Flow Examples

Before continuing to the Flow examples, we recommend installing Flow by executing the following installation instructions.

The examples folder provides several examples demonstrating how both simulation and RL-oriented experiments can be setup and executed within the Flow framework on a variety of traffic problems. These examples are .py files that may be executed either from terminal or via an editor. For example, in order to execute the sugiyama example in examples/sumo, we run:

python <flow-path>/examples/sumo/sugiyama.py

The examples are distributed into the following sections:

examples/sumo/ contains examples of transportation network with vehicles following human-dynamical models of driving behavior using the traffic micro-simulator sumo.

examples/aimsun/ contains examples of transportation network with vehicles following human-dynamical models of driving behavior using the traffic micro-simulator Aimsun.

examples/rllib/ provides similar networks as those presented in the previous point, but in the present of autonomous vehicle (AV) or traffic light agents being trained through RL algorithms provided by RLlib.

Simulated Examples

The following networks are available for simulation within flow, and specifically the examples/sumo folder. Similar networks are available with trainable variants in the examples/rllib and examples/aimsun folders; however, they may be under different names.

bay_bridge.py & bay_bridge_toll.py

Perform simulations of vehicles on the Oakland-San Francisco Bay Bridge.

Unlike bay_bridge.py, bay_bridge_toll.py consists of vehicles being placed only on the toll booth and sections of the road leading up to it.

bottlenecks.py

Example demonstrating formation of congestion in bottleneck

figure_eight.py

Example of a figure 8 network with human-driven vehicles.

Right-of-way dynamics near the intersection causes vehicles to queue up on either side of the intersection, leading to a significant reduction in the average speed of vehicles in the network.

traffic_light_grid.py

Performs a simulation of vehicles on a traffic light grid.

highway.py

Example of an open multi-lane network with human-driven vehicles.

merge.py

Example of a merge network with human-driven vehicles.

In the absence of autonomous vehicles, the network exhibits properties of convective instability, with perturbations propagating upstream from the merge point before exiting the network.

minicity.py

Example of modified minicity of University of Delaware network with human-driven vehicles.

sugiyama.py

Used as an example of sugiyama experiment.

This example consists of 22 IDM cars on a ring road creating shockwaves.