config.yml

graphhopper:
  import.osm.ignored_highways: ''
  # OpenStreetMap input file
  # datareader.file: some.pbf

  ##### Vehicles #####


  ##### Routing Profiles ####

  # Routing can be done only for profiles listed below. For more information about profiles and custom profiles have a
  # look into the documentation at docs/core/profiles.md or the examples under web/src/test/java/com/graphhopper/application/resources/
  # or the CustomWeighting class for the raw details.
  #
  # In general a profile consists of the following
  # - name (required): a unique string identifier for the profile
  # - weighting (optional): by default 'custom'
  # - turn_costs (optional):
  #     vehicle_types: [train, tram, light_rail, subway] (vehicle types used for vehicle-specific turn restrictions)
  #     u_turn_costs: 60 (time-penalty for doing a u-turn in seconds)
  #
  # Depending on the above fields there are other properties that can be used, e.g.
  # - custom_model_files: when you specified "weighting: custom" you need to set one or more json files which are searched in
  #   custom_models.directory or the working directory that defines the custom_model. If you want an empty model you can
  #   set "custom_model_files: []
  #   You can also use the `custom_model` field instead and specify your custom model in the profile directly.
  #
  # To prevent long running routing queries you should usually enable either speed or hybrid mode for all the given
  # profiles (see below). Or at least limit the number of `routing.max_visited_nodes`.

  profiles:
    - name: tgv_all
      turn_costs:
        vehicle_types: [train]
        # Reversing costs 5 minutes.
        u_turn_costs: 300
      custom_model_files: [rail.json, tgv_all.json, preferred_direction.json]
    - name: non_tgv
      turn_costs:
        vehicle_types: [train]
        # Reversing costs 15 minutes.
        u_turn_costs: 900
      custom_model_files: [rail.json, non_tgv.json, preferred_direction.json]
    - name: tramtrain
      turn_costs:
        vehicle_types: [train, tram, light_rail]
        # Reversing costs 3 minutes.
        u_turn_costs: 180
      custom_model_files: [tramtrain.json, 15kv-ac_750v-dc.json, gauge_1435.json, preferred_direction.json]
    - name: all_tracks
      turn_costs:
        vehicle_types: [train, tram, light_rail, subway]
        # Reversing costs 3 minutes.
        u_turn_costs: 180
      custom_model_files: [all_tracks.json]
    - name: all_tracks_1435
      turn_costs:
        vehicle_types: [train, tram, light_rail, subway]
        # Reversing costs 3 minutes.
        u_turn_costs: 180
      custom_model_files: [all_tracks.json, gauge_1435.json]

  # instead of the inbuilt custom models (see ./src/main/resources/com/graphhopper/custom_models)
  # you can specify a folder where to find your own custom model files
  custom_models.directory: custom_models

  # Speed mode:
  # It's possible to speed up routing by doing a special graph preparation (Contraction Hierarchies, CH). This requires
  # more RAM/disk space for holding the prepared graph but also means less memory usage per request. Using the following
  # list you can define for which of the above routing profiles such preparation shall be performed. Note that to support
  # profiles with `turn_costs: true` a more elaborate preparation is required (longer preparation time and more memory
  # usage) and the routing will also be slower than with `turn_costs: false`.
  profiles_ch:
    - profile: tgv_all
    - profile: non_tgv
    - profile: tramtrain
    - profile: all_tracks
    - profile: all_tracks_1435

  # Hybrid mode:
  # Similar to speed mode, the hybrid mode (Landmarks, LM) also speeds up routing by doing calculating auxiliary data
  # in advance. Its not as fast as speed mode, but more flexible.
  #
  # Advanced usage: It is possible to use the same preparation for multiple profiles which saves memory and preparation
  # time. To do this use e.g. `preparation_profile: my_other_profile` where `my_other_profile` is the name of another
  # profile for which an LM profile exists. Important: This only will give correct routing results if the weights
  # calculated for the profile are equal or larger (for every edge) than those calculated for the profile that was used
  # for the preparation (`my_other_profile`)
  profiles_lm: []


  #### Encoded Values ####

  # Add additional information to every edge. Used for path details (#1548) and custom models (docs/core/custom-models.md)
  # Default values are: road_class,road_class_link,road_environment,max_speed,road_access
  # More are: surface,smoothness,max_width,max_height,max_weight,max_weight_except,hgv,max_axle_load,max_length,
  #           hazmat,hazmat_tunnel,hazmat_water,lanes,osm_way_id,toll,track_type,mtb_rating,hike_rating,horse_rating,
  #           country,curvature,average_slope,max_slope,car_temporal_access,bike_temporal_access,foot_temporal_access
  # For railway routing, the encoded values rail_access and rail_average_speed must be mentioned.
  graph.encoded_values: gauge,voltage,electrified,frequency,road_environment,max_speed,rail_access,rail_average_speed,railway_class,railway_service,preferred_direction

  #### Speed, hybrid and flexible mode ####

  # To make CH preparation faster for multiple profiles you can increase the default threads if you have enough RAM.
  # Change this setting only if you know what you are doing and if the default worked for you.
  # prepare.ch.threads: 1

  # To tune the performance vs. memory usage for the hybrid mode use
  # prepare.lm.landmarks: 16

  # Make landmark preparation parallel if you have enough RAM. Change this only if you know what you are doing and if
  # the default worked for you.
  # prepare.lm.threads: 1


  #### Elevation ####

  # To populate your graph with elevation data use SRTM, default is noop (no elevation). Read more about it in docs/core/elevation.md
  # graph.elevation.provider: srtm

  # default location for cache is /tmp/srtm
  # graph.elevation.cache_dir: ./srtmprovider/

  # If you have a slow disk or plenty of RAM change the default MMAP to:
  # graph.elevation.dataaccess: RAM_STORE

  # To enable bilinear interpolation when sampling elevation at points (default uses nearest neighbor):
  # graph.elevation.interpolate: bilinear

  # Reduce ascend/descend per edge without changing the maximum slope:
  # graph.elevation.edge_smoothing: ramer
  # removes elevation fluctuations up to max_elevation (in meter) and replaces the elevation with a value based on the average slope
  # graph.elevation.edge_smoothing.ramer.max_elevation: 5
  # Using an averaging approach for smoothing will reveal values not affected by outliers and realistic slopes and total altitude values (up and down)
  # graph.elevation.edge_smoothing: moving_average
  # window size in meter along a way used for averaging a node's elevation
  # graph.elevation.edge_smoothing.moving_average.window_size: 150


  # To increase elevation profile resolution, use the following two parameters to tune the extra resolution you need
  # against the additional storage space used for edge geometries. You should enable bilinear interpolation when using
  # these features (see #1953 for details).
  # - first, set the distance (in meters) at which elevation samples should be taken on long edges
  # graph.elevation.long_edge_sampling_distance: 60
  # - second, set the elevation tolerance (in meters) to use when simplifying polylines since the default ignores
  #   elevation and will remove the extra points that long edge sampling added
  # graph.elevation.way_point_max_distance: 10


  #### Country-dependent defaults for max speeds ####

  # This features sets a maximum speed in 'max_speed' encoded value if no maxspeed tag was found. It is country-dependent
  # and based on several rules. See https://github.com/westnordost/osm-legal-default-speeds
  # To use it uncomment the following, then enable urban density below and add 'country' to graph.encoded_values
  # max_speed_calculator.enabled: true


  #### Urban density (built-up areas) ####

  # This feature allows classifying roads into 'rural', 'residential' and 'city' areas (encoded value 'urban_density')
  # Use 1 or more threads to enable the feature
  # graph.urban_density.threads: 8
  # Use higher/lower sensitivities if too little/many roads fall into the according categories.
  # Using smaller radii will speed up the classification, but only change these values if you know what you are doing.
  # If you do not need the (rather slow) city classification set city_radius to zero.
  # graph.urban_density.residential_radius: 400
  # graph.urban_density.residential_sensitivity: 6000
  # graph.urban_density.city_radius: 1500
  # graph.urban_density.city_sensitivity: 1000


  #### Subnetworks ####

  # In many cases the road network consists of independent components without any routes going in between. In
  # the most simple case you can imagine an island without a bridge or ferry connection. The following parameter
  # allows setting a minimum size (number of edges) for such detached components. This can be used to reduce the number
  # of cases where a connection between locations might not be found.
  prepare.min_network_size: 200
  prepare.subnetworks.threads: 1

  #### Routing ####

  # You can define the maximum visited nodes when routing. This may result in not found connections if there is no
  # connection between two points within the given visited nodes. The default is Integer.MAX_VALUE. Useful for flexibility mode
  # routing.max_visited_nodes: 1000000

  # The maximum time in milliseconds after which a routing request will be aborted. This has some routing algorithm
  # specific caveats, but generally it should allow the prevention of long-running requests. The default is Long.MAX_VALUE
  # routing.timeout_ms: 300000

  # Control how many active landmarks are picked per default, this can improve query performance
  # routing.lm.active_landmarks: 4

  # You can limit the max distance between two consecutive waypoints of flexible routing requests to be less or equal
  # the given distance in meter. Default is set to 1000km.
  routing.non_ch.max_waypoint_distance: 1000000


  #### Storage ####

  # Excludes certain types of highways during the OSM import to speed up the process and reduce the size of the graph.
  # A typical application is excluding 'footway','cycleway','path' and maybe 'pedestrian' and 'track' highways for
  # motorized vehicles. This leads to a smaller and less dense graph, because there are fewer ways (obviously),
  # but also because there are fewer crossings between highways (=junctions).
  # Another typical example is excluding 'motorway', 'trunk' and maybe 'primary' highways for bicycle or pedestrian routing.
  # import.osm.ignored_highways: footway,cycleway,path,pedestrian,steps # typically useful for motorized-only routing
  # import.osm.ignored_highways: motorway,trunk # typically useful for non-motorized routing

  # configure the memory access, use RAM_STORE for well equipped servers (default and recommended)
  graph.dataaccess.default_type: RAM_STORE

  # will write way names in the preferred language (language code as defined in ISO 639-1 or ISO 639-2):
  # datareader.preferred_language: en

  #### Custom Areas ####

  # GraphHopper reads GeoJSON polygon files including their properties from this directory and makes them available
  # to all tag parsers and custom models. All GeoJSON Features require to have the "id" property.
  # Country borders are included automatically (see countries.geojson).
  # custom_areas.directory: path/to/custom_areas


  #### Country Rules ####

  # GraphHopper applies country-specific routing rules during import (not enabled by default).
  # You need to redo the import for changes to take effect.
  # country_rules.enabled: true

# Dropwizard server configuration
server:
  application_connectors:
  - type: http
    port: 8989
    # for security reasons bind to localhost
    bind_host: localhost
    # increase GET request limit - not necessary if /maps UI is not used or used without custom models
    max_request_header_size: 50k
  request_log:
      appenders: []
  admin_connectors:
  - type: http
    port: 8990
    bind_host: localhost
# See https://www.dropwizard.io/en/latest/manual/core.html#logging
logging:
  level: INFO
  appenders:
    - type: console
      time_zone: UTC
      log_format: "%d{yyyy-MM-dd HH:mm:ss.SSS} [%thread] %-5level %logger{36} - %msg%n"
  loggers:
    "com.graphhopper.osm_warnings":
      level: DEBUG
      additive: false
      appenders:
        - type: console
          logFormat: '[%level] %msg%n'