基于深度学习的城市轨道交通线路跳站策略优化方法
A Deep-Learning Based Optimization Approach to Address Stop-Skipping Strategy in Urban Rail Transit Lines
摘要：
中转站的乘客需求率不同，这突出表明必须采取业务战略，提供满足需求的服务。以提高乘客出行时间为目标，采用数据驱动的优化方法确定城市轨道交通线路的最优停跳模式。具体而言，首先，使用整个月的时间序列灵巧卡数据，我们采用长短期记忆（LSTM）深度学习模型来预测高峰小时的车站级需求率。该预测基于前四个小时，并且特别重要的是，要知道高峰小时的真实需求率是仅在高峰小时操作完成之后才能获得的后验信息。此外，利用实时预测，而不是假设固定的需求率，使我们能够考虑到意外的实时变化，这可能是有害的后续分析。然后，我们将LSTM模型的输出作为优化模型的输入，目标是最小化顾客的总旅行时间。考虑到问题的指数性质，我们提出了一种蚁群优化技术，以解决问题在所需的时间。最后，所提出的模型和解决方案的算法的性能进行评估，使用真实的案例数据。结果表明，所提出的方法可以提高服务的性能，通过改善乘客的在车时间以及乘客的等待时间。
英文摘要：
Different passenger demand rates in transit stations underscore the importance of adopting operational strategies to provide a demand-responsive service. Aiming at improving passengers' travel time, the present study introduces an advanced data-driven optimization approach to determine the optimal stop-skip pattern in urban rail transit lines. In detail, first, using the time-series smart card data for an entire month, we employ a Long Short-Term Memory (LSTM) deep learning model to predict the station-level demand rates for the peak hour. This prediction is based on four preceding hours and is especially important knowing that the true demand rates of the peak hour are posterior information that can be obtained only after the peak hour operation is finished. Moreover, utilizing a real-time prediction instead of assuming fixed demand rates, allows us to account for unexpected real-time changes which can be detrimental to the subsequent analyses. Then, we integrate the output of the LSTM model as an input to an optimization model with the objective of minimizing patrons' total travel time. Considering the exponential nature of the problem, we propose an Ant Colony Optimization technique to solve the problem in a desirable amount of time. Finally, the performance of the proposed models and the solution algorithm is assessed using real case data. The results suggest that the proposed approach can enhance the performance of the service by improving both passengers' in-vehicle time as well as passengers' waiting time.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
