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map_computor.py
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map_computor.py
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# -*- coding: utf-8 -*-
'''
1) interacting with SUMO, including
retrive values, set lights
2) interacting with sumo_agent, including
returning status, rewards, etc.
'''
import numpy as np
import math
import os
import json
import sys
import xml.etree.ElementTree as ET
from sys import platform
from cityflow_agent import Vehicles
import collections
import cityflow as engine
ccccc
'''
TRAFFIC_FILE = "anon_4_4_700_0.3_synthetic.json"
PATH_TO_CONF = "conf/4_4"
ROAD_NET_FILE = "./data/4_4/roadnet_4_4.json"
CITYFLOW_CONFIG_FILE = "./data/4_4/cityflow.config"
DIR = "data/4_4/"
ROADNETFILE = "roadnet_4_4.json"
'''
from agent import State
from keras import backend as K
###### Please Specify the location of your traci module
# --- my modification ---
NUM_OF_NEIGHBORS = 4
yeta = 0.15
tao = 2
constantC = 40.0
carWidth = 3.3
grid_width = 4
area_length, area_width = 600, 600
direction_lane_dict = {"NSG": [1, 0], "SNG": [1, 0], "EWG": [1, 0], "WEG": [1, 0],
"NWG": [0], "WSG": [0], "SEG": [0], "ENG": [0],
"NEG": [2], "WNG": [2], "SWG": [2], "ESG": [2]}
#min_phase_time = [30, 96, 74]
min_phase_time_7 = [10, 35]
# --- my modification ---
global_listLanes = collections.OrderedDict()
global_all_lanes_each_node = collections.OrderedDict()
entering_lanes = collections.OrderedDict()
coordinate_offset = collections.OrderedDict()
# --- my modification ---
def get_node_id_list():
# sumo may not has started
# return traci.trafficlight.getIDList()
# it's a little slow, so we need to only calculate it once
if not hasattr(get_node_id_list, 'node_id_list'):
get_node_id_list.node_id_list = []
file = ROAD_NET_FILE
with open(file) as json_data:
net = json.load(json_data)
for node_id in net['intersections']:
if not node_id['virtual']:
get_node_id_list.node_id_list.append(node_id['id'])
return get_node_id_list.node_id_list
def reset(para_set):
cityflow_config = {
"interval": 1,
"seed": 0,
"laneChange": False,
"dir": DIR,
"roadnetFile": ROADNETFILE,
"flowFile": TRAFFIC_FILE,
"rlTrafficLight": True,
"saveReplay": True,
"roadnetLogFile": "frontend/web/roadnetLogFile.json",
"replayLogFile": "frontend/web/replayLogFile.txt"
}
print("=========================")
print(cityflow_config)
print(os.getcwd())
print(os.path.join(para_set.DIR, "cityflow.config"))
# with open(os.path.join(para_set.DIR, "cityflow.config"), "w") as json_file:
with open(CITYFLOW_CONFIG_FILE, "w") as json_file:
json.dump(cityflow_config, json_file) # json.dumps将一个Python数据结构转换为JSON
eng = engine.Engine(CITYFLOW_CONFIG_FILE, thread_num=1)
return eng
def find_neighbors():
node_id_list = get_node_id_list()
if not hasattr(find_neighbors, 'neighbers'):
file = ROAD_NET_FILE
with open(file) as json_data:
net = json.load(json_data)
find_neighbors.neighbors = collections.OrderedDict()
edge_id_dict = collections.OrderedDict()
for road in net['roads']:
if road['id'] not in edge_id_dict.keys():
edge_id_dict[road['id']] = {}
edge_id_dict[road['id']]['to'] = road['endIntersection']
for node_id in node_id_list: # eg. i = intersection_1_1
find_neighbors.neighbors[node_id] = []
find_neighbors.neighbors[node_id] = [node_id_list.index(node_id)]
for j in range(4):
road_id = node_id.replace("intersection", "road") + "_" + str(j)
neighboring_node = edge_id_dict[road_id]['to']
# calculate the neighboring intersections
if neighboring_node in node_id_list:
find_neighbors.neighbors[node_id].append(node_id_list.index(neighboring_node))
else:
find_neighbors.neighbors[node_id].append(-1)
if len(find_neighbors.neighbors[node_id]) < 1 + NUM_OF_NEIGHBORS:
find_neighbors.neighbors[node_id].extend(
[-1 for _ in range(1 + NUM_OF_NEIGHBORS - len(find_neighbors.neighbors[node_id]))])
if len(find_neighbors.neighbors[node_id]) != 1 + NUM_OF_NEIGHBORS:
find_neighbors.neighbors[node_id] = find_neighbors.neighbors[node_id][0:1 + NUM_OF_NEIGHBORS]
return find_neighbors.neighbors
def get_node_phases(node_id):
# or: return traci.trafficlight.getCompleteRedYellowGreenDefinition()
if not hasattr(get_node_phases, 'phase_dict'):
get_node_phases.phase_dict = collections.OrderedDict()
if node_id not in get_node_phases.phase_dict:
tree = ET.parse(NET_FILE)
root = tree.getroot()
node = root.find("./tlLogic[@id='%s']" % node_id)
phase = node.findall("./phase")
get_node_phases.phase_dict[node_id] = [x.get('state') for x in node]
return get_node_phases.phase_dict[node_id]
# or traci.trafficlight.getCompleteRedYellowGreenDefinition(node_id)
def get_all_lanes_of_this_node(node_id):
file = ROAD_NET_FILE
with open(file) as json_data:
net = json.load(json_data)
for node in net['intersections']:
if node['id'] == node_id:
node_information = node
break
controlled_roads = node_information['roads']
how_much_lanes = {}
lanes = []
for road in net['roads']:
how_much_lanes[road['id']] = len(road['lanes'])
for road in controlled_roads:
for i in range(how_much_lanes[road]):
lane_id = road + '_' + str(i)
lanes.append(lane_id)
return lanes
def get_controlled_lanes(node_id):
# only used once
a = int(node_id[-3])
b = int(node_id[-1])
list_approachs = ["W", "E", "N", "S"]
lane_num = {'left': 1, 'right': 1, 'straight': 1}
dic_entering_approach_to_edge = {"W": "road_{0}_{1}_0".format(a - 1, b)}
dic_entering_approach_to_edge.update({"E": "road_{0}_{1}_2".format(a + 1, b)})
dic_entering_approach_to_edge.update({"S": "road_{0}_{1}_1".format(a, b - 1)})
dic_entering_approach_to_edge.update({"N": "road_{0}_{1}_3".format(a, b + 1)})
list_entering_lanes = []
for approach in list_approachs:
list_entering_lanes += [dic_entering_approach_to_edge[approach] + '_' + str(i) for i in
range(sum(list(lane_num.values())))]
return list_entering_lanes
# root = tree.getroot()
# edge = root.findall("./edge[@to='%s']" % node_id)
# return [y.get('id') for x in edge for y in x.findall("./lane")]
# now it is useless
def get_node_coordination(node_id):
if not hasattr(get_node_coordination, 'coordination_dict'):
get_node_coordination.coordination_dict = collections.OrderedDict()
if node_id not in get_node_coordination.coordination_dict:
tree = ET.parse(NET_FILE)
root = tree.getroot()
node = root.find("./junction[@id='%s'][@type='traffic_light']" % node_id)
offset = root.find("./location")
offset_x, offset_y = offset.get('netOffset').split(",")
offset_x, offset_y = float(offset_x), float(offset_y)
x, y = float(node.get('x')), float(node.get('y'))
get_node_coordination.coordination_dict[node_id] = (x-offset_x, y-offset_y)
return get_node_coordination.coordination_dict[node_id]
def get_incoming_node(node_id_list):
# only used once
incoming, outgoing, road_list = collections.OrderedDict(), collections.OrderedDict(), collections.OrderedDict()
tree = ET.parse(NET_FILE)
root = tree.getroot()
edge = root.findall("./edge")
for x in edge:
src = x.get('from')
dst = x.get('to')
road = x.get('id')
if '#' in road: # one segment
road = road[:road.index('#')] # only record its main name
if src != None and dst != None: # real edge
if src not in outgoing:
outgoing[src] = set()
outgoing[src].add(dst)
if dst not in incoming:
incoming[dst] = set()
incoming[dst].add(src)
if road not in road_list:
road_list[road] = []
if road.startswith('-'):
if dst in node_id_list:
road_list[road].append(dst)
else:
if src in node_id_list:
road_list[road].append(src)
if road.startswith('-'):
if src in node_id_list:
road_list[road].insert(0, src)
else:
if dst in node_id_list:
road_list[road].append(dst)
# if it's a segment, the list will be like: src, dst, dst2, dst3, ...
# segments will appear in order.
# skip non-node
for dst in node_id_list:
if incoming. __contains__(dst) == False:
incoming[dst] = set()
for src in node_id_list:
if outgoing. __contains__(src) == False:
outgoing[src] = set()
incoming_node = collections.OrderedDict()
for dst in node_id_list:
incoming_node[dst] = set()
for src in incoming[dst]: # count those who are nodes and 1-hop incoming
if src in node_id_list:
incoming_node[dst].add(src)
# special: if it's a multi-hop incoming, oon different segments of one road, via some non-nodes
for road, route in road_list.items():
for i in range(len(route)-1):
if route[i] not in incoming_node[route[i+1]]:
incoming_node[route[i+1]].add(route[i])
print('%s and %s add connection via road: %s' % (route[i], route[i+1], road))
# special: if it's a 2-hop incoming, via a non-node
for dst in node_id_list:
for src in node_id_list:
if src != dst and src not in incoming_node[dst]: # no connection
for via in outgoing[src].intersection(incoming[dst]):
if via not in node_id_list:
incoming_node[dst].add(src)
print('%s and %s add connection via non-node: %s' % (src, dst, via))
break
return incoming_node
'''
input: phase "NSG_SNG" , four lane number, in the key of W,E,S,N
output:
1.affected lane number: 4_0_0, 4_0_1, 3_0_0, 3_0_1
# 2.destination lane number, 0_3_0,0_3_1
'''
# --- my modification ---
# record all the vehicles that have left the network
all_vehicles_enter_time_dict = collections.OrderedDict()
lane_vehicle_arrive_leave_time_dict = collections.OrderedDict()
for node_id_1 in get_node_id_list():
lane_vehicle_arrive_leave_time_dict[node_id_1] = collections.OrderedDict()
# record all the vehicles that have left the each node area
def start_sumo(eng):
# traci.start(sumo_cmd_str)
# --- my modification ---
for node_id in get_node_id_list():
# TODO: use find_surrounding_lane_WESN & phase_affected_lane instead
temp = get_controlled_lanes(node_id)
temp1 = get_all_lanes_of_this_node(node_id)
# x, y = get_node_coordination(node_id)
global_listLanes[node_id] = temp
global_all_lanes_each_node[node_id] = temp1
entering_lanes[node_id] = temp
# coordinate_offset[node_id] = [x, y]
# --- my modification ---
random_phase = []
for node_id in get_node_id_list():
random_num = 1
random_phase.append(random_num)
eng.set_tl_phase(node_id, random_num)
eng.next_step()
# for i in range(20):
# eng.next_step()
return random_phase
def end_sumo(eng, episode, current_time, file_name_travel_time, episode_time):
# --- my modification ---
f_travel_time = open(file_name_travel_time, "a")
f_average_travel_time = open("travel_time.txt", "a")
print('%d vehicles have left the network.' % len(all_vehicles_enter_time_dict))
if all_vehicles_enter_time_dict:
average_travel_time = np.mean(list(all_vehicles_enter_time_dict.values()))
print('Their average travel time: %f' % np.mean(list(all_vehicles_enter_time_dict.values())))
else:
average_travel_time = '999999999999999999999'
print('Their average travel time: ', '999999999999999999999')
# average_travel_time_from_cityflow = eng.get_average_travel_time()
mmm = eng.get_average_travel_time()
print('average travel time from cithflow api is %d' % mmm)
memory_str = 'episode = %s\ttime = %d\t%d vehicles left\tepisode_time = %f' % (
episode, current_time, len(all_vehicles_enter_time_dict), episode_time)
travel_time_dict_from_lane = {}
memory_str_cityflow = 'average_travel_time_cityflow = %f' % mmm
f_travel_time.close()
f_average_travel_time.write(memory_str + "\n")
f_average_travel_time.write(memory_str_cityflow + "\n")
f_average_travel_time.close()
all_vehicles_enter_time_dict.clear()
travel_time_dict_from_lane.clear()
def end_sumo_test(eng, episode, current_time, file_name_travel_time, episode_time):
# --- my modification ---
f_travel_time = open(file_name_travel_time, "a")
f_average_travel_time = open("test_travel_time.txt", "a")
print('%d vehicles have left the network.' % len(all_vehicles_enter_time_dict))
if all_vehicles_enter_time_dict:
average_travel_time = np.mean(list(all_vehicles_enter_time_dict.values()))
print('Their average travel time: %f' % np.mean(list(all_vehicles_enter_time_dict.values())))
else:
average_travel_time = '999999999999999999999'
print('Their average travel time: ', '999999999999999999999')
mmm = eng.get_average_travel_time()
memory_str = 'episode = %s\ttime = %d\t%d vehicles left\tepisode_time = %f' % (
episode, current_time, len(all_vehicles_enter_time_dict), episode_time)
travel_time_dict_from_lane = {}
memory_str_cityflow = 'average_travel_time_cityflow = %f' % mmm
f_travel_time.close()
f_average_travel_time.write(memory_str + "\n")
f_average_travel_time.write(memory_str_cityflow + "\n")
f_average_travel_time.close()
all_vehicles_enter_time_dict.clear()
travel_time_dict_from_lane.clear()
def get_current_time(eng):
return eng.get_current_time()
def clear_local_travel_time():
all_vehicles_location_enter_time_dict = collections.OrderedDict()
all_vehicles_this_node_enter_time_dict = collections.OrderedDict()
for node_id_1 in get_node_id_list():
all_vehicles_location_enter_time_dict[node_id_1] = collections.OrderedDict()
all_vehicles_this_node_enter_time_dict[node_id_1] = collections.OrderedDict()
# it looks useless
def phase_affected_lane(phase="NSG_SNG",
four_lane_ids={'W': 'edge1-00', "E": "edge2-00", 'S': 'edge3-00', 'N': 'edge4-00'}):
directions = phase.split('_')
affected_lanes = []
for direction in directions:
for k, v in four_lane_ids.items():
if v.strip() != '' and direction.startswith(k):
for lane_no in direction_lane_dict[direction]:
affected_lanes.append("%s_%d" % (v, lane_no))
# affacted_lanes.append("%s_%d" % (v, 0))
if affected_lanes == []:
raise("Please check your phase and lane_number_dict in phase_affacted_lane()!")
return affected_lanes
'''
input: central nodeid "node0", surrounding nodes WESN: [1,2,3,4]
output: four_lane_ids={'W':'edge1-0',"E":"edge2-0",'S':'edge4-0','N':'edge3-0'})
--- my modification ---
output: four_lane_ids={'W':'edge1-0',"E":"edge2-0",'S':'edge3-0','N':'edge4-0'})
'''
# it looks useless
def find_surrounding_lane_WESN(central_node_id, WESN_node_ids={"W": "1", "E": "2", "S": "3", "N": "4"}):
tree = ET.parse('./data/one_run/cross.net.xml')
root = tree.getroot()
four_lane_ids_dict = collections.OrderedDict()
for k, v in WESN_node_ids.items():
four_lane_ids_dict[k] = root.find("./edge[@id='edge%s-%s']" % (v, central_node_id)).get('id')
return four_lane_ids_dict
'''
coordinate mapper
'''
# it looks useless
def coordinate_mapper(x1, y1, x2, y2):
x1 = int(x1 / grid_width)
y1 = int(y1 / grid_width)
x2 = int(x2 / grid_width)
y2 = int(y2 / grid_width)
x_max = x1 if x1 > x2 else x2
x_min = x1 if x1 < x2 else x2
y_max = y1 if y1 > y2 else y2
y_min = y1 if y1 < x2 else y2
length_num_grids = int(area_length / grid_width)
width_num_grids = int(area_width / grid_width)
return length_num_grids - y_max, length_num_grids - y_min, x_min, x_max
# it looks useless
def get_phase_affected_lane_traffic_max_volume(phase="NSG_SNG", tl_node_id="00",
WESN_node_ids={"W": "1", "E": "2", "S": "3", "N": "4"}):
four_lane_ids_dict = find_surrounding_lane_WESN(central_node_id=tl_node_id, WESN_node_ids=WESN_node_ids)
directions = phase.split('_')
traffic_volume_start_end = []
for direction in directions:
traffic_volume_start_end.append([four_lane_ids_dict[direction[0]],four_lane_ids_dict[direction[1]]])
tree = ET.parse('./data/one_run/cross.rou.xml')
root = tree.getroot()
phase_volumes = []
for lane_id in traffic_volume_start_end:
to_lane_id="edge%s-%s"%(lane_id[1].split('-')[1],lane_id[1].split('-')[0][4:])
time_begin = root.find("./flow[@from='%s'][@to='%s']" % (lane_id[0], to_lane_id)).get('begin')
time_end = root.find("./flow[@from='%s'][@to='%s']" % (lane_id[0], to_lane_id)).get('end')
volume = root.find("./flow[@from='%s'][@to='%s']" % (lane_id[0], to_lane_id)).get('number')
phase_volumes.append((float(time_end)-float(time_begin))/float(volume))
return max(phase_volumes)
# it looks useless
def phase_affected_lane_position(phase="NSG_SNG", tl_node_id="00",
WESN_node_ids={"W": "1", "E": "2", "S": "3", "N": "4"}):
'''
input: NSG_SNG ,central nodeid "node0", surrounding nodes WESN: {"W":"1", "E":"2", "S":"3", "N":"4"}
output: edge-ids, 4_0_0, 4_0_1, 3_0_0, 3_0_1
[[ 98, 100, 204, 301],[ 102, 104, 104, 198]]
'''
four_lane_ids_dict = find_surrounding_lane_WESN(central_node_id=tl_node_id, WESN_node_ids=WESN_node_ids)
affected_lanes = phase_affected_lane(phase=phase, four_lane_ids=four_lane_ids_dict)
tree = ET.parse('./data/one_run/cross.net.xml')
root = tree.getroot()
indexes = []
for lane_id in affected_lanes:
lane_shape = root.find("./edge[@to='node%s']/lane[@id='%s']" % (tl_node_id, lane_id)).get('shape')
lane_x1 = float(lane_shape.split(" ")[0].split(",")[0])
lane_y1 = float(lane_shape.split(" ")[0].split(",")[1])
lane_x2 = float(lane_shape.split(" ")[1].split(",")[0])
lane_y2 = float(lane_shape.split(" ")[1].split(",")[1])
ind_x1, ind_x2, ind_y1, ind_y2 = coordinate_mapper(lane_x1, lane_y1, lane_x2, lane_y2)
indexes.append([ind_x1, ind_x2 + 1, ind_y1, ind_y2 + 1])
return indexes
# it looks useless
def phases_affected_lane_positions(phases=["NSG_SNG_NWG_SEG", "NEG_SWG_NWG_SEG"], tl_node_id="00",
WESN_node_ids={"W": "1", "E": "2", "S": "3", "N": "4"}):
parameterArray = []
for phase in phases:
parameterArray += phase_affected_lane_position(phase=phase, tl_node_id=tl_node_id, WESN_node_ids=WESN_node_ids)
return parameterArray
def vehicle_location_mapper(coordinate):
transformX = math.floor(coordinate[0] / grid_width)
transformY = math.floor((area_length - coordinate[1]) / grid_width)
length_num_grids = int(area_length/grid_width)
transformY = length_num_grids-1 if transformY == length_num_grids else transformY
transformX = length_num_grids-1 if transformX == length_num_grids else transformX
tempTransformTuple = (transformY, transformX)
return tempTransformTuple
# it looks useless
def translateAction(action):
result = 0
for i in range(len(action)):
result += (i + 1) * action[i]
return result
# not global
def changeTrafficLight_7(eng, node_id,current_phase=0): # [WNG_ESG_WSG_ENG_NWG_SEG]
DIC_PHASE_MAP = {
1: 2,
2: 3,
3: 4,
4: 1
}
next_phase = DIC_PHASE_MAP[current_phase]
next_phase_time_eclipsed = 0
eng.set_tl_phase(node_id, next_phase)
return next_phase, next_phase_time_eclipsed
def get_phase_vector(controlSignal, current_phase):
# TODO: different lane number?
controlSignal2phase = {
# 2 phases:
"grrr gGGG grrr gGGG".replace(" ", ""): 'WNG_ESG_EWG_WEG_WSG_ENG',
"gGGG grrr gGGG grrr".replace(" ", ""): 'NSG_NEG_SNG_SWG_NWG_SEG',
# 4 phases: (NSG, NSLG, WEG, NSLG)
"gGGr grrr gGGr grrr".replace(" ", ""): 'NSG_SNG_NWG_SEG',
"grrG grrr grrG grrr".replace(" ", ""): 'NEG_SWG_NWG_SEG',
"grrr gGGr grrr gGGr".replace(" ", ""): 'WEG_EWG_WSG_ENG',
"grrr grrG grrr grrG".replace(" ", ""): 'WNG_ESG_WSG_ENG'
}
direction_list = ["NWG", "WSG", "SEG", "ENG", "NSG", "SNG", "EWG", "WEG", "NEG", "WNG", "SWG", "ESG"]
phase = controlSignal[current_phase] # index -> grrr...
phase = controlSignal2phase[phase].split("_") # grrr... -> WEG...
phase_vector = [0] * len(direction_list)
for direction in phase:
phase_vector[direction_list.index(direction)] = 1
return np.array(phase_vector)
# it looks useless; TODO:
# TODO: now we regard each tl same as node00: we should consider offset
# TODO: more patterns
def getMapOfCertainTrafficLight(current_phase=0, tl_node_id="00", area_length=600):
current_phases_light_7 = [phases_light_7[current_phase]]
parameterArray = phases_affected_lane_positions(phases=current_phases_light_7, tl_node_id=tl_node_id)
length_num_grids = int(area_length / grid_width)
resultTrained = np.zeros((length_num_grids, length_num_grids))
for affected_road in parameterArray:
resultTrained[affected_road[0]:affected_road[1], affected_road[2]:affected_road[3]] = 1 # 127 TODO:???
return resultTrained
# it looks useless
def calculate_reward(tempLastVehicleStateList):
waitedTime = 0
stop_count = 0
for key, vehicle_dict in tempLastVehicleStateList.items():
if tempLastVehicleStateList[key]['speed'] < 5:
waitedTime += 1
#waitedTime += (1 +math.pow(tempLastVehicleStateList[key]['waitedTime']/50,2))
if tempLastVehicleStateList[key]['former_speed'] > 0.5 and tempLastVehicleStateList[key]['speed'] < 0.5:
stop_count += (tempLastVehicleStateList[key]['stop_count']-tempLastVehicleStateList[key]['former_stop_count'])
#PI = (waitedTime + 10 * stop_count) / len(tempLastVehicleStateList) if len(tempLastVehicleStateList)!=0 else 0
PI = waitedTime/len(tempLastVehicleStateList) if len(tempLastVehicleStateList)!=0 else 0
return - PI
# TODO
MASK_IN_MAP = np.zeros((State.D_MAP_FEATURE[0], State.D_MAP_FEATURE[1]))
MASK_OUT_MAP = np.zeros((State.D_MAP_FEATURE[0], State.D_MAP_FEATURE[1]))
MASK_IN_MAP[0:75, 75-3:75] = 1
MASK_IN_MAP[75:150, 75:75+3] = 1
MASK_IN_MAP[75:75+3, 0:75] = 1
MASK_IN_MAP[75-3:75, 75:150] = 1
MASK_OUT_MAP[0:75, 75:75+3] = 1
MASK_OUT_MAP[75:150, 75-3:75] = 1
MASK_OUT_MAP[75-3:75, 0:75] = 1
MASK_OUT_MAP[75:75+3, 75:150] = 1
def getMapOfVehicles(node_id, vehicle_dict, cur_phase, next_phase):
'''
get the vehicle positions as NIPS paper
:param area_length:
:return: numpy narray
'''
# channel 0 & 1: position in & out
# channel 2 & 3: speed in & out
# channel 4 & 5: recount_waiting_time in & out
# channel 6: cur_phase in
# channel 7: next_phase in
length_num_grids = int(area_length / grid_width)
mapOfCars = np.zeros(State.D_MAP_FEATURE)
position_map = np.zeros((State.D_MAP_FEATURE[0], State.D_MAP_FEATURE[1]))
speed_map = np.zeros((State.D_MAP_FEATURE[0], State.D_MAP_FEATURE[1]))
wait_time_map = np.zeros((State.D_MAP_FEATURE[0], State.D_MAP_FEATURE[1]))
vehicle_id_list = traci.vehicle.getIDList()
for vehicle_id in vehicle_id_list:
x, y = traci.vehicle.getPosition(vehicle_id) # (double,double),tuple
transform_tuple = vehicle_location_mapper(
(x-coordinate_offset[node_id][0], y-coordinate_offset[node_id][1])) # call the function
if transform_tuple[0] in range(length_num_grids) and transform_tuple[1] in range(length_num_grids):
# position
position_map[transform_tuple[0], transform_tuple[1]] = 1
traci.vehicle.subscribe(vehicle_id, (tc.VAR_LANE_ID, tc.VAR_SPEED))
speed = traci.vehicle.getSubscriptionResults(vehicle_id).get(64) # VAR_SPEED = 0x40
speed_map[transform_tuple[0], transform_tuple[1]] = speed
if vehicle_id in vehicle_dict:
# recount_waiting_time
# wait_time_map[transform_tuple[0], transform_tuple[1]] = vehicle_dict[vehicle_id].recount_waiting_time
# delta_recount_waiting_time
if speed < 0.1:
wait_time_map[transform_tuple[0], transform_tuple[1]] = 1
mapOfCars[:,:,0] = np.multiply(position_map, MASK_IN_MAP)
mapOfCars[:,:,1] = np.multiply(position_map, MASK_OUT_MAP)
mapOfCars[:,:,2] = np.multiply(speed_map, MASK_IN_MAP)
mapOfCars[:,:,3] = np.multiply(speed_map, MASK_OUT_MAP)
mapOfCars[:,:,4] = np.multiply(wait_time_map, MASK_IN_MAP)
mapOfCars[:,:,5] = np.multiply(wait_time_map, MASK_OUT_MAP)
mapOfCars[:,:,6] = getMapOfCertainTrafficLight(cur_phase, tl_node_id="00")
mapOfCars[:,:,7] = getMapOfCertainTrafficLight(next_phase, tl_node_id="00")
if K.image_data_format() == 'channels_first':
mapOfCars = np.transpose(mapOfCars, (2, 0, 1))
return mapOfCars
def restrict_reward(reward,func="unstrict"):
if func == "linear":
bound = -50
reward = 0 if reward < bound else (reward/(-bound) + 1)
elif func == "neg_log":
reward = math.log(-reward+1)
else:
pass
return reward
# not global
def log_rewards(eng, vehicle_dict, action, rewards_info_dict, file_name, true_reward, timestamp, rewards_detail_dict_list, node_id, reward_indicator, warm_up, global_dic_waiting_time_vehicles):
reward, reward_detail_dict = get_rewards_from_sumo(eng, vehicle_dict, action, rewards_info_dict, node_id, reward_indicator, warm_up, global_dic_waiting_time_vehicles)
list_reward_keys = np.sort(list(reward_detail_dict.keys()))
reward_str = "{0}, {1}, {2}".format(node_id, timestamp, action)
for reward_key in true_reward:
reward_str = reward_str + ", {0}".format(reward_detail_dict[reward_key][2])
reward_str += '\n'
fp = open(file_name, "a")
fp.write(reward_str)
fp.close()
rewards_detail_dict_list.append(reward_detail_dict)
# not global
def log_rewards_control(rewards_info_dict, rewards_this_node_info_dict, file_name, timestamp, rewards_detail_dict_list, rewards_detail_this_node_dict_list, node_id, neighbor, all_vehicles_location_enter_time_dict, all_vehicles_this_node_enter_time_dict):
reward_main, reward_aux, reward_detail_dict, reward_this_node_detail_dict = get_control_rewards(rewards_info_dict, rewards_this_node_info_dict, node_id, neighbor, all_vehicles_location_enter_time_dict, all_vehicles_this_node_enter_time_dict)
list_reward_keys_aux = np.sort(list(reward_detail_dict.keys()))
reward_str_aux = "{0}, {1}".format(node_id, timestamp)
for reward_key in list_reward_keys_aux:
reward_str_aux = reward_str_aux + ", {0}".format(reward_detail_dict[reward_key][2])
reward_str_aux += '\n'
list_reward_keys_main = np.sort(list(reward_this_node_detail_dict.keys()))
reward_str_main = "{0}, {1}".format(node_id, timestamp)
for reward_key in list_reward_keys_main:
reward_str_main = reward_str_main + ", {0}".format(reward_this_node_detail_dict[reward_key][2])
reward_str_main += '\n\n'
fp = open(file_name, "a")
fp.write(reward_str_aux)
fp.write(reward_str_main)
fp.close()
rewards_detail_dict_list.append(reward_detail_dict)
rewards_detail_this_node_dict_list.append(reward_this_node_detail_dict)
# not global
def get_rewards_from_sumo(eng, vehicle_dict, action, rewards_info_dict, node_id, reward_indicator, warm_up, global_dic_waiting_time_vehicles):
reward = 0
import copy
reward_detail_dict = copy.deepcopy(rewards_info_dict)
vehicle_id_entering_list = get_vehicle_id_entering(eng, node_id)
if warm_up:
node_index = get_node_id_list().index(node_id)
if reward_indicator[node_index] == 0:
reward_detail_dict['queue_length'].append(get_overall_queue_length(eng, global_listLanes[node_id]))
reward_detail_dict['wait_time'].append(0)
reward_detail_dict['delay'].append(0)
elif reward_indicator[node_index] == 1:
reward_detail_dict['queue_length'].append(0)
reward_detail_dict['wait_time'].append(get_overall_waiting_time(eng, global_listLanes[node_id], global_dic_waiting_time_vehicles))
reward_detail_dict['delay'].append(0)
elif reward_indicator[node_index] == 2:
reward_detail_dict['queue_length'].append(0)
reward_detail_dict['wait_time'].append(0)
reward_detail_dict['delay'].append(get_overall_delay(eng, global_listLanes[node_id]))
else:
if reward_indicator[node_id] == 0:
reward_detail_dict['queue_length'].append(get_overall_queue_length(eng, global_listLanes[node_id]))
reward_detail_dict['wait_time'].append(0)
reward_detail_dict['delay'].append(0)
elif reward_indicator[node_id] == 1:
reward_detail_dict['queue_length'].append(0)
reward_detail_dict['wait_time'].append(get_overall_waiting_time(eng, global_listLanes[node_id], global_dic_waiting_time_vehicles))
reward_detail_dict['delay'].append(0)
elif reward_indicator[node_id] == 2:
reward_detail_dict['queue_length'].append(0)
reward_detail_dict['wait_time'].append(0)
reward_detail_dict['delay'].append(get_overall_delay(eng, global_listLanes[node_id]))
reward_detail_dict['emergency'].append(0)
reward_detail_dict['duration'].append(0)
reward_detail_dict['flickering'].append(get_flickering(action))
reward_detail_dict['partial_duration'].append(0)
reward_detail_dict['num_of_vehicles_left'].append(0)
reward_detail_dict['duration_of_vehicles_left'].append(0)
for k, v in reward_detail_dict.items():
if v[0]: # True or False
reward += v[1]*v[2]
reward = restrict_reward(reward)#,func="linear")
return reward, reward_detail_dict
def get_control_rewards(rewards_info_dict, rewards_this_node_info_dict, node_id, neighbor, all_vehicles_location_enter_time_dict, all_vehicles_this_node_enter_time_dict):
reward = 0
import copy
travel_time_list = []
reward_detail_dict = copy.deepcopy(rewards_info_dict) # main: this node
reward_this_node_detail_dict = copy.deepcopy(rewards_this_node_info_dict) # aux: local
if len(list(all_vehicles_location_enter_time_dict[node_id].values())) == 0:
local_travel_time_this_node = 0
else:
local_travel_time_this_node = np.sum(list(all_vehicles_location_enter_time_dict[node_id].values()))
reward_this_node_detail_dict['local_travel_time'].append(local_travel_time_this_node)
reward_this_node_detail_dict['average_local_travel_time'].append(0)
if len(list(all_vehicles_this_node_enter_time_dict[node_id].values())) == 0:
travel_time_this_node = 0
else:
travel_time_this_node = np.sum(list(all_vehicles_this_node_enter_time_dict[node_id].values()))
reward_detail_dict['this_node_travel_time'].append(travel_time_this_node)
for k, v in reward_detail_dict.items():
if v[0]: # True or False
reward += v[1]*v[2]
reward_main = restrict_reward(reward)#,func="linear")
for k, v in reward_this_node_detail_dict.items():
if v[0]: # True or False
reward += v[1]*v[2]
reward_aux = restrict_reward(reward)#,func="linear")
return reward_main, reward_aux, reward_detail_dict, reward_this_node_detail_dict
# not global
def get_rewards_from_dict_list(rewards_detail_dict_list):
reward = 0
for i in range(len(rewards_detail_dict_list)):
for k, v in rewards_detail_dict_list[i].items():
if v[0]: # True or False
reward += v[1] * v[2]
reward = restrict_reward(reward)
return reward
def get_overall_queue_length(eng, listLanes):
overall_queue_length = 0
global_vehicles = eng.get_lane_waiting_vehicle_count()
for lane in listLanes:
overall_queue_length += global_vehicles[lane]
return overall_queue_length
def get_vehicle_speed(listVehicles):
list_vehicle_speed = []
for vehicle_id in listVehicles:
list_vehicle_speed.append(traci.vehicle.getSpeed(vehicle_id))
def get_overall_waiting_time(eng, listLanes, global_dic_waiting_time_vehicles):
overall_waiting_time = 0
for lane in listLanes:
for vehicle in global_dic_waiting_time_vehicles[lane]:
overall_waiting_time += global_dic_waiting_time_vehicles[lane][vehicle] / 60.0
return overall_waiting_time
def get_overall_recount_waiting_time(node_id, listLanes, vehicle_dict):
overall_recount_waiting_time = 0
for lane in listLanes:
vehicle_id_lane_list = traci.lane.getLastStepVehicleIDs(lane)
for vehicle_id, vehicle in vehicle_dict.items():
if vehicle_id in vehicle_id_lane_list:
overall_recount_waiting_time += vehicle.recount_waiting_time
return overall_recount_waiting_time / 60
def get_overall_delta_waiting_time(node_id, listLanes, vehicle_dict):
overall_delta_waiting_time = 0
for lane in listLanes:
vehicle_id_lane_list = traci.lane.getLastStepVehicleIDs(lane)
for vehicle_id in vehicle_id_lane_list:
traci.vehicle.subscribe(vehicle_id, (tc.VAR_LANE_ID, tc.VAR_SPEED))
speed = traci.vehicle.getSubscriptionResults(vehicle_id).get(64) # VAR_SPEED = 0x40
if speed < 0.1:
overall_delta_waiting_time += 1
return overall_delta_waiting_time
def get_overall_delay(eng, listLanes):
overall_delay = 0
global_vehicle_speed = eng.get_vehicle_speed()
global_lane_vehicle = eng.get_lane_vehicles()
for lane in listLanes:
vehicle_of_this_lane = global_lane_vehicle[lane]
speed_of_this_lane = [global_vehicle_speed[vehicle] for vehicle in vehicle_of_this_lane]
if len(speed_of_this_lane) != 0:
mean_speed = sum(speed_of_this_lane) / max(len(speed_of_this_lane), 1)
max_speed = max(speed_of_this_lane)
overall_delay += 1 - mean_speed / max(max_speed, 1)
return overall_delay
def get_flickering(action):
return action
# calculate number of emergency stops by vehicle
def get_num_of_emergency_stops(vehicle_dict):
emergency_stops = 0
vehicle_id_list = traci.vehicle.getIDList()
for vehicle_id in vehicle_id_list:
traci.vehicle.subscribe(vehicle_id, (tc.VAR_LANE_ID, tc.VAR_SPEED))
current_speed = traci.vehicle.getSubscriptionResults(vehicle_id).get(64) # VAR_SPEED = 0x40
if (vehicle_id in vehicle_dict.keys()):
vehicle_former_state = vehicle_dict[vehicle_id]
if current_speed - vehicle_former_state.speed < -4.5:
emergency_stops += 1
else:
# print("##New car coming")
if current_speed - Vehicles.initial_speed < -4.5:
emergency_stops += 1
if len(vehicle_dict) > 0:
return emergency_stops/len(vehicle_dict)
else:
return 0
def get_partial_travel_time_duration(eng, vehicle_dict, vehicle_id_list):
travel_time_duration = 0
for vehicle_id in vehicle_id_list:
if (vehicle_id in vehicle_dict.keys()) and (vehicle_dict[vehicle_id].first_stop_time != -1):
travel_time_duration += (eng.get_current_time() - vehicle_dict[vehicle_id].first_stop_time) / 60.0
if len(vehicle_id_list) > 0:
return travel_time_duration # /len(vehicle_id_list)
else:
return 0
def get_travel_time_duration(eng, vehicle_dict, vehicle_id_list):
travel_time_duration = 0
for vehicle_id in vehicle_id_list:
if (vehicle_id in vehicle_dict.keys()):
travel_time_duration += (eng.get_current_time() - vehicle_dict[vehicle_id].enter_time) / 60.0
if len(vehicle_id_list) > 0:
return travel_time_duration # /len(vehicle_id_list)
else:
return 0
### added! Colight's calculating method of average travel time
def update_dic_lane_vehicle_arrive_leave_time(eng, global_list_node_lane_vehicle):
vehicles_each_lane_new = eng.get_lane_vehicles() # new
for node_id_1 in get_node_id_list():
for lane in global_all_lanes_each_node[node_id_1]:
s_old = set(global_list_node_lane_vehicle[lane])
s_new = set(vehicles_each_lane_new[lane])
if lane in global_listLanes[node_id_1]:
for vehicle_id in s_old - s_new: # it has left the lane
lane_vehicle_arrive_leave_time_dict[node_id_1][vehicle_id]["leave_time"] = eng.get_current_time()
lane_vehicle_arrive_leave_time_dict[node_id_1][vehicle_id]["run_time"] = lane_vehicle_arrive_leave_time_dict[node_id_1][vehicle_id]["leave_time"] - lane_vehicle_arrive_leave_time_dict[node_id_1][vehicle_id]["arrive_time"]
for vehicle_id in s_new - s_old:
lane_vehicle_arrive_leave_time_dict[node_id_1][vehicle_id] = {}
lane_vehicle_arrive_leave_time_dict[node_id_1][vehicle_id]["arrive_time"] = eng.get_current_time()
global_list_node_lane_vehicle = vehicles_each_lane_new
return global_list_node_lane_vehicle
def update_vehicles_state(eng, global_dic_vehicles):
global_dic_vehicle_set = set(global_dic_vehicles.keys()) # old list
# list_lane_new_vehicles = eng.get_lane_vehicles()
# global_speed = eng. get_vehicle_speed()
# list_new_vehicles = list(global_speed.keys())
# [list_new_vehicles.extend(vehicle_id) for vehicle_id in list_lane_new_vehicles.values()]
list_new_vehicles = eng.get_vehicles(include_waiting=True)
global_vehicle_id_set = set(list_new_vehicles) # new list
# all_new_list = traci.vehicle.getIDList()
for vehicle_id in global_dic_vehicle_set - global_vehicle_id_set: # it has left the whole network
all_vehicles_enter_time_dict[vehicle_id] = eng.get_current_time() - global_dic_vehicles[vehicle_id]
del (global_dic_vehicles[vehicle_id]) # old -= (old-new): old = old intersect new
for vehicle_id in global_vehicle_id_set - global_dic_vehicle_set:
global_dic_vehicles[vehicle_id] = eng.get_current_time()
return global_dic_vehicles
def find_neighbor_lanes():
node_id_list = get_node_id_list()
if not hasattr(find_neighbor_lanes, 'neighbor_lanes_list'):
find_neighbor_lanes.neighbor_lanes_list = collections.OrderedDict()
for node_id_1 in node_id_list:
find_neighbor_lanes.neighbor_lanes_list[node_id_1] = []
neighbor_this_node = find_neighbors()[node_id_1]
for node_id_index in neighbor_this_node:
if node_id_index != -1:
node_id_2 = node_id_list[node_id_index]
listLanes = global_listLanes[node_id_2]
for lane in listLanes:
find_neighbor_lanes.neighbor_lanes_list[node_id_1].append(lane)
return find_neighbor_lanes.neighbor_lanes_list
def update_vehicles_location(eng, global_dic_location_vehicles, global_dic_this_node_vehicles, all_vehicles_location_enter_time_dict, all_vehicles_this_node_enter_time_dict, global_dic_waiting_time_vehicles): # old_all
global_dic_vehicle_location_set = collections.OrderedDict() # old
global_vehicle_id_location_set = collections.OrderedDict() # new
global_current_location_vehicles = collections.OrderedDict() # new_all
global_dic_vehicle_this_node_set = collections.OrderedDict() # old
global_vehicle_id_this_node_set = collections.OrderedDict() # new
global_current_this_node_vehicles = collections.OrderedDict() # new_all
global_vehicles_speed = eng.get_vehicle_speed()
# global_still_vehicles = {k: v for k, v in global_vehicles_speed.items() if v <= 0.1}
vehicle_id_of_each_lane = collections.OrderedDict()
m1 = eng.get_lane_vehicles()
for node_id_1 in get_node_id_list():
global_current_location_vehicles[node_id_1] = []
global_current_this_node_vehicles[node_id_1] = []
for node_id_1, listLanes in global_listLanes.items():
for lane in listLanes:
vehicle_id_of_each_lane[lane] = m1[lane]
each_lane_vehicleID = m1[lane]
for vehicleID in each_lane_vehicleID:
global_current_this_node_vehicles[node_id_1].append(vehicleID)
neighbor_lanes = find_neighbor_lanes()
for node_id_1, neighbor_lane in neighbor_lanes.items():
for lane in neighbor_lane:
each_lane_vehicleID = m1[lane]
for vehicleID in each_lane_vehicleID:
global_current_location_vehicles[node_id_1].append(vehicleID)
for node_id_1 in get_node_id_list():
global_dic_vehicle_location_set[node_id_1] = set(global_dic_location_vehicles[node_id_1].keys()) # old list
# 此时刻node_id_1控制的lanes上的车辆id
global_vehicle_id_location_set[node_id_1] = set(global_current_location_vehicles[node_id_1]) # new list
global_dic_vehicle_this_node_set[node_id_1] = set(global_dic_this_node_vehicles[node_id_1].keys())
global_vehicle_id_this_node_set[node_id_1] = set(global_current_this_node_vehicles[node_id_1])
for vehicle_id in global_dic_vehicle_location_set[node_id_1] - global_vehicle_id_location_set[node_id_1]: # it has left the whole network
all_vehicles_location_enter_time_dict[node_id_1][vehicle_id] = eng.get_current_time() - \
global_dic_location_vehicles[node_id_1][
vehicle_id]
del (global_dic_location_vehicles[node_id_1][vehicle_id]) # old -= (old-new): old = old intersect new
for vehicle_id in global_vehicle_id_location_set[node_id_1] - global_dic_vehicle_location_set[node_id_1]:
global_dic_location_vehicles[node_id_1][vehicle_id] = eng.get_current_time()
for vehicle_id in global_dic_vehicle_this_node_set[node_id_1] - global_vehicle_id_this_node_set[node_id_1]: # it has left the whole network
all_vehicles_this_node_enter_time_dict[node_id_1][vehicle_id] = eng.get_current_time() - \
global_dic_this_node_vehicles[node_id_1][
vehicle_id]
del (global_dic_this_node_vehicles[node_id_1][vehicle_id]) # old -= (old-new): old = old intersect new
for vehicle_id in global_vehicle_id_this_node_set[node_id_1] - global_dic_vehicle_this_node_set[node_id_1]:
global_dic_this_node_vehicles[node_id_1][vehicle_id] = eng.get_current_time()
for lane in vehicle_id_of_each_lane.keys():
global_wait_time_old = set(global_dic_waiting_time_vehicles[lane].keys()) # old list
# 此时刻node_id_1控制的lanes上的车辆id
global_wait_time_new = set(vehicle_id_of_each_lane[lane]) # new list
for vehicle_id in global_wait_time_old - global_wait_time_new: # it has left the whole network
del (global_dic_waiting_time_vehicles[lane][vehicle_id]) # old -= (old-new): old = old intersect new
for vehicle_id in global_wait_time_new - global_wait_time_old:
if global_vehicles_speed[vehicle_id] <= 0.1:
global_dic_waiting_time_vehicles[lane][vehicle_id] = 1
intersection = [i for i in global_wait_time_new if i in global_wait_time_old]
for vehicle_id in intersection:
if global_vehicles_speed[vehicle_id] <= 0.1:
global_dic_waiting_time_vehicles[lane][vehicle_id] += 1
else:
del (global_dic_waiting_time_vehicles[lane][vehicle_id])
return global_dic_location_vehicles, global_dic_this_node_vehicles, all_vehicles_location_enter_time_dict, all_vehicles_this_node_enter_time_dict, global_dic_waiting_time_vehicles
# --- my modification ---
# add params
def status_calculator(eng, global_vehicle_id_list):
status = collections.OrderedDict()