Mobility service route design requires potential demand information to well
accommodate travel demand within the service region. Transit planners and
operators can access various data sources including household travel survey
data and mobile device location logs. However, when implementing a mobility
system with emerging technologies, estimating demand level becomes harder
because of more uncertainties with user behaviors. Therefore, this study
proposes an artificial intelligence-driven algorithm that combines sequential
transit network design with optimal learning. An operator gradually expands its
route system to avoid risks from inconsistency between designed routes and
actual travel demand. At the same time, observed information is archived to
update the knowledge that the operator currently uses. Three learning policies
are compared within the algorithm: multi-armed bandit, knowledge gradient, and
knowledge gradient with correlated beliefs. For validation, a new route system
is designed on an artificial network based on public use microdata areas in New
York City. Prior knowledge is reproduced from the regional household travel
survey data. The results suggest that exploration considering correlations can
achieve better performance compared to greedy choices in general. In future
work, the problem may incorporate more complexities such as demand elasticity
to travel time, no limitations to the number of transfers, and costs for
expansion.

本研究提出了一种结合顺序公交网络设计和最优学习的人工智能驱动算法，以逐步扩大路线系统并更新当前的操作员使用的知识，验证表明，考虑相关性的探索可以实现比贪婪选择更好的性能，在未来的研究中，该问题可能加入更多复杂性如出行时间弹性、换乘次数无限制和扩展成本等方面。