時空資料預測模型-以臺北捷運為例
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2022
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Abstract
臺北捷運發展至今已成為大臺北地區重要的公共運輸工具之一，對各站進出旅客人數數據進行建模可以有效預測以便決策管理。然而，各站進出人數受到空間、時間以及環境等因素影響；重複時空自我回歸模型(Keunseo Kim, Vinnam Kim and Heeyoung Kim,2019)是針對航空旅客流量較好的預測模型。本研究利用臺北捷運2019年共計12個月119個車站為目標蒐集起訖站客流量，並以臺北各地區人口數作為影響因素，透過R程式語言編寫出一套演算法建立出模型。
The development of Taipei MRT has become one of the important public transportation tools in Taipei area. Modeling the number of passengers entering and leaving each station can effectively predict and facilitate decision-making. However, this data is affected by factors such as space, time, and environment; the replicated spatiotemporal autoregressive model (Keunseo Kim, Vinnam Kim and Heeyoung Kim, 2019) is a better prediction model for air passenger flow. This research uses the passenger OD flow data of 119 Taipei MRT stations each month in 2019 and the population of each area in Taipei as the influencing factor to build a model by R programming algorithms.
The development of Taipei MRT has become one of the important public transportation tools in Taipei area. Modeling the number of passengers entering and leaving each station can effectively predict and facilitate decision-making. However, this data is affected by factors such as space, time, and environment; the replicated spatiotemporal autoregressive model (Keunseo Kim, Vinnam Kim and Heeyoung Kim, 2019) is a better prediction model for air passenger flow. This research uses the passenger OD flow data of 119 Taipei MRT stations each month in 2019 and the population of each area in Taipei as the influencing factor to build a model by R programming algorithms.
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捷運, 起訖點流量, 重複時空自我回歸模型, MRT, OD flows, replicated spatiotemporal autoregressive model