  Bus bunching is a natural-occurring phenomenon that undermines the efficiency
and stability of the public transportation system. The mainstream solutions
control the bus to intentionally stay longer at certain stations. Existing
control methods include conventional methods that provide a formula to
calculate the control time and reinforcement learning (RL) methods that
determine the control policy through repeated interactions with the system. In
this paper, we propose an integrated proximal policy optimization model with
dual-headway (IPPO-DH). IPPO-DH integrates the conventional headway control
with reinforcement learning, so that it acquires the advantages of both
algorithms -- it is more efficient in normal environments and more stable in
harsh ones. To demonstrate such an advantage, we design a bus simulation
environment and compare IPPO-DH with RL and several conventional methods. The
results show that the proposed model maintains the application value of the
conventional method by avoiding the instability of the RL method in certain
environments, and improves the efficiency compared with the conventional
control, shedding new light on real-world bus transit system optimization.
