RL-MSA: a Reinforcement Learning-based Multi-line bus Scheduling
  Approach
Yingzhuo Liu
Multiple Line Bus Scheduling Problem (MLBSP) is vital to save operational
cost of bus company and guarantee service quality for passengers. Existing
approaches typically generate a bus scheduling scheme in an offline manner and
then schedule buses according to the scheme. In practice, uncertain events such
as traffic congestion occur frequently, which may make the pre-determined bus
scheduling scheme infeasible. In this paper, MLBSP is modeled as a Markov
Decision Process (MDP). A Reinforcement Learning-based Multi-line bus
Scheduling Approach (RL-MSA) is proposed for bus scheduling at both the offline
and online phases. At the offline phase, deadhead decision is integrated into
bus selection decision for the first time to simplify the learning problem. At
the online phase, deadhead decision is made through a time window mechanism
based on the policy learned at the offline phase. We develop several new and
useful state features including the features for control points, bus lines and
buses. A bus priority screening mechanism is invented to construct bus-related
features. Considering the interests of both the bus company and passengers, a
reward function combining the final reward and the step-wise reward is devised.
Experiments at the offline phase demonstrate that the number of buses used of
RL-MSA is decreased compared with offline optimization approaches. At the
online phase, RL-MSA can cover all departure times in a timetable (i.e.,
service quality) without increasing the number of buses used (i.e., operational
cost).