bambam-omf 0.3.0

[graph]
edge_list = [
    { input_file = "walk/edges-compass.csv.gz" },
    { input_file = "bike/edges-compass.csv.gz" },
    { input_file = "drive/edges-compass.csv.gz" },
]
vertex_list_input_file = "vertices-compass.csv.gz"

[mapping]
type = "edge"
geometry = [
    { type = "from_linestrings", geometry_input_file = "walk/edges-geometries-enumerated.txt.gz" },
    { type = "from_linestrings", geometry_input_file = "bike/edges-geometries-enumerated.txt.gz" },
    { type = "from_linestrings", geometry_input_file = "drive/edges-geometries-enumerated.txt.gz" },
]
# avg edge length of h3 resolution 8. see <https://h3geo.org/docs/core-library/restable/#edge-lengths>
tolerance.distance = 0.531414010
tolerance.unit = "kilometers"
queries_without_destinations = true
matching_type = ["point"]

[algorithm]
type = "a*"
termination_behavior = "allow_tree_termination"

# cut off searches that exceed these termination policies. 
[termination]
type = "solution_size"
limit = 1_000_000

# use a time-optimal routing strategy
[cost]
weights.trip_time = 1.0
vehicle_rates.trip_time.type = "raw"

###
### WALK TOPOLOGY
###
[[search]] 
traversal.type = "combined"
traversal.models = [
    { type = "distance", distance_unit = "miles" }, 
    { type = "fixed_speed", name = "walk", speed = 5.0, speed_unit = "kph" },
    { type = "time", time_unit = "minutes" },
    { type = "multimodal", this_mode = "walk", available_modes = ["walk", "bike", "drive"] }
]

constraint.type = "combined"
constraint.models = [
    { type = "multimodal", this_mode = "walk", available_modes = ["walk", "bike", "drive"] }
]

###
### BIKE TOPOLOGY
###
[[search]] 
traversal.type = "combined"
traversal.models = [
    { type = "distance", distance_unit = "miles" }, 
    { type = "fixed_speed", name = "bike", speed = 16.0, speed_unit = "kph" },
    { type = "time", time_unit = "minutes" },
    { type = "multimodal", this_mode = "bike", available_modes = ["walk", "bike", "drive"] }
]

constraint.type = "combined"
constraint.models = [
    { type = "multimodal", this_mode = "bike", available_modes = ["walk", "bike", "drive"] }
]

###
### DRIVE TOPOLOGY
###
[[search]] 
traversal.type = "combined"
traversal.models = [
    { type = "distance", distance_unit = "miles" }, 
    { type = "speed", speed_unit = "mph", speed_table_input_file = "drive/edges-speeds-mph-enumerated.txt.gz" },
    { type = "time", time_unit = "minutes" },
    { type = "multimodal", this_mode = "drive", available_modes = ["walk", "bike", "drive"] }
]

constraint.type = "combined"
constraint.models = [
    { type = "multimodal", this_mode = "drive", available_modes = ["walk", "bike", "drive"] }
]


[[plugin.input_plugins]]
type = "grid"
extent_format = "wkt"
grid = { type = "h3", resolution = 8 }
[plugin.input_plugins.population_source]
type = "acs"
acs_type = "five_year"
acs_year = 2022
acs_resolution = "census_tract"
acs_categories = ["B01001_001E"]

# split this row into three for walk/bike/drive analysis using
# different modal constraints in each search and a shared overall
# time limit of 40 minutes.
[[plugin.input_plugins]]
type = "inject"
format = "key_value"
write_mode = "overwrite"
key = "grid_search"
[[plugin.input_plugins.value._modes]]
trip_mode = "walk"
constraints = [
    { type = "time_limit", limit = 40.0, unit = "minutes" },
    { type = "allowed_modes", values = ["walk"]}
]
max_trip_legs = 1
[[plugin.input_plugins.value._modes]]
trip_mode = "bike"
constraints = [
    { type = "time_limit", limit = 40.0, unit = "minutes" },
    { type = "mode_time_limit", values = { walk = { limit = 10.0, unit = "minutes" }}},
    { type = "allowed_modes", values = ["walk", "bike"]}
]
max_trip_legs = 3
[[plugin.input_plugins.value._modes]]
trip_mode = "drive"
constraints = [
    { type = "time_limit", limit = 40.0, unit = "minutes" },
    { type = "mode_leg_distance_limit", values.walk = { leg.type = "first", constraint = { limit = 0.5, unit = "miles" }}},
    { type = "mode_leg_distance_limit", values.walk = { leg.type = "last", constraint = { limit = 0.5, unit = "miles" }}},
    { type = "allowed_modes", values = ["walk", "drive"]}
]
max_trip_legs = 3

[[plugin.input_plugins]]
type = "inject"
format = "key_value"
write_mode = "overwrite"
key = "max_trip_legs"
value = 3

[[plugin.input_plugins]]
type = "grid_search"

[[plugin.output_plugins]]
type = "traversal"
# tree = "geo_json"

[[plugin.output_plugins]]
type = "summary"

[[plugin.output_plugins]]
type = "bambam"
format = "aggregate"
binning = { type = "time", feature = "trip_time", values = [10,20,30,40], unit = "minutes" }
geometry_model = { type = "destination_point" }
isochrone_algorithm = { type = "k_nearest_concave_hull", k = 3 }
isochrone_format = "wkb"


[[plugin.output_plugins]]
type = "isochrone"

### MEP OPPORTUNITY DATA CONFIGURATION #################################
# assigns opportunities to search results based on a file or api data source
# and a taxonomy for MEP activity types.
# this example shows data loaded from the census LODES online file repository
# assigning activity types by NAICS sector id. 
[[plugin.output_plugins]]
type = "opportunity"

[plugin.output_plugins.model]
type = "combined"

[[plugin.output_plugins.model.models]]
type = "api"
vertex_input_file = "vertices-compass.csv.gz"
activity_column_names = ["entertainment", "food", "retail", "healthcare", "services", "jobs"]
table_orientation = "destination_vertex_oriented"

[plugin.output_plugins.model.models.opportunity_source]
type = "lodes"

# denver metro region coverage. replace this with a list of geoids for your study area.
# alternatively, study_region = national for job coverage across the US. 
# for information on year availability of data, see <https://lehd.ces.census.gov/data/lodes/LODES8/LODESTechDoc8.4.pdf>.
study_region = { type = "census", geoids = [
    "08001", # Adams County
    "08005", # Arapahoe County
    "08013", # Boulder County
    "08014", # Broomfield County
    "08031", # Denver County
    "08035", # Douglas County
    "08039", # Elbert County
    "08059", # Jefferson County
    "08123", # Weld County
] }

# collect LODES data at the tract level. while it is available at the block,
# the download + processing time is 10x that of census tracts. other possible values
# are `block`, `county` or `state`.
data_granularity = "census_tract"
# different editions of the dataset, we are choosing LODES v 8.0.
edition = "LODES8"
# Job Type, can have a value of “JT00” for All Jobs, “JT01” for Primary Jobs, “JT02” for All
# Private Jobs, “JT03” for Private Primary Jobs, “JT04” for All Federal Jobs, or “JT05” for
# Federal Primary Jobs
job_type = "JT00"
# Segment of the workforce, can have the values of:
# - S000: Total number of jobs (default)
# - SA01: Number of jobs of workers age 29 or younger
# - SA02: Number of jobs for workers age 30 to 54
# - SA03: Number of jobs for workers age 55 or older 
# - SE01: Number of jobs with earnings $1250/month or less
# - SE02: Number of jobs with earnings $1251/month to $3333/month
# - SE03: Number of jobs with earnings greater than $3333/month
# - SI01: Number of jobs in Goods Producing industry sectors
# - SI02: Number of jobs in Trade, Transportation, and Utilities industry sectors
# - SI03: Number of jobs in All Other Services industry sectors
segment = "S000"

# most recent year with all states. Alaska has no coverage from 2017-2022. we may
# want a "continental" variant which could support 2022.
year = 2022

[plugin.output_plugins.model.models.opportunity_source.activity_mapping]
# see https://lehd.ces.census.gov/lodes/LODES8/LODESTechDoc8.0.pdf
CNS01 = ["jobs"] # 11 (Agriculture, Forestry, Fishing and Hunting)
CNS02 = ["jobs"] # 21 (Mining, Quarrying, and Oil and Gas Extraction)
CNS03 = ["jobs"] # 22 (Utilities)
CNS04 = ["jobs"] # 23 (Construction)
CNS05 = ["jobs"] # 31-33 (Manufacturing)
CNS06 = ["jobs"] # 42 (Wholesale Trade)
CNS07 = ["jobs", "retail"] # 44-45 (Retail Trade)
CNS08 = ["jobs"] # 48-49 (Transportation and Warehousing)
CNS09 = ["jobs"] # 51 (Information)
CNS10 = ["jobs"] # 52 (Finance and Insurance)
CNS11 = ["jobs"] # 53 (Real Estate and Rental and Leasing)
CNS12 = ["jobs", "services"] # 54 (Professional, Scientific, and Technical Services)
CNS13 = ["jobs"] # 55 (Management of Companies and Enterprises)
CNS14 = ["jobs"] # 56 (Admin/Support/Waste Mgmt/Remediation Services)
CNS15 = ["jobs"] # 61 (Educational Services)
CNS16 = ["jobs", "healthcare"] # 62 (Health Care and Social Assistance)
CNS17 = ["jobs", "entertainment"] # 71 (Arts, Entertainment, and Recreation)
CNS18 = ["jobs", "food"] # 72 (Accommodation and Food Services)
CNS19 = ["jobs"] # 81 (Other Services [except Public Administration])
CNS20 = ["jobs"] # 92 (Public Administration)

[system]
parallelism = 8
response_persistence_policy = "persist_response_in_memory"

[system.response_output_policy]
type = "combined"

[[system.response_output_policy.policies]]
type = "file"
filename = "result.json"
[system.response_output_policy.policies.format]
type = "json"
newline_delimited = false

[[system.response_output_policy.policies]]
type = "file"
filename = "result.csv"
[system.response_output_policy.policies.format]
type = "csv"
sorted = true
[system.response_output_policy.policies.format.mapping]
grid_id = "request.grid_id"
lon = "request.origin_x"
lat = "request.origin_y"
mode = "request.trip_mode"
runtime = "search_runtime"
tree_size = "tree_size_count"
error = { optional = "error" }

opps_entertainment_total = "opportunity_totals.entertainment"
opps_retail_total = "opportunity_totals.retail"
opps_healthcare_total = "opportunity_totals.healthcare"
opps_services_total = "opportunity_totals.services"
opps_food_total = "opportunity_totals.food"
opps_jobs_total = "opportunity_totals.jobs"

opps_count_10 = "aggregate_opportunities.10.n_destinations"
opps_count_20 = "aggregate_opportunities.20.n_destinations"
opps_count_30 = "aggregate_opportunities.30.n_destinations"
opps_count_40 = "aggregate_opportunities.40.n_destinations"
opps_entertainment_10 = "aggregate_opportunities.10.opportunities.entertainment"
opps_retail_10 = "aggregate_opportunities.10.opportunities.retail"
opps_healthcare_10 = "aggregate_opportunities.10.opportunities.healthcare"
opps_services_10 = "aggregate_opportunities.10.opportunities.services"
opps_food_10 = "aggregate_opportunities.10.opportunities.food"
opps_jobs_10 = "aggregate_opportunities.10.opportunities.jobs"
opps_entertainment_20 = "aggregate_opportunities.20.opportunities.entertainment"
opps_retail_20 = "aggregate_opportunities.20.opportunities.retail"
opps_healthcare_20 = "aggregate_opportunities.20.opportunities.healthcare"
opps_services_20 = "aggregate_opportunities.20.opportunities.services"
opps_food_20 = "aggregate_opportunities.20.opportunities.food"
opps_jobs_20 = "aggregate_opportunities.20.opportunities.jobs"
opps_entertainment_30 = "aggregate_opportunities.30.opportunities.entertainment"
opps_retail_30 = "aggregate_opportunities.30.opportunities.retail"
opps_healthcare_30 = "aggregate_opportunities.30.opportunities.healthcare"
opps_services_30 = "aggregate_opportunities.30.opportunities.services"
opps_food_30 = "aggregate_opportunities.30.opportunities.food"
opps_jobs_30 = "aggregate_opportunities.30.opportunities.jobs"
opps_entertainment_40 = "aggregate_opportunities.40.opportunities.entertainment"
opps_retail_40 = "aggregate_opportunities.40.opportunities.retail"
opps_healthcare_40 = "aggregate_opportunities.40.opportunities.healthcare"
opps_services_40 = "aggregate_opportunities.40.opportunities.services"
opps_food_40 = "aggregate_opportunities.40.opportunities.food"
opps_jobs_40 = "aggregate_opportunities.40.opportunities.jobs"

isochrone_10 = "aggregate_opportunities.10.isochrone"
isochrone_20 = "aggregate_opportunities.20.isochrone"
isochrone_30 = "aggregate_opportunities.30.isochrone"
isochrone_40 = "aggregate_opportunities.40.isochrone"