def assign():
print('\nBaseline Assignments')
N = env.traci.vehicle.getIDCount()
M = len(env.target_nodes)
for n in range(N):
# Determine which target is the least far away
cost = [float(env.dist.cost(n, m)) for m in range(M)]
min_cost = min(cost)
itar = None
for i, c in enumerate(cost):
if c == min_cost:
itar = i
break
continue
print("Veh=%d Tar=%d Cost=%.3f" % (n, itar, min_cost))
# Then assign the route
veh = env.vehicles_active[n]
path = dict()
# Path veh --> target
path_veh2tar = dict()
tar = env.target_nodes[itar]
path_veh2tar['nids'] = nx.dijkstra_path(env.nx,
veh['current edge']['to'],
tar['id'])
path_veh2tar['nids'].insert(0, veh['current edge']['from'])
path_veh2tar['weight'], path_veh2tar['eids'] = nxops.path_info(
env.nx, path_veh2tar['nids'])
# path target --> dest
path_tar2dest = dict()
path_tar2dest['nids'] = nx.dijkstra_path(env.nx, tar['id'],
veh['destination node']['id'])
path_tar2dest['weight'], path_tar2dest['eids'] = nxops.path_info(
env.nx, path_tar2dest['nids'])
# Combine together
path['nids'] = purr.flattenlist(
[path_veh2tar['nids'], path_tar2dest['nids']])
path['weight'] = path_veh2tar['weight'] + path_tar2dest['weight']
path['eids'] = purr.flattenlist(
[path_veh2tar['eids'], path_tar2dest['eids']])
# Update vehicle tracking data
index = int(veh['id'][3:])
env.vehicles[index]['target nid'] = tar['id']
env.vehicles[index]['target eid'] = path['eids'][path['nids'].index(
tar['id'])]
env.vehicles[index]['diversion path length'] = path['weight']
env.vehicles[index]['shortest path length'] = veh['veh2dest weight']
# Route the vehicle with traci
env.traci.vehicle.setRoute(veh['id'], path['eids'])
continue
print("Complete!")
return
def main():
map_dir = "../london-seg4/data/100"
edges = purr.readXMLtag(purr.mrf(map_dir, r'*.edg.xml'), 'edge')
connections = purr.readCSV(purr.mrf(map_dir, r'*.conn.csv'))
stats = purr.readCSV('edges.stats.csv')
purr.head(edges)
purr.head(connections)
purr.head(stats)
# ~ return
eids = [edge['id'] for edge in edges]
connections = purr.flattenlist(
purr.batchfilterdicts(connections,
'CONNECTION ID',
eids,
show_progress=True))
print()
total = len(connections) - 1
for i, conn in enumerate(connections):
try:
speeds = purr.flattenlist(
purr.batchfilterdicts(stats, 'ID', conn['EDGE IDS']))
except TypeError:
speeds = purr.flattenlist(
purr.batchfilterdicts(stats, 'ID', [conn['EDGE IDS']]))
mean = [float(speed['mean']) for speed in speeds]
std = [float(speed['std']) for speed in speeds]
p50 = [float(speed['p50']) for speed in speeds]
p85 = [float(speed['p85']) for speed in speeds]
edges[i]['mean'] = "%.3f" % (statistics.mean(mean))
edges[i]['std'] = "%.3f" % (statistics.mean(std))
edges[i]['p50'] = "%.3f" % (statistics.mean(p50))
edges[i]['p85'] = "%.3f" % (statistics.mean(p85))
purr.update(i, total, "Correlating speeds with edges ")
continue
with open("edg.xml", 'w') as f:
f.write("<edges>")
for edge in edges:
f.write("\n\t<edge")
for key, val in edge.items():
f.write(' %s="%s"' % (key, val))
f.write("/>")
continue
f.write("\n</edges>")
print("COMPLETE!")
return
def set_route(tA):
cDest = tA[0]
utilities = tA[1]
# Use this info to route.
for x in range(env.nash_assigner.N):
print('Veh=%d Tar=%.0f Util=%.3f'%(x,cDest[x],utilities[x]))
veh = env.vehicles_active[x]
path = dict()
# Not worth the effort. Go home
if np.isnan(cDest[x]):
path['nids'] = nx.dijkstra_path(env.nx,veh['current edge']['to'],veh['destination node']['id'])
path['nids'].insert(0,veh['current edge']['from'])
path['weight'] , path['eids'] = nxops.path_info(env.nx,path['nids'])
# Otherwise, go to the target
else:
# Path veh --> target
path_veh2tar = dict()
tar = env.target_nodes[int(cDest[x])]
path_veh2tar['nids'] = nx.dijkstra_path(env.nx,veh['current edge']['to'],tar['id'])
path_veh2tar['nids'].insert(0,veh['current edge']['from'])
path_veh2tar['weight'] , path_veh2tar['eids'] = nxops.path_info(env.nx,path_veh2tar['nids'])
# path target --> dest
path_tar2dest = dict()
path_tar2dest['nids'] = nx.dijkstra_path(env.nx,tar['id'],veh['destination node']['id'])
path_tar2dest['weight'], path_tar2dest['eids'] = nxops.path_info(env.nx,path_tar2dest['nids'])
# Combine together
path['nids'] = purr.flattenlist([path_veh2tar['nids'],path_tar2dest['nids']])
path['weight'] = path_veh2tar['weight'] + path_tar2dest['weight']
path['eids'] = purr.flattenlist([path_veh2tar['eids'],path_tar2dest['eids']])
# Update vehicle tracking data
index = int(veh['id'][3:])
env.vehicles[index]['target nid'] = tar['id']
env.vehicles[index]['target eid'] = path['eids'][path['nids'].index(tar['id'])]
env.vehicles[index]['diversion path length'] = path['weight']
env.vehicles[index]['shortest path length'] = veh['veh2dest weight']
# Route the vehicle with traci
try:
env.traci.vehicle.setRoute(veh['id'],path['eids'])
except env.traci.TraCIException:
pass
continue
return
def get_valid_edges_from_point(point,radius):
print("Finding valid edges for point",point,"and radius",radius)
x,y = point
# Find the nodes that are within the radius
nod_xml = purr.mrf(env.options.map_dir,r'*.nod.xml')
nodes_all = purr.readXMLtag(nod_xml,'node')
nodes_valid = []
for node in nodes_all:
if abs(x - float(node['x'])) <= radius and abs(y - float(node['y'])) <= radius:
nodes_valid.append(node)
continue
nodes_valid_ids = [node['id'] for node in nodes_valid]
# Now that we know the nodes, we can pick out valid edges that the node begins at
edg_xml = purr.mrf(env.options.map_dir,r'*.edg.xml')
edges_all = purr.readXMLtag(edg_xml,'edge')
edges_filtered = purr.batchfilterdicts(edges_all,'from',nodes_valid_ids,show_progress=True)
edges_filtered = purr.flattenlist(edges_filtered)
edges_valid = []
for edge in edges_filtered:
if not ':' in edge['id']:
edges_valid.append(edge)
print("Found %d edge candidates." % (len(edges_valid)))
return edges_valid
def initialize(traci):
print("Initializing targets...",end='')
# load the node target IDs from file.
target_node_ids = []
try:
with open(env.options.targets,'r') as f:
for line in f:
if '%' in line:
continue
target_node_ids.append(line.strip())
continue
except FileNotFoundError:
print('\nCould not open "%s"' % (env.options.targets))
sys.exit(1)
# Then retrieve the nodes
env.target_nodes = purr.flattenlist(purr.batchfilterdicts(env.nodes,'id',target_node_ids))
# Mark the targets on the map
for node in env.target_nodes:
xy = (float(node['x']),float(node['y']))
preprocess.add_radius_polygon(traci,xy,30,(255,0,255))
# Add target dictionaries to the env
for node in env.target_nodes:
target = {
'id':node['id'],
'sampling times':[],
'sampling vids':[]
}
env.targets.append(target)
continue
print("Complete!")
return
def get_assignments():
print("\nGetting Nash Assignments.")
start = purr.now()
# Get assigments
index = env.traci.vehicle.getIDCount() - 1
t = env.nash_assigners[index].getAssignments()
cDest = t[0]
utilities = t[1]
# Use this info to route.
for x in range(env.nash_assigners[index].N):
print('Veh=%d Tar=%.0f Util=%.3f' % (x, cDest[x], utilities[x]))
veh = env.vehicles_active[x]
path = dict()
# Not worth the effort. Go home
if np.isnan(cDest[x]):
path['nids'] = nx.dijkstra_path(env.nx, veh['current edge']['to'],
veh['destination node']['id'])
path['nids'].insert(0, veh['current edge']['from'])
path['weight'], path['eids'] = nxops.path_info(
env.nx, path['nids'])
# Otherwise, go to the target
else:
# Path veh --> target
path_veh2tar = dict()
tar = env.target_nodes[int(cDest[x])]
path_veh2tar['nids'] = nx.dijkstra_path(env.nx,
veh['current edge']['to'],
tar['id'])
path_veh2tar['nids'].insert(0, veh['current edge']['from'])
path_veh2tar['weight'], path_veh2tar['eids'] = nxops.path_info(
env.nx, path_veh2tar['nids'])
# path target --> dest
path_tar2dest = dict()
path_tar2dest['nids'] = nx.dijkstra_path(
env.nx, tar['id'], veh['destination node']['id'])
path_tar2dest['weight'], path_tar2dest['eids'] = nxops.path_info(
env.nx, path_tar2dest['nids'])
# Combine together
path['nids'] = purr.flattenlist(
[path_veh2tar['nids'], path_tar2dest['nids']])
path['weight'] = path_veh2tar['weight'] + path_tar2dest['weight']
path['eids'] = purr.flattenlist(
[path_veh2tar['eids'], path_tar2dest['eids']])
# Update vehicle tracking data
index = int(veh['id'][3:])
env.vehicles[index]['target nid'] = tar['id']
env.vehicles[index]['target eid'] = path['eids'][
path['nids'].index(tar['id'])]
env.vehicles[index]['diversion path length'] = path['weight']
env.vehicles[index]['shortest path length'] = veh[
'veh2dest weight']
# Route the vehicle with traci
env.traci.vehicle.setRoute(veh['id'], path['eids'])
continue
print("Complete after %.3f" % (purr.elapsed(start)))
return
def recalculate():
env.recalculate_nash = False
print()
print("Retrieving spatial vehicle data...", end='')
st = purr.now()
vehicle.update_nash()
print("Complete after %.3fs" % (purr.elapsed(st)))
print("Recalculating target assignments...", end='')
st = purr.now()
N = len(env.vids_active) - 1
# ~ N = 10
env.nash_assigner = sysSim.nashAssigner(N=N,
M=len(env.targets),
R=200,
tau=1,
dist=env.dist)
t = env.nash_assigner.getAssignments()
print("Complete after %.3fs" % (purr.elapsed(st)))
cDest = t[0]
utilities = t[1]
# ~ print('Assigining vehicles to new targets.')
for x in range(env.nash_assigner.N):
# ~ print('vehicle,target,utililty: (%d,%.0f,%.3f)'%(x,cDest[x],utilities[x]))
veh = env.vehicles_active[x]
path = dict()
# Not worth the effort. Go home
if np.isnan(cDest[x]):
path['nids'] = nxops.dijkstra(env.nx, veh['current edge']['to'],
veh['destination node']['id'])
path['nids'].insert(0, veh['current edge']['from'])
path['weight'], path['eids'] = nxops.path_info(
env.nx, path['nids'])
# Otherwise, go to the target
else:
# Path veh --> target
path_veh2tar = dict()
tar = env.target_nodes[int(cDest[x])]
path_veh2tar['nids'] = nxops.dijkstra(env.nx,
veh['current edge']['to'],
tar['id'])
path_veh2tar['nids'].insert(0, veh['current edge']['from'])
path_veh2tar['weight'], path_veh2tar['eids'] = nxops.path_info(
env.nx, path_veh2tar['nids'])
# path target --> dest
path_tar2dest = dict()
path_tar2dest['nids'] = nxops.dijkstra(
env.nx, tar['id'], veh['destination node']['id'])
path_tar2dest['weight'], path_tar2dest['eids'] = nxops.path_info(
env.nx, path_tar2dest['nids'])
# Combine together
path['nids'] = purr.flattenlist(
[path_veh2tar['nids'], path_tar2dest['nids']])
path['weight'] = path_veh2tar['weight'] + path_tar2dest['weight']
path['eids'] = purr.flattenlist(
[path_veh2tar['eids'], path_tar2dest['eids']])
# Update vehicle tracking data
index = int(veh['id'][3:])
env.vehicles[index]['target nid'] = tar['id']
env.vehicles[index]['target eid'] = path['eids'][
path['nids'].index(tar['id'])]
env.vehicles[index]['diversion path length'] = path['weight']
# Route the vehicle with traci
env.traci.vehicle.setRoute(veh['id'], path['eids'])
continue
# ~ print('Dijkstra: Calls=%d Time=%.3fs Avg=%.3fs' % (env.fs_dijkstra['calls'],env.fs_dijkstra['time'],env.fs_dijkstra['time']/env.fs_dijkstra['calls']))
return
def main():
map_dir = "../orlando-seg1/"
# Load Nodes
nod_xml = purr.mrf(map_dir, r'*.nod.xml')
nodes = purr.readXMLtag(nod_xml, 'node')
# Load Edges
edg_xml = purr.mrf(map_dir, r'*edg.xml')
edges = purr.readXMLtag(edg_xml, 'edge')
# Correlate From/To Nodes with edges
print('Finding Nodes From')
nids_from = [edge['from'] for edge in edges]
nodes_from = purr.flattenlist(
purr.batchfilterdicts(nodes, 'id', nids_from, show_progress=True))
print('\nFinding Nodes To')
nids_to = [edge['to'] for edge in edges]
nodes_to = purr.flattenlist(
purr.batchfilterdicts(nodes, 'id', nids_to, show_progress=True))
print()
# Create the CSV for Nodes
node_attr = [
'id', 'x', 'y', 'z', 'type', 'tlType', 'tl', 'radius', 'keepClear',
'rightOfWay'
]
node_csv = 'node.csv'
with open(node_csv, 'w') as csv:
# Column Names
csv.write(node_attr[0])
for attr in node_attr[1:]:
csv.write(',%s' % (attr))
csv.write('\n')
# Fill in rows
n = 0
total = len(nodes)
for node in nodes:
csv.write(node[node_attr[0]])
for attr in node_attr[1:]:
csv.write(',')
try:
csv.write(node[attr])
except KeyError:
pass
continue
csv.write('\n')
n += 1
purr.update(n, total, msg="Writing node.csv ")
continue
pass
print()
# Create the CSV for Edges
edge_attr = [
'id', 'from', 'to', 'type', 'numLanes', 'speed', 'priority',
'spreadType', 'width', 'name', 'endOffset', 'sidewalkWidth'
]
edge_allow = [
'emergency', 'authority', 'passenger', 'hov', 'taxi', 'bus',
'delivery', 'truck', 'trailer', 'motorcyle', 'moped'
]
edge_csv = 'edge.csv'
with open(edge_csv, 'w') as csv:
# Column Names
csv.write(edge_attr[0])
for attr in edge_attr[1:]:
csv.write(',%s' % (attr))
csv.write(',length')
for attr in edge_allow:
csv.write(',%s' % (attr))
csv.write('\n')
# Fill in Rows
n = 0
total = len(edges)
for i, edge in enumerate(edges):
# id
csv.write(edge[edge_attr[0]])
# Attributes
for attr in edge_attr[1:]:
csv.write(',')
try:
csv.write(edge[attr])
except KeyError:
pass
continue
# Length
try:
csv.write(',%.3f' % (shape_len(edge['shape'])))
except KeyError:
x0 = float(nodes_from[i]['x'])
y0 = float(nodes_from[i]['y'])
x1 = float(nodes_to[i]['x'])
y1 = float(nodes_to[i]['y'])
csv.write(',%.3f' % (dist(x0, y0, x1, y1)))
# Vehicle Types
vtype_allowed = [0 for item in edge_allow]
# Has neither tag. It is assumed that all vehicles are allowed.
if (not purr.xml_has_atrribute(edge, 'allow')) and (
not purr.xml_has_atrribute(edge, 'disallow')):
vtype_allowed = [1 for item in edge_allow]
# Has the allow tag. Those specified are allowed
elif (purr.xml_has_atrribute(edge, 'allow')):
vtypes = edge['allow'].split(' ')
for j, vt in enumerate(edge_allow):
if vt in vtypes:
edge_allow[j] = 1
continue
# Lastly it has the dissalow tag, and what is not speficied is allowed
elif (purr.xml_has_atrribute(edge, 'disallow')):
vtype_allowed = [1 for item in edge_allow]
vtypes = edge['allow'].split(' ')
for j, vt in enumerate(edge_allow):
if vt in vtypes:
edge_allow[j] = 0
continue
# ~ for j,vt in enumerate(edge_allow):
# ~ print(edge_allow[j],vtype_allowed[j])
for vt in vtype_allowed:
csv.write(',%d' % (vt))
csv.write('\n')
n += 1
purr.update(n, total, 'Writing edges.csv ')
continue
pass
print('\nComplete.')
return
def add_baseline(traci):
# Vehicle ID
vid = "veh%d" % (env.veh_id_counter)
env.veh_id_counter += 1
# Selected a path. It must be have a conenction between spawn and sink
ntrys = 0
shortest_path = {
'weight': None,
'nids': None,
'eids': None
}
while True:
# Pick a random spawn and sink edge
spawn_edge = random.choice(env.spawn_edge)
sink_edge = random.choice(env.sink_edge)
# Determine the shortest path
try:
shortest_path['nids'] = nx.dijkstra_path(env.nx,
spawn_edge['from'],
sink_edge['to'])
break
except nx.NetworkXNoPath:
ntrys += 1
print(
"%s: Cannont create a path between %s and %s... Trying again (%d)."
% (vid, spawn_edge['from'], sink_edge['to'], ntrys))
continue
# At this point, it is assumed that the path is connected
# Now the weight of the path and the edge eids of the path is determined
shortest_path['weight'], shortest_path['eids'] = nxops.path_info(
env.nx, shortest_path['nids'])
# display the spawn and sink destions
spawn_node, sink_node = purr.flattenlist(
purr.batchfilterdicts(
env.nodes, 'id',
[shortest_path['nids'][0], shortest_path['nids'][-1]]))
spawn_point = (float(spawn_node['x']), float(spawn_node['y']))
sink_point = (float(sink_node['x']), float(sink_node['y']))
# Find the optimal target point to visit
path = nxops.naive_target_selection(env.nx, shortest_path['nids'],
env.target_nodes)
# Create a route with the path eids
rid = "route%d" % (env.route_id_counter)
env.route_id_counter += 1
traci.route.add(rid, path['path_eids'])
# Add a vehicle
traci.vehicle.add(vid, rid)
# Determine the edge immediately after the node
target_node_index = path['path_nids'].index(path['target_node']['id'])
target_eid = path['path_eids'][target_node_index]
# Create the vehicle dictionary
veh = {
"id": vid,
"source": spawn_node['id'],
"destination": sink_node['id'],
"shortest path length": shortest_path['weight'],
"diversion path length": path['weight'],
"sample time": None,
"target nid": path['target_node']['id'],
"target eid": target_eid,
"path": path
}
env.vehicles.append(veh)
return
