Demand forecasting is an essential task in many industries and the transportation sector is no exception. This is because accurate forecasts are a fundamental aspect of any rationale planning process and an essential component of intelligent transportation systems. In the context of public transit, forecasts are needed to support different level of planning and organisational processes. Short-term forecast, typically a few hours in the future, are developed to support real-time operations. Long-term forecast, typically 5 years or more in the future, are essential for strategic planning. Those two forecast horizons have been widely studied by the academic community but surprisingly little research deal with forecast between those two ranges. The objective of this paper is therefore twofold. First, we proposed a generic modelling approach to forecast next 365 days ridership in a public transit network at different levels of spatiotemporal aggregation. Second, we illustrate how such models can assist public transit operators and transit agencies in monitoring ridership and supporting recurrent tactical planning tasks. The proposed formulation is based on a multiplicative decomposition that combines tree-based models with trend forecasting. The evaluation of models on unseen data proves that this approach generates coherent forecast. Different use cases are then depicted. They demonstrate that the resulting forecast can support various recurrent tactical tasks such as setting future goals, monitoring ridership or supporting the definition of service provision. Overall, this study contributes to the growing literature on the use of automated data collection. It confirms that more sophisticated statistical methods can help to improve public transportation planning and enhance data-driven decision making.

需求预测是许多行业的重要任务，运输行业也不例外。这是因为准确的预测是任何基本原理计划过程的基本方面，也是智能运输系统的重要组成部分。在公共交通的背景下，需要进行预测以支持不同级别的规划和组织过程。为了支持实时操作，通常会在未来几个小时内进行短期预测。长期预测（通常是未来5年或更长时间）对于战略规划至关重要。这两个预测范围已被学术界广泛研究，但令人惊讶的是，很少有研究涉及这两个范围之间的预测。因此，本文的目的是双重的。第一的，我们提出了一种通用的建模方法来预测时空聚集不同级别的公交网络中未来365天的乘车人次。其次，我们说明了这些模型如何协助公交运营商和公交机构监控乘客量并支持经常性战术计划任务。所提出的公式是基于将基于树的模型与趋势预测相结合的乘法分解的。对看不见的数据进行模型评估证明该方法可生成连贯的预测。然后描述了不同的用例。他们证明，得出的预测结果可以支持各种周期性的战术任务，例如设定未来目标，监控乘员率或支持服务提供的定义。全面的，这项研究为有关使用自动数据收集的文献不断增加做出了贡献。它证实了更复杂的统计方法可以帮助改善公共交通规划并增强以数据为依据的决策。