def __init__(self, _id, module, learner=None): #define variaveis da class self.id = _id self.horizontal_edge = lane.getEdgeID(trafficlights.getControlledLanes(self.id)[0]) self.vertical_edge = lane.getEdgeID(trafficlights.getControlledLanes(str(_id))[2]) #define variaveis da classe pai self.horizontalLoad = [] self.verticalLoad = [] self.averageHorizontal = [] self.averageVertical = [] self.nextAction = None self.expectedReward = None self.tolerance = None LearningAgent.__init__(self, module, learner)
def getCumulativeDelay(tls_id):
# Get each vehicle in lanearea
delay = 0
for lane_id in tl.getControlledLanes(tls_id):
det_id = la_mapping[lane_id]
for vid in la.getLastStepVehicleIDs(det_id):
delay += veh.getAccumulatedWaitingTime(vid)
return delay
def getState(tls_id):
EW = 0
N = 0
for lane_id in tl.getControlledLanes(tls_id):
det_id = la_mapping[lane_id]
if det_id[-1] in ["E", "W"]:
EW += la.getLastStepHaltingNumber(det_id)
else: # in ["N", "S"]
N += la.getLastStepHaltingNumber(det_id)
phase = tl.getPhase(tls_id) % 2 # only in this case, for four phases
db('State:', EW, N)
sm = stateMap(EW, N, phase)
visits.append((EW, N))
return sm
def __init__(self, tls_id):
self.tls_id = tls_id
self.controlled_lanes = trafficlights.getControlledLanes(tls_id)
self.directions = ['North', 'East', 'South', 'West']
# Each edge(one direction) has 5 lanes,
# two lanes as edge_0, another three as edge_1
# edge_0_0(lane index:0) is trun-right lane
# edge_1_1(lane index:3) is turn-left lane
# edge_1_2(lane index:4) is turn-around lane
# edge_0_1, edge_1_0 is straight lane
self.N_edge = Edge(self.controlled_lanes[1], self.controlled_lanes[3]) # North edge_0, edge_1
self.E_edge = Edge(self.controlled_lanes[6], self.controlled_lanes[8])
self.S_edge = Edge(self.controlled_lanes[11], self.controlled_lanes[13])
self.W_edge = Edge(self.controlled_lanes[16], self.controlled_lanes[18])
self.edges = dict(zip(self.directions,
[self.N_edge, self.E_edge, self.S_edge, self.W_edge]))
self.tls_status = dict()
def __init__(self, collect_data):
self.ID = tratl.getIDList()[0]
self.controlledLanes = tratl.getControlledLanes(self.ID)
self.timeInPhase = 0
self.comfortAcceleration = 4
self.df = pd.DataFrame()
self.vehicleCount = 0
self.collect_data = collect_data
self.phases = tratl.getCompleteRedYellowGreenDefinition(self.ID)[
0]._phases
self.phase = 0
self.greenCheck = re.compile("[gG]")
self.redCheck = re.compile("[r]")
self.ICData = {lane: [] for lane in self.controlledLanes}
self.timeTillGreen = {lane: None for lane in self.controlledLanes}
self.timeTillNextGreen = {lane: None for lane in self.controlledLanes}
self.timeTillRed = {lane: None for lane in self.controlledLanes}
self.Tl = {l: 0 for l in self.controlledLanes}
self.light_change = traci.simulation.getCurrentTime()
def getDTSE(tls_id):
# Get all vehicles in the lanearea
# Given a list of relevant vehicles, convert it t
# Array of intersection state maps
DTSE_pos = np.zeros((NUM_LANES * 4, num_cells))
DTSE_spd = np.zeros((NUM_LANES * 4, num_cells))
for l, lane_id in enumerate(tl.getControlledLanes(tls_id)):
det_id = la_mapping[lane_id]
det_len = la.getLength(det_id)
lane_len = lane.getLength(la.getLaneID(det_id))
assert det_len == LANEAREA_LENGTH
for vid in la.getLastStepVehicleIDs(det_id):
# What is the correct encoding of pos to cell? Should low indices specify cars close or far away for traffic light?
#print('lid',veh.getLanePosition(vid))
pos = LANEAREA_LENGTH - lane_len + veh.getLanePosition(vid)
if pos < 0:
continue
cell = int(
min(pos // c, num_cells)
) # in case pos ends up being exactly the length of the detector
speed = veh.getSpeed(vid)
#print("Veh {}: {}".format(vid, pos))
#print('cell',cell)
# Record binary position and speed arrays
DTSE_pos[l][cell] = 1
DTSE_spd[l][cell] = speed
phase = int(tl.getPhase(tls_id)) % 2
signal_state = torch.zeros(1, A).float()
signal_state[0][phase] = 1
# Add batch dimension and input filter dimensions to state vectors
DTSE_pos = torch.from_numpy(DTSE_pos).unsqueeze(0).unsqueeze(0).float()
DTSE_spd = torch.from_numpy(DTSE_spd).unsqueeze(0).unsqueeze(0).float()
return DTSE_pos, DTSE_spd, signal_state
tl_list=[]
for connection in connection_list:
tl_list.append( connection[2])
return tl_list
#lust= import_datasets()
#tls= extract_tl_ids(lust.iloc[0])
#print tls
#connections = dataset[5]==[from,to,tl,dir,state]
if True:
TLSID= "0"
while step < 1000:
simulationStep()
arrived_vehicles_in_last_step= simulation.getArrivedNumber()
departed_vehicles_in_last_step= simulation.getDepartedNumber()
current_simulation_time_ms= simulation.getCurrentTime()
print arrived_vehicles_in_last_step
phase= trafficlights.getPhase(TLSID)
trafficlights.setRedYellowGreenState(TLSID, "grrrrrrrrrrr")
lanes= trafficlights.getControlledLanes(TLSID)
print len(lanes)
#for lane in lanes:
# print lane
print phase
#if traci.inductionloop.getLastStepVehicleNumber("0") > 0:
# traci.trafficlights.setRedYellowGreenState("0", "GrGr")
step += 1
close()
for connection in connection_list:
tl_list.append(connection[2])
return tl_list
#lust= import_datasets()
#tls= extract_tl_ids(lust.iloc[0])
#print tls
#connections = dataset[5]==[from,to,tl,dir,state]
if True:
TLSID = "0"
while step < 1000:
simulationStep()
arrived_vehicles_in_last_step = simulation.getArrivedNumber()
departed_vehicles_in_last_step = simulation.getDepartedNumber()
current_simulation_time_ms = simulation.getCurrentTime()
print arrived_vehicles_in_last_step
phase = trafficlights.getPhase(TLSID)
trafficlights.setRedYellowGreenState(TLSID, "grrrrrrrrrrr")
lanes = trafficlights.getControlledLanes(TLSID)
print len(lanes)
#for lane in lanes:
# print lane
print phase
#if traci.inductionloop.getLastStepVehicleNumber("0") > 0:
# traci.trafficlights.setRedYellowGreenState("0", "GrGr")
step += 1
close()
def cumHaltingNumber(tls_id):
cum_halt_num = 0
for lane_id in tl.getControlledLanes(tls_id):
det_id = la_mapping[lane_id]
cum_halt_num += la.getLastStepHaltingNumber(det_id)
return cum_halt_num