The efficiency of urban traffic management and congestion alleviation relies heavily on accurate forecasting of Origin-Destination (O-D) demand matrices. Existing models primarily focus on estimating O-D demand for various travel purposes throughout the day, which is characterised by its pulsating nature. However, these models often compromise the precision of peak-hour forecasts, leading to unreliable dynamic traffic control and challenges in effectively reducing peak-hour congestion. To tackle this challenge, this paper proposes a novel method for predicting commuting O-D demand matrices. Our method employs community detection algorithms on road networks to precisely partition commute O-D regions, incorporating Points of Interest (POIs). We also present a spatio-temporal dynamic weighted hypergraph model that leverages these partitioned regions, time characteristics from observed O-D trips, and meteorological data to improve forecasting. Comparative analyses with contemporary models and ablation studies indicate our method significantly enhances prediction accuracy, by approximately 5%. These findings imply that the proposed method more effectively encompasses the varied characteristics of commuting during peak hours, thereby providing more accurate demand matrices for urban traffic management.

中文翻译：

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通过集成社区检测和超图表示学习，开发一种估计城市通勤交通的新方法

城市交通管理和缓解拥堵的效率在很大程度上依赖于对出发地-目的地（OD）需求矩阵的准确预测。现有模型主要侧重于估算全天各种出行目的的 OD 需求，其特点是脉动性。然而，这些模型往往会损害高峰时段预测的精度，导致动态交通控制不可靠，并给有效减少高峰时段拥堵带来挑战。为了应对这一挑战，本文提出了一种预测通勤 OD 需求矩阵的新方法。我们的方法在道路网络上采用社区检测算法来精确划分通勤 OD 区域，并结合兴趣点 (POI)。我们还提出了一个时空动态加权超图模型，该模型利用这些分区、观测到的 OD 行程的时间特征以及气象数据来改进预测。与当代模型和消融研究的比较分析表明，我们的方法显着提高了预测准确性，大约提高了 5%。这些发现表明，所提出的方法更有效地涵盖了高峰时段通勤的不同特征，从而为城市交通管理提供了更准确的需求矩阵。