Simulation with traffic generation.#
This demo is for the case of extrapolating the maximum effect caused by a regional traffic flow.
The bridge:
Has four 3.5m-width lanes.
Has a length of 20m.
Has a width of 16m.
Has one load effect being considered.
The traffic flow:
Vehicles are generated from Garage model.
Headways are in freeflow condition (Poisson arrival model).
The part that has already been introduced in the single_vehicle demo.
But this time we change to define a 4-lane bridge.
[1]:
import pybtls as pb
from pathlib import Path
# set the load effect by using a 2D influence surface
lanes_position = [(0.5, 4.0), (4.0, 7.5), (8.5, 12.0), (12.0, 15.5)]
IS_matrix = [
[0.0, 0.0, 4.0, 8.0, 12.0, 16.0],
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
[10.0, 0.0, 2.5, 5.0, 2.5, 0.0],
[20.0, 0.0, 0.0, 0.0, 0.0, 0.0],
]
load_effect = pb.InfluenceSurface()
load_effect.set_IS(IS_matrix, lanes_position)
# set the bridge
bridge = pb.Bridge(length=20.0, no_lane=4)
bridge.add_load_effect(inf_line_surf=load_effect, threshold=0.0)
bridge.add_load_effect(inf_line_surf=load_effect, threshold=1000.0) # We add the same load effect again, but this time with a threshold of 1000.0 for the later POT analysis
Set lane flow compositions (here we just use the same flow for all lanes).
For the lane_dir, 1 means from left to right and 2 means from right to left, based on the same coordinate system as the influence surface.
[2]:
lfc_lane1 = pb.LaneFlowComposition(lane_index=1, lane_dir=1)
lfc_lane1.assign_lane_data(
hourly_truck_flow=[100] * 24,
hourly_car_flow=[0] * 24,
hourly_speed_mean=[80 / 3.6 * 10] * 24,
hourly_speed_std=[10.0] * 24,
)
lfc_lane2 = pb.LaneFlowComposition(lane_index=2, lane_dir=1)
lfc_lane2.assign_lane_data(
hourly_truck_flow=[100] * 24,
hourly_car_flow=[0] * 24,
hourly_speed_mean=[80 / 3.6 * 10] * 24,
hourly_speed_std=[10.0] * 24,
)
lfc_lane3 = pb.LaneFlowComposition(lane_index=3, lane_dir=2)
lfc_lane3.assign_lane_data(
hourly_truck_flow=[100] * 24,
hourly_car_flow=[0] * 24,
hourly_speed_mean=[80 / 3.6 * 10] * 24,
hourly_speed_std=[10.0] * 24,
)
lfc_lane4 = pb.LaneFlowComposition(lane_index=4, lane_dir=2)
lfc_lane4.assign_lane_data(
hourly_truck_flow=[100] * 24,
hourly_car_flow=[0] * 24,
hourly_speed_mean=[80 / 3.6 * 10] * 24,
hourly_speed_std=[10.0] * 24,
)
Set vehicle generator. Here we use the garage one.
[3]:
# The kernel is to ensure variation between the generated vehicles and garages.
kernel = [[1.0, 0.08], [1.0, 0.05], [1.0, 0.02]]
vehicle_gen = pb.VehicleGenGarage(
garage=Path(".") / "garage.txt", kernel=kernel, garage_format=4
)
Choose headway generator with the freeflow one.
[4]:
# set headway generator
headway_gen = pb.HeadwayGenFreeflow()
Assemble the vehicle generator and the headway generator with the lfc information to get a traffic generator.
[5]:
traffic_gen = pb.TrafficGenerator(no_lane=4)
traffic_gen.add_lane(vehicle_gen=vehicle_gen, headway_gen=headway_gen, lfc=lfc_lane1)
traffic_gen.add_lane(vehicle_gen=vehicle_gen, headway_gen=headway_gen, lfc=lfc_lane2)
traffic_gen.add_lane(vehicle_gen=vehicle_gen, headway_gen=headway_gen, lfc=lfc_lane3)
traffic_gen.add_lane(vehicle_gen=vehicle_gen, headway_gen=headway_gen, lfc=lfc_lane4)
# traffic_gen.set_start_time(0.0) # optional, the default start_time is 0.0 unless you want to change it.
Set output, which will be written to HDD.
To do the extrapolation, we need the block-max and peak-over-threshold output.
[6]:
output_config = pb.OutputConfig()
output_config.set_BM_output(
write_vehicle=True, write_summary=True, write_mixed=True
)
output_config.set_POT_output(
write_vehicle=True, write_summary=True, write_counter=True
)
Set the simulation and run.
[7]:
sim_task = pb.Simulation(Path(".") / "temp")
sim_task.add_sim(
bridge=bridge,
traffic=traffic_gen,
no_day=250, # 1-year working days
output_config=output_config,
time_step=0.1,
min_gvw=35, # Ignore vehicles with GVW less than 35 kN.
# active_lane=[1,2,3,4], # optional, if not set, all lanes will be active.
# track_progress=False, # optional, if True, the progress print will show up.
tag="Case2",
)
# run simulation
sim_task.run(no_core=1)
Check what are the outputs.
[8]:
sim_output = sim_task.get_output()
print(sim_output.keys())
dict_keys(['Case2'])
Use two simple loops to display all the read-in outputs.
The data are stored as pandas.DataFrame.
[9]:
from IPython.display import display
case2_output = sim_output["Case2"]
for i in case2_output.get_summary():
print(i)
for j in case2_output.read_data(i):
print(j)
display(case2_output.read_data(i)[j])
BM_by_no_trucks
BM_V_20_2
| Index | Effect | Value | Time | Position on Bridge | No. Trucks | Trucks | |
|---|---|---|---|---|---|---|---|
| 0 | 1 | 1 | 851.5 | 86009.7 | 28.40 | 2 | [<pybtls.lib.BTLS.Vehicle object at 0x127ed317... |
| 1 | 1 | 2 | 851.5 | 86009.7 | 28.40 | 2 | [<pybtls.lib.BTLS.Vehicle object at 0x127ed323... |
| 2 | 2 | 1 | 912.2 | 141409.0 | 26.90 | 2 | [<pybtls.lib.BTLS.Vehicle object at 0x127ed3cb... |
| 3 | 2 | 2 | 912.2 | 141409.0 | 26.90 | 2 | [<pybtls.lib.BTLS.Vehicle object at 0x127ed347... |
| 4 | 3 | 1 | 728.1 | 210254.1 | -2.57 | 2 | [<pybtls.lib.BTLS.Vehicle object at 0x127ed3dd... |
| ... | ... | ... | ... | ... | ... | ... | ... |
| 495 | 248 | 2 | 848.5 | 21373964.1 | 21.78 | 2 | [<pybtls.lib.BTLS.Vehicle object at 0x14010f1d... |
| 496 | 249 | 1 | 509.6 | 21489013.1 | 14.24 | 2 | [<pybtls.lib.BTLS.Vehicle object at 0x14010f29... |
| 497 | 249 | 2 | 509.6 | 21489013.1 | 14.24 | 2 | [<pybtls.lib.BTLS.Vehicle object at 0x14010f35... |
| 498 | 250 | 1 | 716.2 | 21575517.0 | 15.06 | 2 | [<pybtls.lib.BTLS.Vehicle object at 0x14010f41... |
| 499 | 250 | 2 | 716.2 | 21575517.0 | 15.06 | 2 | [<pybtls.lib.BTLS.Vehicle object at 0x14010f4d... |
500 rows × 7 columns
BM_V_20_1
| Index | Effect | Value | Time | Position on Bridge | No. Trucks | Trucks | |
|---|---|---|---|---|---|---|---|
| 0 | 1 | 1 | 1152.8 | 72178.8 | 22.32 | 1 | [<pybtls.lib.BTLS.Vehicle object at 0x127edb8f0>] |
| 1 | 1 | 2 | 1152.8 | 72178.8 | 22.32 | 1 | [<pybtls.lib.BTLS.Vehicle object at 0x127edb950>] |
| 2 | 2 | 1 | 1152.6 | 89180.0 | 21.65 | 1 | [<pybtls.lib.BTLS.Vehicle object at 0x127edb830>] |
| 3 | 2 | 2 | 1152.6 | 89180.0 | 21.65 | 1 | [<pybtls.lib.BTLS.Vehicle object at 0x127edaf90>] |
| 4 | 3 | 1 | 1142.6 | 175921.2 | 22.02 | 1 | [<pybtls.lib.BTLS.Vehicle object at 0x127edae10>] |
| ... | ... | ... | ... | ... | ... | ... | ... |
| 495 | 248 | 2 | 1122.3 | 21400190.1 | -1.61 | 1 | [<pybtls.lib.BTLS.Vehicle object at 0x140164d70>] |
| 496 | 249 | 1 | 1116.6 | 21508482.4 | 22.69 | 1 | [<pybtls.lib.BTLS.Vehicle object at 0x140164dd0>] |
| 497 | 249 | 2 | 1116.6 | 21508482.4 | 22.69 | 1 | [<pybtls.lib.BTLS.Vehicle object at 0x140164e30>] |
| 498 | 250 | 1 | 1148.9 | 21522860.1 | 22.02 | 1 | [<pybtls.lib.BTLS.Vehicle object at 0x140164e90>] |
| 499 | 250 | 2 | 1148.9 | 21522860.1 | 22.02 | 1 | [<pybtls.lib.BTLS.Vehicle object at 0x140164ef0>] |
500 rows × 7 columns
BM_V_20_3
| Index | Effect | Value | Time | Position on Bridge | No. Trucks | Trucks | |
|---|---|---|---|---|---|---|---|
| 0 | 2 | 1 | 577.1 | 119150.6 | 21.56 | 3 | [<pybtls.lib.BTLS.Vehicle object at 0x14012d13... |
| 1 | 2 | 2 | 577.1 | 119150.6 | 21.56 | 3 | [<pybtls.lib.BTLS.Vehicle object at 0x14012dbb... |
| 2 | 3 | 1 | 34.5 | 176750.8 | -2.60 | 3 | [<pybtls.lib.BTLS.Vehicle object at 0x14012dd3... |
| 3 | 3 | 2 | 34.5 | 176750.8 | -2.60 | 3 | [<pybtls.lib.BTLS.Vehicle object at 0x14012dfd... |
| 4 | 4 | 1 | 433.8 | 277810.9 | 33.93 | 3 | [<pybtls.lib.BTLS.Vehicle object at 0x14012e0f... |
| ... | ... | ... | ... | ... | ... | ... | ... |
| 71 | 208 | 2 | 243.7 | 17908150.0 | -16.38 | 3 | [<pybtls.lib.BTLS.Vehicle object at 0x14011f17... |
| 72 | 239 | 1 | 385.2 | 20602573.4 | 6.38 | 3 | [<pybtls.lib.BTLS.Vehicle object at 0x14011f29... |
| 73 | 239 | 2 | 385.2 | 20602573.4 | 6.38 | 3 | [<pybtls.lib.BTLS.Vehicle object at 0x14011f3b... |
| 74 | 241 | 1 | 755.5 | 20769591.6 | -13.34 | 3 | [<pybtls.lib.BTLS.Vehicle object at 0x14011f4d... |
| 75 | 241 | 2 | 755.5 | 20769591.6 | -13.34 | 3 | [<pybtls.lib.BTLS.Vehicle object at 0x14011f5f... |
76 rows × 7 columns
BM_by_mixed
BM_V_20_All
| Index | Effect | Value | Time | Position on Bridge | No. Trucks | Trucks | |
|---|---|---|---|---|---|---|---|
| 0 | 1 | 1 | 1152.8 | 72178.8 | 22.32 | 1 | [<pybtls.lib.BTLS.Vehicle object at 0x127ed9fd0>] |
| 1 | 1 | 2 | 1152.8 | 72178.8 | 22.32 | 1 | [<pybtls.lib.BTLS.Vehicle object at 0x127ed9df0>] |
| 2 | 2 | 1 | 1152.6 | 89180.0 | 21.65 | 1 | [<pybtls.lib.BTLS.Vehicle object at 0x127ed9f10>] |
| 3 | 2 | 2 | 1152.6 | 89180.0 | 21.65 | 1 | [<pybtls.lib.BTLS.Vehicle object at 0x127ed94f0>] |
| 4 | 3 | 1 | 1142.6 | 175921.2 | 22.02 | 1 | [<pybtls.lib.BTLS.Vehicle object at 0x127ed9370>] |
| ... | ... | ... | ... | ... | ... | ... | ... |
| 495 | 248 | 2 | 1122.3 | 21400190.1 | -1.61 | 1 | [<pybtls.lib.BTLS.Vehicle object at 0x140155790>] |
| 496 | 249 | 1 | 1116.6 | 21508482.4 | 22.69 | 1 | [<pybtls.lib.BTLS.Vehicle object at 0x1401558b0>] |
| 497 | 249 | 2 | 1116.6 | 21508482.4 | 22.69 | 1 | [<pybtls.lib.BTLS.Vehicle object at 0x140155730>] |
| 498 | 250 | 1 | 1148.9 | 21522860.1 | 22.02 | 1 | [<pybtls.lib.BTLS.Vehicle object at 0x140155850>] |
| 499 | 250 | 2 | 1148.9 | 21522860.1 | 22.02 | 1 | [<pybtls.lib.BTLS.Vehicle object at 0x1401556d0>] |
500 rows × 7 columns
BM_summary
BM_S_20_Eff_2
| Block Index | 1-Truck Event | 2-Truck Event | 3-Truck Event | |
|---|---|---|---|---|
| 0 | 1 | 1152.8 | 851.5 | NaN |
| 1 | 2 | 1152.6 | 912.2 | 577.1 |
| 2 | 3 | 1142.6 | 728.1 | 34.5 |
| 3 | 4 | 1114.8 | 1108.0 | 433.8 |
| 4 | 5 | 1131.7 | 1162.3 | 278.3 |
| ... | ... | ... | ... | ... |
| 245 | 246 | 1144.9 | 659.9 | NaN |
| 246 | 247 | 1083.9 | 511.5 | NaN |
| 247 | 248 | 1122.3 | 848.5 | NaN |
| 248 | 249 | 1116.6 | 509.6 | NaN |
| 249 | 250 | 1148.9 | 716.2 | NaN |
250 rows × 4 columns
BM_S_20_Eff_1
| Block Index | 1-Truck Event | 2-Truck Event | 3-Truck Event | |
|---|---|---|---|---|
| 0 | 1 | 1152.8 | 851.5 | NaN |
| 1 | 2 | 1152.6 | 912.2 | 577.1 |
| 2 | 3 | 1142.6 | 728.1 | 34.5 |
| 3 | 4 | 1114.8 | 1108.0 | 433.8 |
| 4 | 5 | 1131.7 | 1162.3 | 278.3 |
| ... | ... | ... | ... | ... |
| 245 | 246 | 1144.9 | 659.9 | NaN |
| 246 | 247 | 1083.9 | 511.5 | NaN |
| 247 | 248 | 1122.3 | 848.5 | NaN |
| 248 | 249 | 1116.6 | 509.6 | NaN |
| 249 | 250 | 1148.9 | 716.2 | NaN |
250 rows × 4 columns
POT_vehicle
PT_V_20_1
| Index | Effect | Value | Time | Position on Bridge | No. Trucks | Trucks | |
|---|---|---|---|---|---|---|---|
| 0 | 1 | 1 | 174.2 | 77.8 | 13.98 | 1 | [<pybtls.lib.BTLS.Vehicle object at 0x29942ebd0>] |
| 1 | 1 | 2 | 174.2 | 77.8 | 13.98 | 1 | [<pybtls.lib.BTLS.Vehicle object at 0x29942ec30>] |
| 2 | 2 | 1 | 151.2 | 122.2 | 4.75 | 1 | [<pybtls.lib.BTLS.Vehicle object at 0x29942ec90>] |
| 3 | 2 | 2 | 151.2 | 122.2 | 4.75 | 1 | [<pybtls.lib.BTLS.Vehicle object at 0x29942f590>] |
| 4 | 3 | 1 | 277.0 | 160.6 | 13.93 | 1 | [<pybtls.lib.BTLS.Vehicle object at 0x29942f710>] |
| ... | ... | ... | ... | ... | ... | ... | ... |
| 745315 | 2658 | 2 | 557.9 | 21599860.3 | 22.89 | 1 | [<pybtls.lib.BTLS.Vehicle object at 0x2c1c50710>] |
| 745316 | 2659 | 1 | 204.4 | 21599867.3 | 10.65 | 1 | [<pybtls.lib.BTLS.Vehicle object at 0x2c1c50770>] |
| 745317 | 2659 | 2 | 204.4 | 21599867.3 | 10.65 | 1 | [<pybtls.lib.BTLS.Vehicle object at 0x2c1c507d0>] |
| 745318 | 2660 | 1 | 529.2 | 21599874.5 | 5.46 | 1 | [<pybtls.lib.BTLS.Vehicle object at 0x2c1c50830>] |
| 745319 | 2660 | 2 | 529.2 | 21599874.5 | 5.46 | 1 | [<pybtls.lib.BTLS.Vehicle object at 0x2c1c50890>] |
745320 rows × 7 columns
PT_V_20_2
| Index | Effect | Value | Time | Position on Bridge | No. Trucks | Trucks | |
|---|---|---|---|---|---|---|---|
| 0 | 1 | 1 | 1076.6 | 15308.3 | -1.15 | 1 | [<pybtls.lib.BTLS.Vehicle object at 0x2c1650950>] |
| 1 | 1 | 2 | 1076.6 | 15308.3 | -1.15 | 1 | [<pybtls.lib.BTLS.Vehicle object at 0x2c16509b0>] |
| 2 | 2 | 1 | 1044.7 | 19863.9 | 18.67 | 1 | [<pybtls.lib.BTLS.Vehicle object at 0x2c1650a10>] |
| 3 | 2 | 2 | 1044.7 | 19863.9 | 18.67 | 1 | [<pybtls.lib.BTLS.Vehicle object at 0x2c1651430>] |
| 4 | 3 | 1 | 1149.6 | 31969.8 | -2.09 | 1 | [<pybtls.lib.BTLS.Vehicle object at 0x2c16515b0>] |
| ... | ... | ... | ... | ... | ... | ... | ... |
| 4369 | 12 | 2 | 1073.3 | 21587461.9 | 23.08 | 1 | [<pybtls.lib.BTLS.Vehicle object at 0x2c16e42f0>] |
| 4370 | 13 | 1 | 1006.2 | 21593103.8 | 18.33 | 1 | [<pybtls.lib.BTLS.Vehicle object at 0x2c16e4350>] |
| 4371 | 13 | 2 | 1006.2 | 21593103.8 | 18.33 | 1 | [<pybtls.lib.BTLS.Vehicle object at 0x2c16e43b0>] |
| 4372 | 14 | 1 | 1056.7 | 21599507.2 | 0.25 | 1 | [<pybtls.lib.BTLS.Vehicle object at 0x2c16e4410>] |
| 4373 | 14 | 2 | 1056.7 | 21599507.2 | 0.25 | 1 | [<pybtls.lib.BTLS.Vehicle object at 0x2c16e4470>] |
4374 rows × 7 columns
POT_summary
PT_S_20_Eff_2
| Peak Index | Time | No. Trucks | Peak Value | |
|---|---|---|---|---|
| 0 | 1 | 15308.3 | 1 | 1076.6 |
| 1 | 2 | 19863.9 | 1 | 1044.7 |
| 2 | 3 | 31969.8 | 1 | 1149.6 |
| 3 | 4 | 33774.4 | 1 | 1109.9 |
| 4 | 5 | 65469.4 | 1 | 1062.8 |
| ... | ... | ... | ... | ... |
| 2182 | 10 | 21575323.8 | 1 | 1078.3 |
| 2183 | 11 | 21576665.2 | 1 | 1102.4 |
| 2184 | 12 | 21587461.9 | 1 | 1073.3 |
| 2185 | 13 | 21593103.8 | 1 | 1006.2 |
| 2186 | 14 | 21599507.2 | 1 | 1056.7 |
2187 rows × 4 columns
PT_S_20_Eff_1
| Peak Index | Time | No. Trucks | Peak Value | |
|---|---|---|---|---|
| 0 | 1 | 77.8 | 1 | 174.2 |
| 1 | 2 | 122.2 | 1 | 151.2 |
| 2 | 3 | 160.6 | 1 | 277.0 |
| 3 | 4 | 166.3 | 1 | 698.5 |
| 4 | 5 | 214.7 | 1 | 207.2 |
| ... | ... | ... | ... | ... |
| 372655 | 2656 | 21599843.5 | 1 | 51.5 |
| 372656 | 2657 | 21599859.9 | 1 | 603.5 |
| 372657 | 2658 | 21599860.3 | 1 | 557.9 |
| 372658 | 2659 | 21599867.3 | 1 | 204.4 |
| 372659 | 2660 | 21599874.5 | 1 | 529.2 |
372660 rows × 4 columns
POT_counter
PT_C_20
| Block | Effect 1 | Effect 2 | |
|---|---|---|---|
| 0 | 1 | 1624 | 8 |
| 1 | 2 | 1435 | 7 |
| 2 | 3 | 1560 | 8 |
| 3 | 4 | 1546 | 8 |
| 4 | 5 | 1602 | 10 |
| ... | ... | ... | ... |
| 282 | 247 | 1392 | 10 |
| 283 | 248 | 1519 | 11 |
| 284 | 249 | 322 | 1 |
| 285 | 249 | 1195 | 2 |
| 286 | 250 | 1465 | 12 |
287 rows × 3 columns
The simulation output can be saved and loaded for further analysis.
[10]:
pb.save_output(sim_output, Path("./temp") / "output.pkl")
sim_output = pb.load_output(Path("./temp") / "output.pkl")
Outputs have been successfully saved to temp/output.pkl!
Outputs have been successfully loaded from temp/output.pkl!