Detailed knowledge of service utilisation and passenger load profiles is the basis for the design, operation, and adjustment of a public transport service. The advancement in sensing technologies enable transit operators to monitor the variabilities in passenger flows continuously and consistently. There is a growing body of literature on using supervised learning models with direct passenger counts from historical observations. However, the incomplete, inaccurate, and biased data from automatic sensors pose challenges in this process. This paper proposes novel supervised learning models to estimate the onboard load profile of public transport services based on two main data sources: (1) limited data collected on a subset of service vehicles by automatic passenger counting (APC) systems, and (2) fare data collected by automatic fare collection (AFC) systems. The specific consideration is given to the fact that the developed models can be transferred across different routes. This is motivated by the commonly “limited coverage” of automated passenger counter devices on service vehicles. We introduce an array of new models, including a superior segment-based model, which demonstrates remarkable improvement in model transferability and accuracy. The proposed methodology utilises separate methods in different segments of a transit line. The proposed models were applied to three tram lines in Melbourne, Australia, where various types of shortcomings exist in the automated data. The test results demonstrate that the proposed models can be transferred and applied to other transit route without relying on historical observations. This would enable transit operators to reduce the number of required devices and monitor service utilisation in a more cost-efficiently manner, particularly in public transport networks where AFC coverage is usually incomplete and negatively skewed. The information on service utilisation will not only help operators to accommodate the variability in passenger demand but also assist passengers in journey planning to avoid overcrowding on services.

详细了解服务利用率和乘客负载情况是公共交通服务设计、运营和调整的基础。传感技术的进步使交通运营商能够持续、一致地监控客流的变化。越来越多的文献涉及使用监督学习模型和历史观察中的直接乘客计数。然而，来自自动传感器的不完整、不准确和有偏差的数据给这一过程带来了挑战。本文提出了新颖的监督学习模型，基于两个主要数据源来估计公共交通服务的车载负载概况：（1）通过自动乘客计数（APC）系统收集的服务车辆子集的有限数据，(2) 自动收费 (AFC) 系统收集的票价数据。特别考虑到所开发的模型可以跨不同路线转移。这是由于服务车辆上的自动乘客柜台设备通常“覆盖有限”。我们引入了一系列新模型，包括基于分段的卓越模型，该模型在模型可移植性和准确性方面表现出显着改进。所提出的方法在公交线路的不同路段采用不同的方法。所提出的模型已应用于澳大利亚墨尔本的三条有轨电车线路，这些线路的自动化数据存在各种类型的缺陷。测试结果表明，所提出的模型可以转移并应用于其他交通路线，而无需依赖历史观测。这将使交通运营商能够减少所需设备的数量，并以更具成本效益的方式监控服务利用率，特别是在 AFC 覆盖通常不完整且负偏的公共交通网络中。有关服务利用的信息不仅可以帮助运营商适应乘客需求的变化，还可以帮助乘客规划行程，避免服务过度拥挤。