Switch from Euclidean to approximate driving distance

test/graph/build_test.jl

@@ -21,7 +21,7 @@ using RELOG
     @test node.outgoing_arcs[1].source.location.name == "C1"
     @test node.outgoing_arcs[1].dest.location.plant_name == "F1"
     @test node.outgoing_arcs[1].dest.location.location_name == "L1"
-    @test node.outgoing_arcs[1].values["distance"] == 1095.62
+    @test node.outgoing_arcs[1].values["distance"] == 1695.364
 
     node = process_node_by_location_name["L1"]
     @test node.location.plant_name == "F1"

test/graph/dist_test.jl

@@ -0,0 +1,25 @@
+# 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 RELOG
+
+@testset "KnnDrivingDistance" begin
+    # Euclidean distance between Chicago and Indianapolis
+    @test RELOG._calculate_distance(
+        41.866,
+        -87.656,
+        39.764,
+        -86.148,
+        RELOG._EuclideanDistance(),
+    ) == 265.818
+
+    # Approximate driving distance between Chicago and Indianapolis
+    @test RELOG._calculate_distance(
+        41.866,
+        -87.656,
+        39.764,
+        -86.148,
+        RELOG._KnnDrivingDistance(),
+    ) == 316.43
+end

test/runtests.jl

@@ -11,6 +11,7 @@ using Test
     end
     @testset "Graph" begin
         include("graph/build_test.jl")
+        include("graph/dist_test.jl")
     end
     @testset "Model" begin
         include("model/build_test.jl")