Tests and documentation for intersection clustering.

Changed type HighwayCluster to HighwaySet.
Intersection clustering requires nodes to be in ENU coordinates.

Issue #31.

Signed-off-by: Ted Steiner <tsteiner2@gmail.com>

docs/data.rst

@@ -36,6 +36,20 @@ The only required input is the highway dictionary returned by "getOSMData()." A
 dictionary of "Intersection" types is returned, intexed by the node ID of the
 intersection.
 
+In some cases, such as boulevards and other divided roads, OpenStreetMap represents a street as two one-way highways.  This can result in multiple "intersections" detected per true intersection. If desired, these intersections can be "clustered," replacing these multiple intersection-lets with a single node. To do this, we must first cluster the highways, by gathering all highways with a common name (note that this is dependent on the quality of street name tags in your source data). 
+We then search for proximal instances of these highway sets crossing one another. Flag max_dist can be used to change the required proximity of the nodes to be considered an intersection (the default is 15 meters). Note that this proximity is the maximum distance the node can be from the centroid of all nodes in the intersection at the time the node is added.
+
+The code to accomplish this is as follows:
+
+.. code-block:: python 
+
+    highway_sets = findHighwaySets(highways)
+    intersection_mapping, intersection_nodes = findIntersectionClusters(nodes,intersections,highway_sets,max_dist=15)
+    replaceHighwayNodes!(highways,intersection_mapping)
+
+The optional flag "max_dist" is in the units of your "nodes" object. 
+
+
 Working with Segments
 ---------------------
 

src/OpenStreetMap.jl

@@ -19,6 +19,7 @@ export lla2enu, lla2ecef, ecef2lla, ecef2enu
 export roadways, walkways, cycleways, classify
 export createGraph, shortestRoute, fastestRoute, distance, routeEdges
 export nodesWithinRange, nodesWithinDrivingDistance, nodesWithinDrivingTime
+export findHighwaySets, findIntersectionClusters, replaceHighwayNodes!
 export simCityGrid
 
 include("types.jl")

src/highways.jl

@@ -53,9 +53,9 @@ function cycleways(highways::Dict{Int,Highway})
     return cycles
 end
 
-### Find Highway Pairs (two one-way highways with same name) ###
-function findHighwayPairs( highways::Dict{Int,Highway} )
-    clusters = HighwayCluster[]
+### Find sets of same-named highways ###
+function findHighwaySets( highways::Dict{Int,Highway} )
+    clusters = HighwaySet[]
 
     street_names = (String,String,Int)[]
 
@@ -84,7 +84,7 @@ function findHighwayPairs( highways::Dict{Int,Highway} )
         end
 
         if length(cluster) > 1
-            push!(clusters,HighwayCluster(Set(cluster)))
+            push!(clusters,HighwaySet(Set(cluster)))
         end
     end
 

src/intersections.jl

@@ -39,7 +39,7 @@ function findIntersections(highways::Dict{Int,Highway})
 end
 
 ### Generate a new list of highways divided up by intersections
-function segmentHighways(nodes, highways, intersections, classes, levels=Set(1:10))
+function segmentHighways(nodes, highways::Dict{Int,Highway}, intersections, classes, levels=Set(1:10))
     segments = Segment[]
     inters = Set(keys(intersections))
 
@@ -70,7 +70,7 @@ function segmentHighways(nodes, highways, intersections, classes, levels=Set(1:1
 end
 
 ### Generate a list of highways from segments, for plotting purposes
-function highwaySegments(segments::Vector{Segment})
+function highwaySegments( segments::Vector{Segment} )
     highways = Dict{Int,Highway}()
 
     for k = 1:length(segments)
@@ -82,7 +82,10 @@ end
 
 
 ### Cluster highway intersections into higher-level intersections ###
-function findIntersectionClusters(nodes, intersections_in, highway_clusters; max_dist=15.0)
+function findIntersectionClusters( nodes::Dict{Int,ENU}, 
+                                   intersections_in::Dict{Int,Intersection}, 
+                                   highway_clusters::Vector{HighwaySet}; 
+                                   max_dist=15.0 )
     hwy_cluster_mapping = Dict{Int,Int}()
     for k = 1:length(highway_clusters)
         hwys = [highway_clusters[k].highways...]
@@ -176,7 +179,7 @@ end
 
 
 ### Replace Nodes in Highways Using Node Remapping
-function replaceHighwayNodes!(highways::Dict{Int,Highway}, node_map::Dict{Int,Int})
+function replaceHighwayNodes!( highways::Dict{Int,Highway}, node_map::Dict{Int,Int} )
     for (key,hwy) in highways
         all_equal = true
         for k = 1:length(hwy.nodes)

src/types.jl

@@ -40,7 +40,7 @@ type Intersection
 end
 Intersection() = Intersection(Set{Int}())
 
-type HighwayCluster # Multiple highways representing a single "street" with a common name
+type HighwaySet # Multiple highways representing a single "street" with a common name
     highways::Set{Int}
 end
 

test/intersections.jl

@@ -0,0 +1,36 @@
+# Test intersection detection and clustering
+
+module TestIntersections
+
+using OpenStreetMap
+using Base.Test
+using Compat
+
+import OpenStreetMap: Bounds, centerBounds
+
+MAP_FILENAME = "tech_square.osm"
+
+bounds = Bounds(42.3637, 42.3655, -71.0919, -71.0893)
+
+bounds_ENU = lla2enu(bounds)
+
+nodesLLA, hwys, builds, feats = getOSMData(MAP_FILENAME)
+nodes = lla2enu(nodesLLA,centerBounds(bounds))
+
+# Find intersections in map
+intersections = findIntersections(hwys)
+@test length(intersections) == 91
+
+# Find Highway Sets
+highway_sets = findHighwaySets(hwys)
+@test length(highway_sets) == 4
+
+# Cluster intersections
+intersection_cluster_mapping, intersection_cluster_nodes = findIntersectionClusters(nodes,intersections,highway_sets,max_dist=15)
+replaceHighwayNodes!(hwys,intersection_cluster_mapping)
+intersections_clustered = findIntersections(hwys)
+@test length(intersections_clustered) == 82
+
+end # module TestIntersections
+
+

test/runtests.jl

@@ -4,6 +4,7 @@ tests = [
     "crop_map",
     "classes",
     "coordinates",
+    "intersections",
     "routes",
     "plots" ]