gh-config.yml

graphhopper:

  # OpenStreetMap input file PBF or XML, can be changed via command line -Ddw.graphhopper.datareader.file=some.pbf
  datareader.file: ""
  # Local folder used by graphhopper to store its data
  graph.location: graph-cache


  ##### 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
  # - vehicle (required): refers to the `graph.vehicles` used for this profile
  # - weighting (required): the weighting used for this profile like custom,fastest,shortest or short_fastest
  # - turn_costs (true/false, default: false): whether or not turn restrictions should be applied for this profile.
  #
  # Depending on the above fields there are other properties that can be used, e.g.
  # - distance_factor: 0.1 (can be used to fine tune the time/distance trade-off of short_fastest weighting)
  # - u_turn_costs: 60 (time-penalty for doing a u-turn in seconds (only possible when `turn_costs: true`)).
  #   Note that since the u-turn costs are given in seconds the weighting you use should also calculate the weight
  #   in seconds, so for example it does not work with shortest weighting.
  # - custom_model_file: when you specified "weighting: custom" you need to set a json file inside your custom_model_folder
  #   or working directory that defines the custom_model. If you want an empty model you can also set "custom_model_file: empty".
  #   You can also use th e`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: car4wd
      vehicle: roads
      weighting: custom
      custom_model:
        distance_influence: 1
        speed:
          - if: "track_type == GRADE4 || track_type == GRADE5"
            limit_to: 5
          - else: ""
            limit_to: "car_average_speed"
        priority:
          - if: "track_type != GRADE4 && track_type != GRADE5 && car_access == false"
            multiply_by: "0"
          - if: "road_class == TRACK"
            multiply_by: "2"
    - name: hike
      vehicle: roads
      weighting: custom
      custom_model:
        priority:
          - if: "true"
            multiply_by: "foot_priority"
          - if: "!foot_access && hike_rating < 4"
            multiply_by: "0"
          - if: "foot_network == INTERNATIONAL || foot_network == NATIONAL"
            multiply_by: "1.7"
          - else_if: "foot_network == REGIONAL || foot_network == LOCAL"
            multiply_by: "1.5"
        speed:
          - if: "hike_rating < 1"
            limit_to: "foot_average_speed"
          - else_if: "hike_rating > 2"
            limit_to: "2"
          - else: ""
            limit_to: "4"
          - if: "average_slope >= 15"
            limit_to: "1.5"
          - else_if: "average_slope >=  7"
            limit_to: "2.5"
          - else_if: "average_slope >=  4"
            multiply_by: "0.85"
          - else_if: "average_slope <= -4"
            multiply_by: "1.05"
    - name: mtb
      vehicle: mtb
      custom_model_files: []

  # Speed mode:
  # Its 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: car4wd
    - profile: hike
    - profile: mtb

  # 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: []


  #### Vehicles ####

  # The vehicle defines the base for how the routing of a profile behaves. It can be fine tuned using the options:
  # name=mycustomvehicle,block_private=true,turn_costs=true,transportation_mode=MOTOR_VEHICLE (only for the roads vehicle)
  # Still, it is recommended to avoid changing the vehicle settings and change the custom model instead.
  graph.vehicles: roads|block_fords=false|block_barriers=false,car|block_fords=false|block_barriers=false,foot|block_fords=false,mtb|block_fords=false|block_barriers=false
  # Other standard vehicles: foot,bike,mtb,racingbike,motorcycle,wheelchair


  #### 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,hgv,max_axle_load,max_length,hazmat,hazmat_tunnel,hazmat_water,toll,track_type,
  #           mtb_rating, hike_rating,horse_rating,lanes
  graph.encoded_values: road_class,road_class_link,road_environment,max_speed,road_access,track_type,surface,hike_rating,average_slope,max_slope

  #### 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: /usr/src/app/elevation/

  # 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
  # A potentially bigger reduction of ascend/descend is possible, but maximum slope will often increase (do not use when average_slope or maximum_slope shall be used in a custom_model)
  # graph.elevation.edge_smoothing: moving_average

  # 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


  #### 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: 300
  # graph.urban_density.residential_sensitivity: 60
  # graph.urban_density.city_radius: 2000
  # graph.urban_density.city_sensitivity: 30


  #### 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


  #### 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

  # 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 ####

  import.osm.ignored_highways:

  # 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

  # Sort the graph after import to make requests roughly ~10% faster. Note that this requires significantly more RAM on import.
  # graph.do_sort: true


  #### Custom Areas ####

  # GraphHopper reads GeoJSON polygon files including their properties from this directory and makes them available
  # to all tag parsers and vehicles. Country borders (see countries.geojson) are always included automatically.
  # 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
  request_log:
      appenders: []
  admin_connectors:
  - type: http
    port: 8990
    bind_host: localhost
# See https://www.dropwizard.io/en/latest/manual/core.html#logging
logging:
  appenders:
  - type: file
    time_zone: UTC
    current_log_filename: logs/graphhopper.log
    log_format: "%d{yyyy-MM-dd HH:mm:ss.SSS} [%thread] %-5level %logger{36} - %msg%n"
    archive: true
    archived_log_filename_pattern: ./logs/graphhopper-%d.log.gz
    archived_file_count: 30
    never_block: true
  - type: console
    time_zone: UTC
    log_format: "%d{yyyy-MM-dd HH:mm:ss.SSS} [%thread] %-5level %logger{36} - %msg%n"