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.

翻译：出行服务路线设计需要潜在需求信息，以充分满足服务区域内的出行需求。交通规划者和运营商可以获取多种数据源，包括家庭出行调查数据和移动设备位置日志。然而，在实施采用新兴技术的出行系统时，由于用户行为的不确定性增加，需求水平的估计变得更加困难。因此，本研究提出了一种人工智能驱动的算法，将序贯交通网络设计与最优学习相结合。运营商逐步扩展其路线系统，以避免因设计路线与实际出行需求不一致而带来的风险。同时，收集到的信息被存档，以更新运营商当前使用的知识。在该算法中比较了三种学习策略：多臂赌博机、知识梯度以及具有相关信念的知识梯度。为了验证，基于纽约市的公共使用微观数据区域，在人工网络上设计了一个新的路线系统。先验知识来源于区域家庭出行调查数据。结果表明，考虑相关性的探索总体上比贪婪选择能实现更好的性能。在未来的工作中，该问题可能融入更多复杂性，例如出行时间需求弹性、换乘次数无限制以及扩展成本。