Travel demand forecasting is useful for both trip and service planning, and thus is of great importance. Most existing studies focus on demand forecasting for a single mode, while much less attention has been paid to multimodal demand forecasting. This paper develops a multimodal demand forecasting approach, which can learn and utilize information/knowledge from different public transit modes and thus improve the demand prediction of the travel mode with sparse observations (e.g., station-sparse mode). In particular, this study focuses on improving the passenger demand prediction accuracy of the station-sparse mode(s) with the help of the station-intensive mode (i.e., the mode with more sufficient knowledge and intensive station distribution over space). We propose a novel Knowledge Adaptation with Attentive Multi-task Memory Network (KA2M2) in order to utilize closely-related demand patterns from the station-intensive mode for demand forecasting of the station-sparse mode(s). Specifically, we first design a memory-augmented recurrent network for enhancing the ability to capture the long-and-short term demand information and storing the extracted temporal knowledge of each transit mode. Then, we develop and integrate an attention-based knowledge adaptation module to adapt relevant information from the station-intensive source to the station-sparse source(s). The experimental results on a real-world dataset collected from the Greater Sydney area covering four public transport modes (bus, train, light rail, and ferry) demonstrate that the proposed approach consistently outperforms a number of baseline methods and state-of-the-art models. Our findings also illustrate that incorporating information/knowledge from multimodal trip records can enhance the demand forecasting accuracy for station-sparse modes.

旅行需求预测对旅行和服务规划都很有用，因此非常重要。大多数现有研究都集中在单一模式的需求预测上，而对多模式需求预测的关注则少得多。本文开发了一种多模式需求预测方法，该方法可以学习和利用来自不同公共交通模式的信息/知识，从而改进具有稀疏观测（例如，站点稀疏模式）的出行模式的需求预测。特别是，本研究着眼于借助车站密集模式（即知识更充足且车站分布在空间上密集的模式）来提高车站稀疏模式的乘客需求预测精度。我们提出了一个新颖的ķ nowledge一daptation有一个ttentive中号ULTI-任务中号埃默里网（KA2M2) 以便利用与站点密集模式密切相关的需求模式进行站点稀疏模式的需求预测。具体来说，我们首先设计了一个记忆增强循环网络，以增强捕获长期和短期需求信息的能力，并存储提取的每种交通方式的时间知识。然后，我们开发并集成了一个基于注意力的知识适应模块，以将相关信息从站点密集源适配到站点稀疏源。从大悉尼地区收集的涵盖四种公共交通方式（公共汽车、火车、轻轨和渡轮）的真实世界数据集的实验结果表明，所提出的方法始终优于许多基线方法和最新状态——艺术模型。