Accurately predicting the demand for ride-hailing services can result in significant benefits such as more effective surge pricing strategies, improved driver positioning, and enhanced customer service. By understanding the demand fluctuations, companies can anticipate and respond to consumer requirements more efficiently, leading to increased efficiency and revenue. However, forecasting demand in a particular region can be challenging, as it is influenced by several external factors, such as time of day, weather conditions, and location. Thus, understanding and evaluating these factors is essential for predicting consumer behavior and adapting to their needs effectively. Grid-based deep learning approaches have proven effective in predicting regional taxi demand. However, these models have limitations in integrating external factors in their spatiotemporal complexity and maintaining high accuracy over extended time horizons without continuous retraining, which makes them less suitable for practical and commercial applications. To address these limitations, this paper introduces STEF-DHNet, a demand prediction model that combines Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) to integrate external features as spatiotemporal information and capture their influence on ride-hailing demand. The proposed model is evaluated using a long-term performance metric called the rolling error, which assesses its ability to maintain high accuracy over long periods without retraining. The results show that STEF-DHNet outperforms existing state-of-the-art methods on three diverse datasets, demonstrating its potential for practical use in real-world scenarios.

翻译：准确预测网约车服务需求可带来显著效益，例如更有效的动态定价策略、优化的司机调度以及提升的客户服务质量。通过理解需求波动，企业能更高效地预测并响应消费者需求，从而提高运营效率和营收。然而，特定区域的需求预测因受时段、天气状况和地理位置等多重外部因素影响而颇具挑战。因此，理解并评估这些因素对于预测消费者行为并有效适应其需求至关重要。基于网格的深度学习方法已被证明可有效预测区域出租车需求。然而，这些模型在整合具有时空复杂性的外部因素方面存在局限，且难以在不持续重新训练的情况下长期保持高精度，这使其在实践和商业应用中适用性不足。为解决上述局限，本文提出STEF-DHNet需求预测模型，该模型融合卷积神经网络（CNN）与长短期记忆网络（LSTM），将外部特征作为时空信息整合，并捕捉其对网约车需求的影响。采用名为滚动误差的长期性能指标评估所提模型，该指标衡量模型在不重新训练情况下长期保持高精度的能力。结果表明，STEF-DHNet在三个不同数据集上均优于现有最先进方法，展示了其在真实场景中的实际应用潜力。