應用手機信令預測捷運站間量之研究

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姓名

翁宇鴻(Yu-Hung Wong) 查詢紙本館藏

畢業系所

土木工程學系

論文名稱

應用手機信令預測捷運站間量之研究

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摘要(中)

為解決私人運具造成的交通壅塞與廢氣排放等問題，大眾捷運系統逐漸扮演著都市運輸的重要角色，因此持續提升捷運服務水準就成為營運管理單位努力的目標。其中站間量可提供重要的績效指標，但電子票證數據無法直接推估旅客之移動軌跡，限制其對站間量的預測與分析。因此本研究先利用手機信令資料追蹤旅客於捷運路網的移動軌跡，並於分叉轉運站與相同匯入站間計算其間多條區段之運量分配比例，將此總區段流量比例應用於分岔轉運站之進出旅客數預測值，即可估算未來各區段的站間量。
本研究採用三種模型預測捷運站進出旅客數：函數資料分析(FDA)、長短期記憶(LSTM)與閥控遞迴單元(GRU)。結合總區段流量比例與捷運站進出旅客數預測值，進一步提出捷運站間量的推估方法，實證結果貼近旅客實際的移動軌跡，精度高於現行之「建議路線」軌跡，這主要原因為旅客選擇路徑會受到不同因素影響，包含：路線長度、路線車站數、乘車時間、轉乘時間、擁擠度與彎繞度等。
就臺北車站之預測誤差而言，機器學習模型LSTM(4.58%)與GRU(4.60%)較統計模型FDA(7.04%)為佳，但需較長的訓練時間。針對已觀測區間長度與未觀測區間長度的探討，各模型無明顯趨勢，綜合來說，最佳預測組合仍需依不同模型與數據，根據經驗或測試後選定。最後，根據信令站間量之預測結果顯示，文湖線存在超載狀況，應彈性調派列車或增加列車容量，其餘路線的服務供過於求，則應調整列車服務計畫或採彈性編組的服務模式，以降低成本。

摘要(英)

Link flow of Mass rapid systems is an important performance indicators to manager. In this research, we use sighting data for tracking the trajectory of passengers in MRT network and calculating the segment proportionality of the transfer station. Then, we can combine the result of flow prediction and the segment proportionality to predict short-term link flow. In this research, we use functional data analysis (FDA), long short-term memory (LSTM) and gate recurrent unit (GRU) model to predict passenger flow. Combined Total segment proportionality and flow prediction, we propose an estimation method for link flow. The empirical results are close to real situation. The accuracy is higher than practical method which use “suggested route” trajectory. Because the route choice of passengers is affected by many factors such as travel time, transfer time, degree of crowding and degree of circuitousness of path. The results of passenger predict in Taipei Main Station show that the performance with machine learning method (LSTM:4.58%; GRU:4.60%) are better than FDA(7.04%). The result of link flow predict shows that Wenhu Line overloading situation occurs during peak hours on weekdays and weekends. The flexible schedule of the train should be assigned by manager. Other routes supply exceed demand. Manager should adjust the train service plan or use elastic way of combination train to lower the cost.

關鍵字(中)

★ 手機信令資料
★ 捷運站間量預測
★ 函數資料分析
★ 機器學習

關鍵字(英)

★ sighting data
★ prediction of MRT link flow
★ functional data analysis
★ machine learning

論文目次

摘要 i
Abstract ii
誌謝 iii
目錄 iv
圖目錄 vi
表目錄 vii
第一章、緒論 1
第二章、文獻回顧 5
2-1　　捷運站間量之推估 5
2-2　　交通變量之預測 6
2-3　　手機信令資料前處理 8
第三章、研究方法 9
3-1　　函數資料分析 9
3-1-1　歷史資料的k-center函數分群 9
3-1-2　新觀測資料之函數混合預測模型 10
3-2　　機器學習 12
3-2-1　長短期記憶 12
3-2-2　閥控遞迴單元 14
3-2-3　模型設定 15
第四章、手機信令資料前處理與OD區段流量比例 17
4-1　　原始手機信令資料與前處理 17
4-2　　站間信令量與OD區段流量比例 19
4-2-1　OD區段流量比例與建議路線之差異分析 19
第五章、模型訓練與驗證 28
5-1　　電子票證數據內容與模型評估指標 28
5-2　　臺北車站之FDA分群與驗證結果 29
5-2-1　k-center分群演算法之各群組型態 29
5-2-2　FDA之訓練與驗證結果 31
5-3　　臺北車站之機器學習驗證結果 32
5-3-1　機器學習之訓練與驗證結果 32
5-3-2　訓練資料筆數對機器學習的影響 36
5-4　　各模型之綜合比較 37
第六章、站間量預測與分析 40
6-1　　站間量預測正確性 40
6-2　　旅客站間量分析 42
6-2-1　旅客需求依時性分析 42
6-2-2　捷運班次服務容量分析 45
6-3　　研究限制 51
第七章、結論與建議 53
參考文獻 56
附錄A：臺北車站3/23各模型之預測量 62
附錄B：各模型之MAPE 66
附錄C：各時段各路線之站間量與乘載率 74

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指導教授

陳惠國(Huey-Kuo Chen)

審核日期

2022-9-27

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