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RELOG/src/graph/dist.jl

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1.8 KiB

# RELOG: Reverse Logistics Optimization
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
# Released under the modified BSD license. See COPYING.md for more details.
using Geodesy
using NearestNeighbors
using DataFrames
Base.@kwdef mutable struct _KnnDrivingDistance
tree = nothing
ratios = nothing
end
mutable struct _EuclideanDistance end
function _calculate_distance(
source_lat,
source_lon,
dest_lat,
dest_lon,
::_EuclideanDistance,
)::Float64
x = LLA(source_lat, source_lon, 0.0)
y = LLA(dest_lat, dest_lon, 0.0)
return round(euclidean_distance(x, y) / 1000.0, digits = 3)
end
function _calculate_distance(
source_lat,
source_lon,
dest_lat,
dest_lon,
metric::_KnnDrivingDistance,
)::Float64
if metric.tree === nothing
basedir = joinpath(dirname(@__FILE__), "..", "..", "data")
csv_filename = joinpath(basedir, "dist_driving.csv")
# Download pre-computed driving data
if !isfile(csv_filename)
_download_zip(
"https://axavier.org/RELOG/0.6/data/dist_driving_0b9a6ad6.zip",
basedir,
csv_filename,
0x0b9a6ad6,
)
end
# Fit kNN model
df = DataFrame(CSV.File(csv_filename))
coords = Matrix(df[!, [:source_lat, :source_lon, :dest_lat, :dest_lon]])'
metric.ratios = Matrix(df[!, [:ratio]])
metric.tree = KDTree(coords)
end
# Compute Euclidean distance
dist_euclidean = _calculate_distance(
source_lat,
source_lon,
dest_lat,
dest_lon,
_EuclideanDistance(),
)
# Predict ratio
idxs, _ = knn(metric.tree, [source_lat, source_lon, dest_lat, dest_lon], 5)
ratio_pred = mean(metric.ratios[idxs])
return round(dist_euclidean * ratio_pred, digits = 3)
end