def isRouteValidForTraCI(mtraci, route):
"""
Returns true if the given route is linked by SUMO lanes, false else
"""
for i in range(0, len(route) - 1):
inEdge = route[i]
outEdge = route[i + 1]
inLaneIndex = 0
inLane = getFirstLaneFromEdge(inEdge)
found = False
while not found:
try:
mtraci.acquire()
lanesOut = traci.lane.getLinks(inLane)
mtraci.release()
except:
mtraci.release()
return False
laneOutIndex = 0
while laneOutIndex < len(lanesOut) and not found:
outLane = lanesOut[laneOutIndex][0]
if getEdgeFromLane(outLane) == outEdge:
found = True
else:
laneOutIndex += 1
if not found:
inLaneIndex += 1
inLane = inLane[:len(inLane) - 1] + str(inLaneIndex)
return True
def updateTllForPriorityVehicles(mtraci, priorityVehicles, mPriorityVehicles,
tllDict, yellowTllDict, managedTllDict):
"""
Determines for each priority vehicle which are the next traffic lights and the distance to these ones.
Regarding to the vehicle current speed and the time elapsed during a SUMO simulation step,
the traffic light hidden lane the vehicle will cross is set to green or the junction to a temporary orange state
if the vehicle is close enough and if the concurrent access between priority vehicles allows it.
Note: the following algorithm could be highly improved by exploring every edges the vehicle will use
at the beginning, but then only explore the edges which are now in the vehicle scope because of
the last step progression. Two list, one for the orange look up and one for the green, would be used
for this purpose.
Note 2: The main complexity of this algorithm resides in the lack of TraCI functionalities in order to get
retrieve the state of a hidden lane linking two lanes. This could also be highly improved by using a
dictionary built when starting the software.
"""
mPriorityVehicles.acquire()
managedTlls = []
length = len(priorityVehicles)
# Checking if the next traffic light has to be changed for each priority vehicleId in the simulation
for prioVehIndex in range(0, length):
vehicleId = priorityVehicles[prioVehIndex]
# Getting route and position information for the current priority vehicleId
mtraci.acquire()
route = traci.vehicle.getRoute(vehicleId)
currentLane = traci.vehicle.getLaneID(vehicleId)
mtraci.release()
if currentLane != '' and not isJunction(currentLane):
# Getting speed information for space and time predictions
mtraci.acquire()
lanePosition = traci.vehicle.getLanePosition(vehicleId)
laneLength = traci.lane.getLength(currentLane)
mtraci.release()
currentEdge = getEdgeFromLane(currentLane)
currentEdgeIndex = route.index(currentEdge)
remainingLength = laneLength - lanePosition
edge = currentEdge
lane = currentLane
edgeIndex = currentEdgeIndex
# Browsing the next edges the vehicleId will go to
while remainingLength <= constants.YELLOW_LENGTH_ANTICIPATION and edgeIndex < len(
route) - 1:
# If the current edge (the vehicleId is not) ends with a traffic light
if edge in tllDict:
# If the car is close enough for the traffic light to become green
if remainingLength <= constants.GREEN_LENGTH_ANTICIPATION:
setState = constants.SET_GREEN
# If the car is close enough for the traffic light to prepare (temporary state) becoming green
elif not tllDict[edge] in yellowTllDict:
setState = constants.SET_YELLOW
else:
managedTlls.append(tllDict[edge])
setState = constants.IGNORE
# Calculating the next lane the vehicleId will go to
outEdge = route[edgeIndex + 1]
outLane = getOutLane(mtraci, lane, outEdge)
# Calling for a traffic light change
if outLane != -1 and setState != constants.IGNORE:
tllId = tllDict[edge]
managedTlls.append(tllId)
if (tllId in managedTllDict
and managedTllDict[tllId][1] > remainingLength
) or not tllId in managedTllDict:
managedTllDict[tllId] = (vehicleId,
remainingLength)
changeState(mtraci, tllId, lane, outLane, setState,
yellowTllDict)
edgeIndex += 1
if edgeIndex < len(route):
edge = route[edgeIndex]
lane = getFirstLaneFromEdge(edge)
mtraci.acquire()
laneLength = traci.lane.getLength(lane)
mtraci.release()
remainingLength += laneLength
# Removing the tlls which have been crossed from the managedTllDict
for key in managedTllDict.keys():
if managedTllDict[key][
0] == vehicleId and not key in managedTlls:
del managedTllDict[key]
managedTlls[:] = []
mPriorityVehicles.release()
def updateTllForPriorityVehicles(mtraci, priorityVehicles, mPriorityVehicles, tllDict, yellowTllDict, managedTllDict):
"""
Determines for each priority vehicle which are the next traffic lights and the distance to these ones.
Regarding to the vehicle current speed and the time elapsed during a SUMO simulation step,
the traffic light hidden lane the vehicle will cross is set to green or the junction to a temporary orange state
if the vehicle is close enough and if the concurrent access between priority vehicles allows it.
Note: the following algorithm could be highly improved by exploring every edges the vehicle will use
at the beginning, but then only explore the edges which are now in the vehicle scope because of
the last step progression. Two list, one for the orange look up and one for the green, would be used
for this purpose.
Note 2: The main complexity of this algorithm resides in the lack of TraCI functionalities in order to get
retrieve the state of a hidden lane linking two lanes. This could also be highly improved by using a
dictionary built when starting the software.
"""
mPriorityVehicles.acquire()
managedTlls = [];
length = len(priorityVehicles)
# Checking if the next traffic light has to be changed for each priority vehicleId in the simulation
for prioVehIndex in range(0, length):
vehicleId = priorityVehicles[prioVehIndex]
# Getting route and position information for the current priority vehicleId
mtraci.acquire()
route = traci.vehicle.getRoute(vehicleId)
currentLane = traci.vehicle.getLaneID(vehicleId)
mtraci.release()
if currentLane != '' and not isJunction(currentLane):
# Getting speed information for space and time predictions
mtraci.acquire()
lanePosition = traci.vehicle.getLanePosition(vehicleId)
laneLength = traci.lane.getLength(currentLane)
mtraci.release()
currentEdge = getEdgeFromLane(currentLane)
currentEdgeIndex = route.index(currentEdge)
remainingLength = laneLength - lanePosition
edge = currentEdge
lane = currentLane
edgeIndex = currentEdgeIndex
# Browsing the next edges the vehicleId will go to
while remainingLength <= constants.YELLOW_LENGTH_ANTICIPATION and edgeIndex < len(route) - 1:
# If the current edge (the vehicleId is not) ends with a traffic light
if edge in tllDict:
# If the car is close enough for the traffic light to become green
if remainingLength <= constants.GREEN_LENGTH_ANTICIPATION:
setState = constants.SET_GREEN
# If the car is close enough for the traffic light to prepare (temporary state) becoming green
elif not tllDict[edge] in yellowTllDict:
setState = constants.SET_YELLOW
else:
managedTlls.append(tllDict[edge])
setState = constants.IGNORE
# Calculating the next lane the vehicleId will go to
outEdge = route[edgeIndex + 1]
outLane = getOutLane(mtraci, lane, outEdge)
# Calling for a traffic light change
if outLane != -1 and setState != constants.IGNORE:
tllId = tllDict[edge]
managedTlls.append(tllId)
if (tllId in managedTllDict and managedTllDict[tllId][1] > remainingLength) or not tllId in managedTllDict:
managedTllDict[tllId] = (vehicleId, remainingLength)
changeState(mtraci, tllId, lane, outLane, setState, yellowTllDict)
edgeIndex += 1
if edgeIndex < len(route):
edge = route[edgeIndex]
lane = getFirstLaneFromEdge(edge)
mtraci.acquire()
laneLength = traci.lane.getLength(lane)
mtraci.release()
remainingLength += laneLength
# Removing the tlls which have been crossed from the managedTllDict
for key in managedTllDict.keys():
if managedTllDict[key][0] == vehicleId and not key in managedTlls:
del managedTllDict[key]
managedTlls[:] = []
mPriorityVehicles.release()