Long-term Joint Scheduling for Urban Traffic
  Recently, the traffic congestion in modern cities has become a growing worry
for the residents. As presented in Baidu traffic report, the commuting stress
index has reached surprising 1.973 in Beijing during rush hours, which results
in longer trip time and increased vehicular queueing. Previous works have
demonstrated that by reasonable scheduling, e.g, rebalancing bike-sharing
systems and optimized bus transportation, the traffic efficiency could be
significantly improved with little resource consumption. However, there are
still two disadvantages that restrict their performance: (1) they only consider
single scheduling in a short time, but ignoring the layout after first
reposition, and (2) they only focus on the single transport. However, the
multi-modal characteristics of urban public transportation are largely
under-exploited. In this paper, we propose an efficient and economical
multi-modal traffic scheduling scheme named JLRLS based on spatio -temporal
prediction, which adopts reinforcement learning to obtain optimal long-term and
joint schedule. In JLRLS, we combines multiple transportation to conduct
scheduling by their own characteristics, which potentially helps the system to
reach the optimal performance. Our implementation of an example by PaddlePaddle
is available at https://github.com/bigdata-ustc/Long-term-Joint-Scheduling,
with an explaining video at https://youtu.be/t5M2wVPhTyk.
