  Traffic flow prediction is an important part of smart transportation. The
goal is to predict future traffic conditions based on historical data recorded
by sensors and the traffic network. As the city continues to build, parts of
the transportation network will be added or modified. How to accurately predict
expanding and evolving long-term streaming networks is of great significance.
To this end, we propose a new simulation-based criterion that considers
teaching autonomous agents to mimic sensor patterns, planning their next visit
based on the sensor's profile (e.g., traffic, speed, occupancy). The data
recorded by the sensor is most accurate when the agent can perfectly simulate
the sensor's activity pattern. We propose to formulate the problem as a
continuous reinforcement learning task, where the agent is the next flow value
predictor, the action is the next time-series flow value in the sensor, and the
environment state is a dynamically fused representation of the sensor and
transportation network. Actions taken by the agent change the environment,
which in turn forces the agent's mode to update, while the agent further
explores changes in the dynamic traffic network, which helps the agent predict
its next visit more accurately. Therefore, we develop a strategy in which
sensors and traffic networks update each other and incorporate temporal context
to quantify state representations evolving over time.
