each element is a value as described above.

A CoverageJSON grid coverage of global air temperature:

where "http://example.com/coverages/123/TEMP" points to the following document:

Range data can also be directly embedded into the main CoverageJSON document, making it stand-alone.

The candidate OGC standard Coverage Implementation Schema 1.1 (short CIS) defines a coverage model targeted towards OGC service types like Web Coverage Service (WCS) and is the successor of the “GML 3.2.1 Application Schema – Coverages” version 1.0 (short GMLCOV).

The model of CoverageJSON can be seen as a mix of CIS and the data cube-based NetCDF file format .

The following lists some areas where the model used by CoverageJSON departs from CIS:

An i18n object represents a string in multiple languages where each key is a language tag as defined in BCP 47 , and the value is the string in that language. The special language tag "und" can be used to identify a value whose language is unknown or undetermined.

Example:

Parameter objects represent metadata about the values of the coverage in terms of the observed property (like water temperature), the units, and others.

Example for a continuous-data parameter:

Example for a categorical-data parameter:

Parameter group objects represent logical groups of parameters, for example vector quantities.

Example of a group describing a vector quantity:

where "WIND_SPEED" and "WIND_DIR" reference existing parameters in a CoverageJSON coverage or collection object by their short identifiers.

Example of a group describing uncertainty of a parameter:

where "SST_mean" references the following parameter:

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