The growing demand for ride-hailing services has led to an increasing need for accurate taxi demand prediction. Existing systems are limited to specific regions, lacking generalizability to unseen areas. This paper presents a novel taxi demand forecasting system that leverages a graph neural network to capture spatial dependencies and patterns in urban environments. Additionally, the proposed system employs a region-neutral approach, enabling it to train a model that can be applied to any region, including unseen regions. To achieve this, the framework incorporates the power of Variational Autoencoder to disentangle the input features into region-specific and region-neutral components. The region-neutral features facilitate cross-region taxi demand predictions, allowing the model to generalize well across different urban areas. Experimental results demonstrate the effectiveness of the proposed system in accurately forecasting taxi demand, even in previously unobserved regions, thus showcasing its potential for optimizing taxi services and improving transportation efficiency on a broader scale.

翻译：人们对网约车服务需求的日益增长，促使了对出租车需求精准预测的需求不断提升。现有系统局限于特定区域，缺乏对未见区域的泛化能力。本文提出了一种新颖的出租车需求预测系统，该系统利用图神经网络来捕捉城市环境中的空间依赖关系和模式。此外，所提出的系统采用了一种区域无关的方法，使其能够训练出适用于任何区域（包括未见区域）的模型。为实现此目标，该框架借助变分自编码器的能力，将输入特征解耦为区域特定组件和区域无关组件。区域无关特征有助于实现跨区域的出租车需求预测，使得模型能够在不同城市区域间很好地泛化。实验结果证明了所提出的系统在准确预测出租车需求方面的有效性，即使是在先前未观测到的区域也是如此，从而展示了其在更广泛范围内优化出租车服务、提升交通效率的潜力。