COeXISTENCE is an ecosystem to experiment with future Urban Traffic Systems, where routing decisions are simulatenously made by humans and autonomous vehicles.

The following use-case synthesize main features of an ecosystem:

• You import road network of a given urban areas from Open Street Map
• You generate a demand pattern, where each of agents is specified with own traits and travel demans $(o_i,d_i,{\tau }_i$)
• You control your experiment with a .json file and specify details of conducted experiment (or set of experiments).
• You specify your human behaviour models to accurately reproduce how human drivers select routes.
• You generate choice set of paths for each agent to select from.
• You connect with SUMO traffic simulator to be used as environment to compute travel costs.
• You run $n$ days of human learning (SUMO days), hoping the system will stabilize in proximity of Wardrop User Equilibrium
• You introduce mutation and replace some human agents with AVs.
• You determine reinforcement learning algorithm for each agent by defining rewards, observations and hyperparameters
• You train your algorithms until it finds suitable policy
• You roll-out the trained policy and observe impact of new routing on the system.
• You further allow humans to adapt to actions of AVs and allow AVs to refine its policies.

Each of above handled with specific packages/modules/workflows of COeXISTENCE: