Rail transit delays are generally discussed in terms of on-time performance or problems at individual stops. Such stop-scale approaches ignore the fact that delays are also caused and perpetuated by network-wide factors (e.g., bottlenecks caused by shared tracks by multiple transit lines). The objective of this paper is to develop a network model and metrics that can quantify the delay dependencies between transit network stops, and identify local sources of network-wide issues. For this purpose, Bayesian network learning (at the intersection of machine learning and network science) was utilized. Based on the calculated Bayesian networks (BNs), network metrics (inducer and susceptible) were formulated to quantify the network-wide impacts of the delays experienced at the stops. To implement the proposed framework, the delays at Long Island Rail Road (LIRR) were gathered through a crowdsourced real-time transit information app called onTime. The developed BN model was tested through cross-validation, yielded promising accuracy results, successfully identified the problematic stops based on LIRR reports, and provided further insights on network impacts. The BN model and the developed metrics were further tested using a natural experiment, i.e., a before and after study focusing on a recently completed track expansion project at LIRR. The findings imply that BN learning can successfully identify the network dependencies and indicate the rail links/corridors that are the best candidate for subsequent improvement investments. Overall, the developed metrics can quantify the delay dependencies between stops and they can be used by policy makers and practitioners for investment and improvement decisions.

通常根据准时性能或各个站点的问题来讨论轨道交通延误。这种停止规模方法忽略了以下事实：网络范围内的各种因素（例如，多条公交线路共享航迹造成的瓶颈）也造成并造成了延误。本文的目的是开发一种网络模型和度量标准，可以量化公交网络站点之间的延迟依赖关系，并确定网络范围问题的本地来源。为此，利用了贝叶斯网络学习（在机器学习和网络科学的交集处）。基于计算出的贝叶斯网络（BN），网络指标（诱导者和易感者），以量化停站所遇到的延误对整个网络的影响。为了实施建议的框架，长岛铁路（LIRR）的延误是通过众包的实时交通信息应用程序onTime收集的。通过交叉验证对开发的BN模型进行了测试，得出了有希望的准确性结果，并根据LIRR报告成功识别了有问题的停靠点，并提供了对网络影响的进一步见解。BN模型和已开发的度量标准使用自然实验进行了进一步测试，即之前和之后的研究集中于LIRR最近完成的轨道扩展项目。研究结果表明，BN学习可以成功地识别网络依存关系，并指出铁路线/走廊是进行后续改进投资的最佳人选。