| 摘要: | 摘要
運量預測在城市交通管理中具有重要的應用價值其中起迄之預測更能有效瞭解乘客的移動模式進而有效地安排列車運行時間表和車輛配置，以匹配實際需求，然而傳統方法常常無法有效應對節假日、特殊活動或突發事件散場對乘運量的影響，這導致預測準確度不高。為解決這一問題，本研究提出了一種類神經網路預測模型，結合活動標籤和歷史分時起迄數據，以提高對特殊活動期間乘客起迄分佈的準確預測能力。
該模型利用PyTorch框架中的nn.Module構建，輸入包括時段、進站點、出站點、人次數據、工作日或假日、異常事件標記、活動類型及起迄點間行車時間等特徵，輸出則為各站點的出站比例，同時本研究因起迄比例為機率分佈故使用交叉熵損失函數能有效提高模型對捷運站點起迄分佈的準確預測。
在學習率算法上，本研究從Adam轉為SGD，並手動調整學習率以避免模型輸出極端值，顯著改善了模型對高維度數據的適應性。此外本研究提出了針對臺北捷運的預測性調度和增強服務建議，包括找出與發生特殊活動站點關聯較強的迄點並提前於該站點做準備和制定活動合作策略，以提升整體運營效率和乘客滿意度。
;Abstract
Passenger volume prediction in urban transportation management is crucial, particularly origin-destination (OD) prediction, which helps understand passenger movement patterns and efficiently schedule train operations and vehicle allocation. Traditional methods often fail to handle holidays, special events, or unexpected incidents, resulting in low prediction accuracy. To address this, we propose a neural network prediction model that combines activity labels with historical time-series OD data, improving prediction accuracy during special events.Our model, constructed using the nn.Module in PyTorch, takes inputs such as time periods, entry and exit stations, passenger counts, workdays or holidays, anomaly event markers, activity types, and travel times between OD points. The output is the exit proportion at each station. Given that the OD proportion is a probability distribution, we use the cross-entropy loss function to enhance the model′s accuracy in predicting OD distributions for metro stations. For the learning rate algorithm, we switch from Adam to SGD and manually adjust the learning rate to avoid extreme values in the model output, significantly improving the model′s adaptability to high-dimensional data. Additionally, we propose predictive scheduling and enhanced service recommendations for the Taipei Metro, including preemptively adjusting train intervals, real-time updates of passenger flow information, and developing event cooperation strategies to boost overall operational efficiency and passenger satisfaction. Ultimately, we use ticket OD data to calculate inter-station volume. |
