  An efficient urban bus control system has the potential to significantly
reduce travel delays and streamline the allocation of transportation resources,
thereby offering enhanced and user-friendly transit services to passengers.
However, bus operation efficiency can be impacted by bus bunching. This problem
is notably exacerbated when the bus system operates along a signalized corridor
with unpredictable travel demand. To mitigate this challenge, we introduce a
multi-strategy fusion approach for the longitudinal control of connected and
automated buses. The approach is driven by a physics-informed deep
reinforcement learning (DRL) algorithm and takes into account a variety of
traffic conditions along urban signalized corridors. Taking advantage of
connected and autonomous vehicle (CAV) technology, the proposed approach can
leverage real-time information regarding bus operating conditions and road
traffic environment. By integrating the aforementioned information into the
DRL-based bus control framework, our designed physics-informed DRL state fusion
approach and reward function efficiently embed prior physics and leverage the
merits of equilibrium and consensus concepts from control theory. This
integration enables the framework to learn and adapt multiple control
strategies to effectively manage complex traffic conditions and fluctuating
passenger demands. Three control variables, i.e., dwell time at stops, speed
between stations, and signal priority, are formulated to minimize travel
duration and ensure bus stability with the aim of avoiding bus bunching. We
present simulation results to validate the effectiveness of the proposed
approach, underlining its superior performance when subjected to sensitivity
analysis, specifically considering factors such as traffic volume, desired
speed, and traffic signal conditions.
