def update_nash():
# Clean out the old object
env.vehicles_active = []
# Refill
for i, vid in enumerate(env.vids_active):
veh = dict()
# Who am I?
veh['id'] = vid
# Where am I?
veh['route index'] = env.traci.vehicle.getRouteIndex(veh['id'])
# What is my route?
veh['route id'] = env.traci.vehicle.getRouteID(veh['id'])
# What is my sink node?
veh['destination node'] = env.vehicles_dest[int(veh['id'][3:])]
# How far along the edge am I (m)?
veh['position on edge (m)'] = env.traci.vehicle.getLanePosition(
veh['id'])
# What edge am I on?
eid = env.traci.vehicle.getRoadID(veh['id'])
# Obtain the edge object. Here we can have two cases:
# 1. veh is at an intersection
# 2. veh is on an edg
# Veh is within an intersection. Assume the start of the next edge with a position on edge (m) of zero.
if ':' in eid:
veh['route'] = env.traci.route.getEdges(veh['route id'])
eid = veh['route'][veh['route index'] + 1]
veh['current edge'] = purr.filterdicts(env.edges, 'id', eid)[0]
veh['position on edge (m)'] = 0.000001
else:
veh['current edge'] = purr.filterdicts(env.edges, 'id', eid)[0]
# How far am I along the edge (weight)
veh['position on edge (s)'] = veh['position on edge (m)'] / float(
veh['current edge']['speed'])
# How much weight to the end of the edge?
edge_candidates = env.nx.get_edge_data(veh['current edge']['from'],
veh['current edge']['to'])
for i in range(len(edge_candidates)):
if veh['current edge']['id'] == edge_candidates[i]['id']:
veh['weight remaining'] = edge_candidates[i]['weight'] - veh[
'position on edge (s)']
break
continue
env.vehicles_active.append(veh)
continue
# ~ print('active vehicles',len(env.vehicles_active))
return
def main():
global OPTIONS
OPTIONS = get_options()
# Nodes
nod_xml = purr.mrf(OPTIONS.map_dir, r'*.nod.xml')
nodes = purr.readXMLtag(nod_xml, 'node')
print("nodes: ", len(nodes))
# Edges
edg_xml = purr.mrf(OPTIONS.map_dir, r'*.edg.xml')
edges = purr.readXMLtag(edg_xml, 'edge')
print("edges: ", len(edges))
# Load in Uber speed data
original_edges = speedhist.load_edges()
connections = speedhist.load_connections()
with open(OPTIONS.output, 'w') as f:
f.write("% from to weight id\n")
# Each each is a connection
n = 0
total = len(edges)
for edge in edges:
# Edges with shape
try:
length = length_from_shape(edge['shape'])
# Edges w/o shape
except KeyError:
node_from = purr.filterdicts(nodes, 'id', edge['from'])[0]
node_to = purr.filterdicts(nodes, 'id', edge['to'])[0]
length = length_from_nodes(node_from, node_to)
# Determine Speed
# ~ speed = float(edge['speed'])
conn = purr.filterdicts(connections, 'ID', edge['id'])[0]
child_edges = speedhist.get_edges(conn, original_edges)
_sum = 0
for ce in child_edges:
_sum += ce['mean']
speed = _sum / len(child_edges)
f.write("%s %s %.3f %s\n" %
(edge['from'], edge['to'], length / speed, edge['id']))
n += 1
purr.update(n, total, msg="Calculating edge lengths ")
continue
pass
print()
return
def add():
# Vehicle ID
index = env.veh_id_counter
env.veh_id_counter += 1
vid = "veh%d" % (index)
# Retrieve Route
try:
veh = env.vehicles[index]
except IndexError:
print(index,len(env.vehicles))
purr.pause()
# Create a route with the path eids
rid = "route%d" % (env.route_id_counter)
env.route_id_counter += 1
env.traci.route.add(rid,veh['shortest path']['eids'])
# Add a vehicle
env.traci.vehicle.add(vid,rid)
# Add the destination node to list of sink nodes so it may be found later
dest_node = purr.filterdicts(env.nodes,'id',veh['shortest path']['nids'][-1])[0]
env.vehicles_dest.append(dest_node)
return
def main():
global OPTIONS
OPTIONS = get_options()
edg_xml = purr.mrf(OPTIONS.map_dir, r'*.edg.xml')
edges = purr.readXMLtag(edg_xml, 'edge')
original_edges = speedhist.load_edges()
connections = speedhist.load_connections()
fn = 'hhh.edg.xml'
with open(fn, 'w') as f:
f.write('<edges>')
n = 0
total = len(edges)
for edge in edges:
conn = purr.filterdicts(connections, 'ID', edge['id'])[0]
child_edges = speedhist.get_edges(conn, original_edges)
_sum = 0
for ce in child_edges:
_sum += ce['mean']
speed = _sum / len(child_edges)
f.write(
'\n\t<edge id="%s" from="%s" to="%s" priority="%s" numLanes="%s" speed="%.3f"/>'
% (edge['id'], edge['from'], edge['to'], edge['priority'],
edge['numLanes'], speed))
n += 1
purr.update(n, total)
continue
f.write('\n</edges>')
return
def add():
# Vehicle ID
index = env.veh_id_counter
env.veh_id_counter += 1
vid = "veh%d" % (index)
# Retrieve Route
veh = env.vehicles[index]
# Create a route with the path eids
rid = "route%d" % (env.route_id_counter)
env.route_id_counter += 1
env.traci.route.add(rid, veh['shortest path']['eids'])
# Add a vehicle
env.traci.vehicle.add(vid, rid)
# Add the destination node to list of sink nodes so it may be found later
dest_node = purr.filterdicts(env.nodes, 'id',
veh['shortest path']['nids'][-1])[0]
env.vehicles_dest.append(dest_node)
env.recalculate_nash = True
env.update_vehicle_info = True
return
def add_nash(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'] = nxops.dijkstra(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
shortest_path['weight'], shortest_path['eids'] = nxops.path_info(
env.nx, shortest_path['nids'])
# Create a route with the path eids
rid = "route%d" % (env.route_id_counter)
env.route_id_counter += 1
traci.route.add(rid, shortest_path['eids'])
# Add a vehicle
traci.vehicle.add(vid, rid)
# Add the destination node to list of sink nodes so it may be found later
dest_node = purr.filterdicts(env.nodes, 'id', shortest_path['nids'][-1])[0]
env.vehicles_dest.append(dest_node)
# The nash equilibrium will have to be recalculated since another contestant is being introduced.
env.recalculate_nash = True
# Create the vehicle dictionary
veh = {
"id": vid,
"source": spawn_edge['from'],
"destination": sink_edge['to'],
"shortest path length": shortest_path['weight'],
"diversion path length": None,
"sample time": None,
"target nid": None,
"target eid": None
}
env.vehicles.append(veh)
return
def main():
global OPTIONS
OPTIONS = get_options()
# Nodes
nod_xml = purr.mrf(OPTIONS.map_dir, r'*.nod.xml')
nodes = purr.readXMLtag(nod_xml, 'node')
print("nodes: ", len(nodes))
# Edges
edg_xml = purr.mrf(OPTIONS.map_dir, r'*.edg.xml')
edges = purr.readXMLtag(edg_xml, 'edge')
print("edges: ", len(edges))
with open(OPTIONS.output, 'w') as f:
f.write("% from to weight id\n")
# Each each is a connection
n = 0
total = len(edges)
for edge in edges:
if edge['from'] == edge['to']:
n += 1
continue
# Edges with shape
try:
length = length_from_shape(edge['shape'])
# Edges w/o shape
except KeyError:
node_from = purr.filterdicts(nodes, 'id', edge['from'])[0]
node_to = purr.filterdicts(nodes, 'id', edge['to'])[0]
length = length_from_nodes(node_from, node_to)
speed = float(edge['speed'])
f.write("%s %s %.3f %s\n" %
(edge['from'], edge['to'], length / speed, edge['id']))
n += 1
purr.update(n, total, msg="Calculating edge lengths ")
continue
pass
print()
return
def update():
env.update_vehicle_info = False
# Clean out the old object
env.vehicles_active = []
# Refill
total = len(env.vehicles)
for i,v in enumerate(env.vehicles):
veh = dict()
# Who am I?
veh['id'] = v['id']
# Where am I?
veh['route index'] = env.traci.vehicle.getRouteIndex(veh['id'])
# What is my route?
veh['route id'] = env.traci.vehicle.getRouteID(veh['id'])
# What is my sink node?
veh['destination node'] = env.vehicles_dest[int(veh['id'][3:])]
if veh['route index'] < 0:
veh['route index'] = 0
veh['position on edge (m)'] = 0.000001
eid = env.traci.route.getEdges(veh['route id'])[0]
else:
# How far along the edge am I (m)?
veh['position on edge (m)'] = env.traci.vehicle.getLanePosition(veh['id'])
# What edge am I on?
eid = env.traci.vehicle.getRoadID(veh['id'])
# Obtain the edge object. Here we can have two cases:
# 1. veh is at an intersection
# 2. veh is on an edg
# Veh is within an intersection. Assume the start of the next edge with a position on edge (m) of zero.
if ':' in eid:
veh['route'] = env.traci.route.getEdges(veh['route id'])
eid = veh['route'][veh['route index']+1]
veh['current edge'] = purr.filterdicts(env.edges,'id',eid)[0]
veh['position on edge (m)'] = 0.000001
else:
veh['current edge'] = purr.filterdicts(env.edges,'id',eid)[0]
# How far am I along the edge (weight)
veh['position on edge (s)'] = veh['position on edge (m)'] / float(veh['current edge']['speed'])
# How much weight to the end of the edge?
edge_candidates = env.nx.get_edge_data(veh['current edge']['from'],veh['current edge']['to'])
for j in range(len(edge_candidates)):
if veh['current edge']['id'] == edge_candidates[j]['id']:
veh['weight remaining'] = edge_candidates[j]['weight'] - veh['position on edge (s)']
break
continue
# What is the weight of Veh --> Dest
veh['veh2dest weight'] = veh['weight remaining'] + nxops.path_info(env.nx,nx.dijkstra_path(env.nx,veh['current edge']['to'],veh['destination node']['id']))[0]
env.vehicles_active.append(veh)
purr.update(i+1,total,"Updating vehicle location data ")
# ~ env.recalculate_nash = True
continue
print()
return
