def main():
# read inputs
argParser = initOptions()
options = argParser.parse_args()
r_all = {}
rv_dict = {}
memory_problems = [0]
# start traci
if options.sumocfg:
run_traci = [
options.sumo, "-c", options.sumocfg,
'--tripinfo-output.write-unfinished'
]
else:
run_traci = [
options.sumo, '--net-file',
'%s' % options.network, '-r',
'%s,%s' % (options.reservations, options.taxis), '-l', 'log.txt',
'--device.taxi.dispatch-algorithm', 'traci', '--tripinfo-output',
'%s' % options.output, '--tripinfo-output.write-unfinished'
]
if options.gui_settings:
run_traci.extend(['-g', '%s' % options.gui_settings])
traci.start(run_traci)
# execute the TraCI control loop
step = traci.simulation.getTime() + 10
v_type = None
rerouting = True
while rerouting:
traci.simulationStep(step)
# get vType for route calculation
if not v_type:
fleet = traci.vehicle.getTaxiFleet(-1)
v_type = traci.vehicle.getTypeID(fleet[0])
# get new reservations
r_id_new = []
for x in traci.person.getTaxiReservations(1):
# search direct travel time
direct = int(
traci.simulation.findRoute(x.fromEdge,
x.toEdge,
v_type,
x.depart,
routingMode=0).travelTime)
# add new reservation attributes
setattr(x, 'direct', direct) # direct travel time
setattr(x, 'vehicle', False) # id of assigned vehicle
setattr(x, 'delay',
0) # difference between assigned pick up time and real one
setattr(
x, 'tw_pickup',
[x.depart, x.depart + options.max_wait]) # pickup time window
# drop off time window
if x.direct * options.drf < options.drf_min:
setattr(x, 'tw_dropoff', [
x.tw_pickup[0] + direct,
x.tw_pickup[1] + direct + options.drf_min
])
else:
setattr(x, 'tw_dropoff', [
x.tw_pickup[0] + direct,
x.tw_pickup[1] + direct * options.drf
])
# add id to new reservations
r_id_new.append(x.id)
# add reservation to list
r_all[x.id] = x
# unassigned reservations
r_id_unassigned = [x.id for x_id, x in r_all.items() if not x.vehicle]
# if reservations pending
if r_id_unassigned:
if options.debug:
print('\nRun dispatcher')
if r_id_new:
print('New reservations: ', r_id_new)
print('Unassigned reservations: ',
list(set(r_id_unassigned) - set(r_id_new)))
# get fleet
fleet = traci.vehicle.getTaxiFleet(-1)
if set(fleet) != set(fleet) & set(
traci.vehicle.getIDList()): # TODO manage teleports
print("\nVehicle %s is being teleported, skip to next step" %
(set(fleet) - set(traci.vehicle.getIDList())))
step += options.sim_step
continue # if a vehicle is being teleported skip to next step
# remove reservations already served
r_id_current = [x.id for x in traci.person.getTaxiReservations(0)]
r_id_served = list(set(r_all.keys()) - set(r_id_current))
[r_all.pop(key) for key in r_id_served]
[
rv_dict.pop(key) for key in list(rv_dict)
if set(r_id_served) & set(rv_dict[key][2])
]
# reservations already picked up
r_id_picked = [x.id for x in traci.person.getTaxiReservations(8)]
# search request-vehicles pairs
if options.debug:
print('Calculate RV-Graph')
get_rv(options, r_id_new, r_id_picked, r_id_served, r_all, fleet,
v_type, rv_dict, step)
# search trips (rtv graph)
# TODO define/import algorithm before to avoid checking each time
# Maybe a list with the function as element and get the element (0, 1, 2)
if options.debug:
print('Calculate RTV-Graph')
if options.rtv_algorithm == '0':
rtv_dict, r_id_rtv, memory_problems = rtvAlgorithm.exhaustive_search(
options, r_id_unassigned, r_id_picked, r_all, fleet,
v_type, rv_dict, step, memory_problems)
elif options.rtv_algorithm == '1':
rtv_dict, r_id_rtv = rtvAlgorithm.simple(
options, r_id_unassigned, r_id_picked, r_id_served, r_all,
fleet, v_type, rv_dict, step)
elif options.rtv_algorithm == '2':
rtv_dict, r_id_rtv = rtvAlgorithm.simple_rerouting(
options, r_id_unassigned, r_id_picked, r_id_served, r_all,
fleet, v_type, rv_dict, step)
# rtv_dict, r_id_rtv = get_rtv(options, r_id_unassigned, r_id_picked, r_id_served, r_all, fleet, v_type, rv_dict, step)
if len(rtv_dict) == 0:
step += options.sim_step
continue # if no routes found
elif len(rtv_dict.keys()) == 1:
# if one vehicle darp, assign route
best_routes = list(rtv_dict.keys())
else:
# if multiple vehicle darp, solve ILP with pulp
vehicle_constraints = {}
request_constraints = {}
costs = {}
trips = list(rtv_dict.keys()
) # list of all trips for ILP solution parse
# add bonus_cost to trip cost (makes trips with more served requests cheaper than splitting the requests to more
# vehicles with smaller trips if both strategies would yield a similar cost)
for idx, trip_id in enumerate(trips):
# rtv_dict[route] = [travel_time, v_bin, r_bin, value]
bonus_cost = (
sum(rtv_dict[trip_id][2]) + 1
) * options.cost_per_trip # TODO specific cost for vehicle can be consider here
costs.update({idx: rtv_dict[trip_id][0] + bonus_cost
}) # generate dict with cost
vehicle_constraints.update({
idx: rtv_dict[trip_id][1]
}) # generate dict with vehicle used in the trip
request_constraints.update({
idx: rtv_dict[trip_id][2]
}) # generate dict with served requests in the trip
if options.debug:
print('Solve ILP')
ilp_result = ilp_solve(options, len(fleet), len(r_id_rtv),
costs, vehicle_constraints,
request_constraints)
# parse ILP result
best_routes = [
trips[int(route_index)] for route_index in ilp_result
]
# assign routes to vehicles
for route_id in best_routes:
stops = route_id.replace('y', '')
stops = stops.replace('z', '')
stops = stops.split("_")
# first check if route different or better (when no optimal solution) than already assigned
current_route = []
try: # get current route
for taxi_stop in traci.vehicle.getStops(stops[0]):
sub_stops = taxi_stop.actType.split(
",") # if more than 1 reservation in stop
for sub_stop in sub_stops:
current_route.append(sub_stop.split(" ")[2][1:-1])
except:
current_route = ['None']
if current_route == stops[1:]:
# route is the same
continue
elif set(current_route) == set(
stops[1:]) and len(current_route) == len(stops[1:]):
# if route serve same request, check if new is faster
tt_current_route = 0
edges = [
taxi_stops.lane.split("_")[0]
for taxi_stops in traci.vehicle.getStops(stops[0])
] # TODO check next_act update
edges.insert(
0,
traci.vehicle.getLaneID(
stops[0]).split("_")[0]) # add current edge
for idx, edge in enumerate(edges[:-1]):
tt_current_route += int(
traci.simulation.findRoute(
edge,
edges[idx + 1],
v_type,
step,
routingMode=0).travelTime
) + 60 # TODO default stop time ticket #6714
tt_new_route = rtv_dict[route_id][0]
if tt_new_route >= tt_current_route:
continue # current route better than new found
if options.debug:
print('Dispatch: ', route_id)
traci.vehicle.dispatchTaxi(stops[0], stops[1:])
# assign vehicle to requests
# TODO to avoid major changes in the pick-up time when assigning new passengers,
# tw_pickup should be updated, whit some constant X seconds, e.g. 10 Minutes
for x_id in set(stops[1:]):
x = r_all[x_id]
x.vehicle = stops[0]
if step > options.end_time or (
not traci.simulation.getMinExpectedNumber() > 0
and not traci.person.getIDList()): #TODO ticket #8385
rerouting = False
step += options.sim_step
if options.rtv_algorithm == 0: # if exhaustive search
if sum(memory_problems) == 0:
print('Optimal solution found')
else:
print(
'The maximum specified time for the calculation with the exact method was exceeded. Solution could not be optimal'
)
print('DRT simulation ended')
traci.close()
def main():
# read inputs
ap = initOptions()
options = ap.parse_args()
res_all = {}
rv_dict = {}
exact_sol = [0]
if options.sumo == 'sumo':
SUMO = checkBinary('sumo')
else:
SUMO = checkBinary('sumo-gui')
# start traci
if options.sumocfg:
run_traci = [
SUMO, "-c", options.sumocfg, '--tripinfo-output.write-unfinished'
]
else:
run_traci = [
SUMO, '--net-file',
'%s' % options.network, '-r',
'%s,%s' % (options.reservations, options.taxis), '-l', 'log.txt',
'--device.taxi.dispatch-algorithm', 'traci', '--tripinfo-output',
'%s' % options.output, '--tripinfo-output.write-unfinished'
]
if options.gui_settings:
run_traci.extend(['-g', '%s' % options.gui_settings])
traci.start(run_traci)
# execute the TraCI control loop
step = traci.simulation.getTime() + 10
veh_type = None
rerouting = True
while rerouting:
traci.simulationStep(step)
# get vType for route calculation
if not veh_type:
fleet = traci.vehicle.getTaxiFleet(-1)
veh_type = traci.vehicle.getTypeID(fleet[0])
# get new reservations
res_id_new = []
for res in traci.person.getTaxiReservations(1):
# search direct travel time
direct = int(
findRoute(res.fromEdge,
res.toEdge,
veh_type,
res.depart,
routingMode=0).travelTime)
# add new reservation attributes
setattr(res, 'direct', direct) # direct travel time
setattr(res, 'vehicle', False) # id of assigned vehicle
setattr(res, 'delay', 0) # real pick up time - assigned time
# pickup time window
setattr(res, 'tw_pickup',
[res.depart, res.depart + options.max_wait])
# drop off time window
if res.direct * options.drf < options.drf_min:
setattr(res, 'tw_dropoff', [
res.tw_pickup[0] + direct,
res.tw_pickup[1] + direct + options.drf_min
])
else:
setattr(res, 'tw_dropoff', [
res.tw_pickup[0] + direct,
res.tw_pickup[1] + direct * options.drf
])
# add reservation id to new reservations
res_id_new.append(res.id)
# add reservation object to list
res_all[res.id] = res
# unassigned reservations
res_id_unassigned = [
res.id for res_id, res in res_all.items() if not res.vehicle
]
# if reservations pending
if res_id_unassigned:
if options.debug:
print('\nRun dispatcher')
if res_id_new:
print('New reservations: ', res_id_new)
print('Unassigned reservations: ',
list(set(res_id_unassigned) - set(res_id_new)))
# get fleet
fleet = traci.vehicle.getTaxiFleet(-1)
if set(fleet) != set(fleet) & set(
traci.vehicle.getIDList()): # TODO manage teleports # noqa
print("\nVehicle %s is being teleported, skip to next step" %
(set(fleet) - set(traci.vehicle.getIDList())))
step += options.sim_step
continue # if a vehicle is being teleported skip to next step
# remove reservations already served
res_id_current = [
res.id for res in traci.person.getTaxiReservations(0)
] # noqa
res_id_served = list(set(res_all.keys()) - set(res_id_current))
[res_all.pop(key) for key in res_id_served]
[
rv_dict.pop(key) for key in list(rv_dict)
if set(res_id_served) & set(rv_dict[key][2])
]
# reservations already picked up
res_id_picked = [
res.id for res in traci.person.getTaxiReservations(8)
] # noqa
# search reservation-vehicles pairs
if options.debug:
print('Calculate RV-Graph')
get_rv(options, res_id_new, res_id_picked, res_id_served, res_all,
fleet, veh_type, rv_dict, step)
# search trips (rtv graph)
if options.debug:
print('Calculate RTV-Graph')
if options.rtv_algorithm == '0':
rtv_dict, rtv_res, exact_sol = rtvAlgorithm.exhaustive_search(
options, res_id_unassigned, res_id_picked, res_all, fleet,
veh_type, rv_dict, step, exact_sol)
elif options.rtv_algorithm == '1':
rtv_dict, rtv_res = rtvAlgorithm.simple(
options, res_id_unassigned, res_id_picked, res_id_served,
res_all, fleet, veh_type, rv_dict, step)
elif options.rtv_algorithm == '2':
rtv_dict, rtv_res = rtvAlgorithm.simple_rerouting(
options, res_id_unassigned, res_id_picked, res_id_served,
res_all, fleet, veh_type, rv_dict, step)
if len(rtv_dict) == 0:
step += options.sim_step
continue # if no routes found
elif len(rtv_dict.keys()) == 1:
# if one vehicle darp, assign route
best_routes = list(rtv_dict.keys())
else:
# if multiple vehicle darp, solve ILP with pulp
veh_constraints = {}
res_constraints = {}
costs = {}
trips = list(rtv_dict.keys()) # trips for parsing ILP solution
# add bonus_cost to trip cost (makes trips with more served
# reservations cheaper than splitting the reservations to more
# vehicles with smaller trips if both strategies would yield
# a similar cost)
for idx, trip_id in enumerate(trips):
# rtv_dict[route] = [travel_time, veh_bin, res_bin, value]
# TODO specific cost for vehicle can be consider here
bonus_cost = (sum(rtv_dict[trip_id][2]) + 1) * \
options.cost_per_trip
# generate dict with costs
costs.update({idx: rtv_dict[trip_id][0] + bonus_cost})
# generate dict with vehicle used in the trip
veh_constraints.update({idx: rtv_dict[trip_id][1]})
# generate dict with served reservations in the trip
res_constraints.update({idx: rtv_dict[trip_id][2]})
if options.debug:
print('Solve ILP')
ilp_result = ilp_solve(options, len(fleet), len(rtv_res),
costs, veh_constraints, res_constraints)
# parse ILP result
best_routes = [
trips[int(route_index)] for route_index in ilp_result
] # noqa
# assign routes to vehicles
for route_id in best_routes:
stops = route_id.replace('y', '')
stops = stops.replace('z', '')
stops = stops.split("_")
veh_id = stops[0]
# first check if new route is better than the current one
current_route = []
if len(traci.vehicle.getStops(veh_id)) > 1:
for taxi_stop in traci.vehicle.getStops(veh_id):
# get reservations served at each stop
sub_stops = taxi_stop.actType.split(",")
# if more than 1 reservation in stop
for sub_stop in sub_stops:
current_route.append(sub_stop.split(" ")[2][1:-1])
if current_route == stops[1:]:
# route is the same
continue
elif (set(current_route) == set(stops[1:])
and len(current_route) == len(stops[1:])):
# if route serve same reservations and have the same stops
# get travel time of current route
tt_current_route = 0
edges = [
taxi_stop.lane.split("_")[0]
for taxi_stop in traci.vehicle.getStops(veh_id)
]
# add current edge to list
edges.insert(
0,
traci.vehicle.getLaneID(veh_id).split("_")[0]) # noqa
# calculate travel time
for idx, edge in enumerate(edges[:-1]):
# TODO default stop time ticket #6714
tt_current_route += int(
findRoute(edge,
edges[idx + 1],
veh_type,
step,
routingMode=0).travelTime) + 60
# get travel time of the new route
tt_new_route = rtv_dict[route_id][0]
if tt_new_route >= tt_current_route:
continue # current route better than new found
if options.debug:
print('Dispatch: ', route_id)
traci.vehicle.dispatchTaxi(veh_id, stops[1:])
# assign vehicle to reservations
# TODO to avoid major changes in the pick-up time when assigning new passengers, # noqa
# tw_pickup should be updated, whit some constant X seconds, e.g. 10 Minutes # noqa
for res_id in set(stops[1:]):
res = res_all[res_id]
res.vehicle = veh_id
# TODO ticket #8385
if step > options.end_time or (traci.simulation.getMinExpectedNumber()
<= 0 and not traci.person.getIDList()):
rerouting = False
step += options.sim_step
if options.rtv_algorithm == 0: # if exhaustive search
if sum(exact_sol) == 0:
print('Optimal solution found.')
else:
print(
'The maximal specified calculation time has been exceeded. Solution could be not optimal.'
) # noqa
print('DRT simulation ended')
traci.close()
