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.

通过应用图神经网络捕捉城市环境中的空间依赖性和模式，本研究提出了一种新颖的出租车需求预测系统。该系统采用区域中立的方法，利用变分自编码器将输入特征分解为区域特定和区域中立的组件，以实现跨区域出租车需求预测并推广到不同城市区域。实验证明了该系统在准确预测出租车需求方面的有效性，甚至在以前观察不到的区域，从而展示了其优化出租车服务和提高交通效率的潜力。

一模適用於所有地區的計程車需求預測