Basic Example#

This demo shows users how to generate traffic using PyBTLS. It generates a 1-month 1-lane Auxerre-equivalent traffic without calculating any load effect.

To do this, we need to:

Step 1: Define a traffic generator;

Step 2: Request the output of vehicle files;

Step 3: Set and run the simulation.

Step 4: Load the generated traffic.

Import all the necessary modules.

[18]:

import pybtls as pb
from pathlib import Path

Step 1: Define a traffic generator, which consists of three components:

1.1. Lane flow composition (LFC): determines the traffic flow rate and vehicle class proportions.

1.2. Vehicle generator: determines the vehicle types and their characteristics.

1.3. Headway generator: determines the vehicle headway distribution.

1.1. Define the lane flow composition. The traffic will have the following properties:

  • The traffic is identical among 24 hours of a day.

  • 80 cars/hour/lane and 20 trucks/hour/lane are generated.

  • The mean vehicle speed is 80 km/h with a standard deviation of 10 km/h.

  • Trucks are in the following composition:

No. Axles

2

3

4

5

Relative comps (%)

23

2.8

31.7

42.5

 
[ ]:

lfc = pb.LaneFlowComposition(lane_index = 1, lane_dir = 1)
lfc.assign_lane_data(  # 24 hours per day by default
    hourly_truck_flow=[80] * 24,
    hourly_car_flow=[20] * 24,
    hourly_speed_mean=[80 / 3.6 * 10] * 24,  # in dm/s
    hourly_speed_std=[10 / 3.6 * 10] * 24,  # in dm/s
    hourly_truck_composition=[[23, 2.8, 31.7, 42.5]] * 24,
)

1.2. Choose the Grave vehicle generator for Auxerre-equivalent traffic.

(Note that the Grave generator is designed for several built-in traffic sites. It is recommended to use the Garage generator for customized traffic sites, which will be covered in later tutorials.)

[20]:

vehicle_gen = pb.VehicleGenGrave(traffic_site = "Auxerre")

1.3. Choose the Freeflow headway generator (Poisson arrival theory).

[21]:

headway_gen = pb.HeadwayGenFreeflow()

Getting the traffic generator by combining the lane flow composition, vehicle generator, and headway generator.

[22]:

traffic_gen = pb.TrafficGenerator(no_lane = 1)
traffic_gen.add_lane(
    vehicle_gen = vehicle_gen,
    headway_gen = headway_gen,
    lfc = lfc,
)

Step 2: Specify the output we want - the traffic data.

[ ]:

output_config = pb.OutputConfig()
output_config.set_vehicle_file_output(write_vehicle_file = True)

Step 3: Set and start a new simulation.

The finished traffic output is stored in ./temp/minimum_example.

[ ]:

sim_task = pb.Simulation(output_dir = Path("./temp"))  # Can specify a different output directory here.
sim_task.add_sim(
    traffic = traffic_gen,
    no_day = 25,  # 25 working days per month
    output_config = output_config,
    tag = "minimum_example",
)
sim_task.run()

Step 4: Load the generated traffic as a dataframe.

[31]:

sim_output = sim_task.get_output()["minimum_example"]
generated_vehicle = sim_output.read_data("traffic")["output_traffic"]

from IPython.display import display  # Display the variable in jupyter notebook (optional)
display(generated_vehicle)

[31]:
Head Year Month Day Hour Min Sec NoAxles NoAxleGroups GVW Velocity Length Lane Dir Trns AxleWeights AxleSpacings AxleWidths
0 1001 1 1 0 0 0 21.757 5 0 380.22579 18.888889 10.660 1 1 1.8 [58.32045000000001, 145.02123, 58.958100000000... [3.307, 5.243, 1.119, 0.991, 0.0] [1.98, 1.98, 1.98, 1.98, 1.98]
1 1001 1 1 0 0 1 0.945 2 0 144.15795 17.777778 5.932 1 1 1.8 [46.15605000000001, 98.0019] [5.932, 0.0] [1.98, 1.98]
2 1001 1 1 0 0 1 33.245 2 0 52.31673 20.555556 5.946 1 1 1.8 [14.37165, 37.945080000000004] [5.946, 0.0] [1.98, 1.98]
3 1001 1 1 0 0 1 42.168 3 0 243.16047 21.666667 6.336 1 1 1.8 [80.61858000000001, 98.22753000000002, 64.30455] [5.136, 1.199, 0.0] [1.98, 1.98, 1.98]
4 1001 1 1 0 0 1 51.885 4 0 546.17175 21.388889 10.524 1 1 1.8 [85.41567, 187.11594000000002, 136.82007000000... [3.157, 6.077, 1.29, 0.0] [1.98, 1.98, 1.98, 1.98]
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
58635 1001 25 1 0 23 59 1.680 4 0 423.10530 25.555556 10.053 1 1 1.8 [67.01211, 145.85508000000002, 105.12396000000... [3.104, 6.006, 0.943, 0.0] [1.98, 1.98, 1.98, 1.98]
58636 1001 25 1 0 23 59 9.968 2 0 20.00259 20.277778 4.000 1 1 1.8 [9.996390000000002, 9.996390000000002] [4.0, 0.0] [1.98, 1.98]
58637 1001 25 1 0 23 59 19.765 5 0 427.84353 31.111111 11.061 1 1 1.8 [61.06725000000001, 128.84454000000002, 79.313... [3.315, 5.595, 1.045, 1.106, 0.0] [1.98, 1.98, 1.98, 1.98, 1.98]
58638 1001 25 1 0 23 59 53.692 4 0 455.04666 20.555556 11.081 1 1 1.8 [67.78710000000001, 154.90971000000002, 116.17... [3.267, 6.349, 1.464, 0.0] [1.98, 1.98, 1.98, 1.98]
58639 1001 1 2 0 0 0 30.426 2 0 20.00259 23.333333 4.000 1 1 1.8 [9.996390000000002, 9.996390000000002] [4.0, 0.0] [1.98, 1.98]

58640 rows × 18 columns