Deep Reinforcement Learning based Dynamic Optimization of Bus Timetable
  Bus timetable optimization is a key issue to reduce operational cost of bus
companies and improve the service quality. Existing methods use exact or
heuristic algorithms to optimize the timetable in an offline manner. In
practice, the passenger flow may change significantly over time. Timetables
determined in offline cannot adjust the departure interval to satisfy the
changed passenger flow. Aiming at improving the online performance of bus
timetable, we propose a Deep Reinforcement Learning based bus Timetable dynamic
Optimization method (DRL-TO). In this method, the timetable optimization is
considered as a sequential decision problem. A Deep Q-Network (DQN) is employed
as the decision model to determine whether to dispatch a bus service during
each minute of the service period. Therefore, the departure intervals of bus
services are determined in real time in accordance with passenger demand. We
identify several new and useful state features for the DQN, including the load
factor, carrying capacity utilization rate, and the number of stranding
passengers. Taking into account both the interests of the bus company and
passengers, a reward function is designed, which includes the indicators of
full load rate, empty load rate, passengers' waiting time, and the number of
stranding passengers. Building on an existing method for calculating the
carrying capacity, we develop a new technique to enhance the matching degree at
each bus station. Experiments demonstrate that compared with the timetable
generated by the state-of-the-art bus timetable optimization approach based on
a memetic algorithm (BTOA-MA), Genetic Algorithm (GA) and the manual method,
DRL-TO can dynamically determine the departure intervals based on the real-time
passenger flow, saving 8$\%$ of vehicles and reducing 17$\%$ of passengers'
waiting time on average.
