Follow these steps:

1. Define a Route: Create a route file (e.g., `myroute.rou.xml`) that defines the routes the vehicles will follow. Specify the desired start and end edges for each route.

2. Define a Vehicle Type: Create a vehicle type file (e.g., `myvtype.add.xml`) that defines the vehicle types and their properties, such as maximum speed. You can define multiple vehicle types with different speed values to simulate vehicles moving at different speeds.

3. Generate Vehicle Trajectories: Use the `randomTrips.py` script provided with SUMO to generate vehicle trajectories. This script allows you to specify the number of vehicles to generate, their depart times, and their routes. For example, you can gradually increase the number of vehicles generated over time.

Here's an example command to generate vehicles using `randomTrips.py`:

````

randomTrips.py -n mynetwork.net.xml -r myroute.rou.xml -e 1000 -o mytrips.trips.xml --additional-files myvtype.add.xml

```

This command generates 1000 trips (`-e 1000`) based on the defined route file, and the additional vehicle type file is specified with `--additional-files`.

4. Run SUMO Simulation: Launch the SUMO simulation using the generated vehicle trajectories:

````

sumo-gui -n mynetwork.net.xml -r mytrips.trips.xml -a myvtype.add.xml

```

This command opens the SUMO-GUI with the network layout, vehicles, and simulation settings. You can visualize the simulation in real-time and observe the congestion scenario.

To extract the average speed from SUMO and use it in Python to plot the results, you can utilize the TraCI interface and follow these steps:

1. Import TraCI in Python: Import the TraCI library in your Python script to establish a connection with the SUMO simulation and retrieve simulation data.

````python

import traci

```

2. Connect to SUMO: Establish a connection with the running SUMO simulation within your Python script.

````python

traci.init(port=)

```

3. Retrieve Vehicle Speeds: Within a simulation loop, retrieve the speeds of the vehicles at each time step using the `traci.vehicle.getSpeed()` function. Store the speeds in a list or any data structure for further analysis.

````python

speeds = []

for step in range(simulation_steps):

traci.simulationStep()

vehicle_ids = traci.vehicle.getIDList()

for vehicle_id in vehicle_ids:

speed = traci.vehicle.getSpeed(vehicle_id)

speeds.append(speed)

```

4. Calculate Average Speed: After the simulation loop, calculate the average speed from the collected speed data.

````python

average_speed = sum(speeds) / len(speeds)

```

5. Plot the Results: Use a plotting library such as Matplotlib to visualize the results. You can plot the speed values over time or create histograms to analyze the distribution of speeds.

````python

import matplotlib.pyplot as plt

# Plotting speed over time

time = range(simulation_steps)

plt.plot(time, speeds)

plt.xlabel('Time Step')

plt.ylabel('Speed')

plt.title('Vehicle Speed Over Time')

plt.show()

# Plotting speed distribution

plt.hist(speeds, bins=20)

plt.xlabel('Speed')

plt.ylabel('Frequency')

plt.title('Vehicle Speed Distribution')

plt.show()

```

Remember to close the TraCI connection and end the simulation when you are done:

```python

traci.close()

```

Hope it helps: credit AI.