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研究生:謝萬興
研究生(外文):Wan-Hsing Hsieh
論文名稱:運用悠遊卡巨量資料分析公車乘客行為
論文名稱(外文):Analysis of Bus Passengers’ Travel Behavior based on Easycard Big Data
指導教授:張學孔張學孔引用關係
口試委員:林祥生黃國平
口試日期:2015-07-15
學位類別:碩士
校院名稱:國立臺灣大學
系所名稱:土木工程學研究所
學門:工程學門
學類:土木工程學類
論文種類:學術論文
論文出版年:2015
畢業學年度:103
語文別:中文
論文頁數:125
中文關鍵詞:智慧卡公共運輸使用者行為時空變異性巨量資料
外文關鍵詞:SmartcardPublic TransportTravel BehaviorSpatial-Temporal HeterogeneityBig Data
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  • 被引用被引用:7
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近年來,巨量資料分析應用在交通領域對於政府、營運者、乘客之效益逐漸獲得重視,其中智慧卡資料具備可儲存大量個人旅行資料、由卡號連接到使用者以及比現存運輸資料來源可以獲得更長期連續旅次資料的特性,具備相當高的商業價值。悠遊卡自2000年發行至今,累積發卡量已突破5,000萬張,目前平均每日應用於各領域之總交易筆數合計超過550萬筆。本研究係以悠遊卡刷卡資料輔以站點刷卡人數統計,由2014年11月1日至11月30日共4,973,841筆交易資料中,針對南京東西路廊公車群組共20路線進行分析,了解公車使用者之行為及特性,並探討不同族群在時間面與空間面的變異情形。
時間面分析結果顯示平日比起假日具有較明顯的尖峰,學生族群在平日下午8點至10點會出現另一使用尖峰。敬老族群在平日與假日的使用規律性十分相似,與全部票種、一般及學生族群不同。此外,捷運松山線通車後造成南京路廊整體路線日運量在平日下降14.65%,假日下降8.97%,對於一般以及學生族群衝擊較大,而其餘票種在週日的運量反而有所提升。在空間面分析,研究結果呈現不同票種在上、下午尖峰的站點選擇所不同,以及通車前後各站點搭乘人數的變化。本研究另以關聯法則的Apriori演算法探勘資料欄位間關係,發現各路線間在通車前、通車後與不同票種間捷運轉乘比例的差異,由通車前的11.47%下滑到通車後9.74%。本研究選取2014年11月第二週之平常日資料進行通勤旅次分析,建立出對應卡號在第一筆與最後一筆交易紀錄的使用時間及路線選擇矩陣,研究結果指出上午8至9點與下午6至7點為使用頻率最高的組合,而選擇搭乘同一條路線的比例54.96%,兩者的時間間隔,可呈現出不同族群的搭乘特性與機動力。
本研究建立出一套從資料前置處理、時間面分析、空間面分析、資料關聯性分析與通勤旅次分析之流程,對於任何使用智慧卡搭乘之公車路線皆可應用,具可應用性及可移轉性,可供其他城市參考。主管機關在新政策之實施、新路線之引進以及接駁路線的協調等議題時,可檢視各族群的使用特性、路線載客績效及變化輔助決策的進行,業者亦可據以進行營運策略的調整,提升整體服務品質。


In recent years, Big Data has been applied to transportation field and obtained benefits for governments, operators and passengers. Smartcard data with characteristics of plenty of personal travel data connected to cardholders as well as long-term continuous journey information, it has been considered having high commercial value. Easycard has issued 50 million cards since 2000, while the daily transactions were more than 5 million in 2014. The study aims to understand the behavior and characteristics of bus passengers, to explore spatial-temporal variability among different user groups. The study selected 20 bus routes in Nanjing Corridor for case study in which data from nearly 5 million transaction records in November 2014 were collected and analyzed.
Temporal analysis indicated that there had two obvious peaks on weekdays compared to holidays while student group had a third peak from 8PM to 10PM on weekdays. For all card types, adult and student groups had less regularity between weekdays and holidays, while elderly one remained high. It is also shown opening MRT Songshan Line has caused bus passengers in Nanjing corridor fell 14.65 % on weekdays and 8.97 % on holidays, especially for adult and student card types, while the rest card types (Concessionaire, Senior, Charity and Escort) yet increased on Sundays after MRT operation. The results of spatial analysis showed stop selection varies from different card types on morning and afternoon peaks. On associate rule analysis side, Apriori algorithm was applied to conduct data mining on relationships among data fields. It is found that MRT-Bus interchange proportion changed before and after operation of MRT Songshan Line among different card types and corridor bus routes, the entire routes fell from 11.47% to 9.47%.
The study selected over 1 million transaction records from November 10 to 14, 2014 to establish commuting time and route selection matrix which corresponded to the first and the last transaction records by matching unique card ID and sequence number of each card. The results showed that 8-9AM and 6-7PM was the highest frequency combination of all and there were 54.96% passengers choosing the same route on their first and the last transaction records. The duration between the two records could also showed the transit characteristics and mobility patterns among different card type users. Overall, the travel behaviour of the all categories follows a certain degree of temporal and spatial universality but also displays unique patterns within their own specialties.
The study proposed a systematic process from data pre-processing, spatial-temporal pattern analysis, association rule analysis to commuter journeys analysis. It has shown that the proposed methodology has high applicability and transferability for any bus routes with smart card as payment media. The process can help of evaluating impact of the implementation of the new policy, the introduction of new routes and coordination of feeder bus routes. It can also provide information of travel characteristics of each focused groups and changes on operating performance so that transport authorities on decision-making, operator efficiency and service quality could be all enhanced.


誌謝 II
摘要 IV
Abstract V
目錄 VII
圖目錄 IX
表目錄 XI
第一章 緒論 1
1.1 研究背景與動機 1
1.2 研究目的 3
1.3 研究範圍與對象 3
1.4 研究流程 4
第二章 文獻回顧與評析 6
2.1 悠遊卡發展歷程與營運現況 6
2.1.1 悠遊卡發展歷程 6
2.1.2 悠遊卡發行及交易概況 9
2.1.3 悠遊卡公司系統架構 11
2.2 智慧卡之特性與應用 11
2.2.1 智慧卡之優點與缺點 11
2.2.2 運輸領域應用 15
2.2.3 商業領域應用 19
2.3 智慧卡產出資料之相關研究 21
2.3.1 國內相關研究 21
2.3.2 國外相關研究 24
2.4 悠遊卡資料庫欄位介紹 31
2.5 文獻評析 36
第三章 研究方法 38
3.1 研究架構 38
3.2 研究方法 40
3.2.1 關聯法則之定義及概念 40
3.2.2 Apriori演算法 42
第四章 南京東路智慧卡巨量資料分析 44
4.1 資料蒐集與處理 44
4.1.1 選取路線之特性說明 44
4.1.2 資料欄位說明及數據預處理 50
4.1.3 資料呈現 53
4.2 時間面分析 55
4.3 空間面分析 62
4.4 關聯性分析 74
4.5 通勤旅次分析 79
第五章 結論與建議 90
5.1 結論 90
5.2 建議 93
參考文獻 95
附錄一 SQL程式碼 101
附錄二 本研究之資料庫基本資料一覽 116
附錄三 通車前後運量變化(星期別) 117
附錄四 通車前後運量變化(票種別) 118
附錄五 松山線通車前後各票種運量變化 119
附錄六 一週內時間變異性 120
附錄七 以關聯法則探勘各路線轉乘比例 122
附錄八 平常日公車使用者交易時間分佈明細 123
附錄九 不同票種於第一筆與最末筆交易之時間間隔分佈明細1 124
附錄十 不同票種於第一筆與最末筆交易之時間間隔分佈明細2 125


1.Agard, B., Morency, C. and Trépanier, M., "Mining Public Transport User Behaviour from Smart Card Data", Presented in the 12th IFAC Symposium on Information Control Problems in Manufacturing-INCOM, 2006, pp.17-19.
2.Bagchi, M. and White, P., "What Role for Smart Card Data from Bus Systems?", Municipal Engineer, Vol. 157, No. 1, 2004, pp.39-46.
3.Bagchi, M. and White, P., "The Potential of Public Transport Smart Card Data", Transport Policy, Vol. 12, No. 5, 2005, pp.464-474.
4.Chen, J. and Yang, D.-Y., "Identifying Boarding Stops of Bus Passengers with Smart Cards Based on Intelligent Dispatching Data", Journal of Transportation Systems Engineering and Information Technology, Vol. 1, 2013, pp.76-80.
5.El Mahrsi, M. K., Etienne, C., Johanna, B. and Oukhellou, L., "Understanding Passenger Patterns in Public Transit Through Smart Card and Socioeconomic Data: A Case Study in Rennes, France", Presented in the ACM SIGKDD Workshop on Urban Computing, 2014, pp.1-9.
6.Eom, J. K. and Sung, M. J., "Analysis of Travel Patterns of the Elderly Using Transit Smart Card Data", Presented in the Transportation Research Board 90th Annual Meeting, No. 11-2357, 2011.
7.Fuse, T., Makimura, K. and Nakamura, T., "Observation of Travel Behavior by IC Card Data and Application to Transportation Planning", Presented in the Special Joint Symposium of ISPRS Commission IV and AutoCarto, Vol. 2010, 2010, pp.1-5.
8.Hong, S.-P., Min, Y.-H., Park, M.-J., Kim, K. M. and Oh, S. M., "Precise Estimation of Connections of Metro Passengers from Smart Card Data", Transportation, 2015, pp.1-21.
9.Huang, X. and Tan, J., "Understanding Spatio-Temporal Mobility Patterns for Seniors, Child/Student and Adult Using Smart Card Data", ISPRS-International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. 1, 2014, pp.167-172.
10.Kieu, L.-M., Bhaskar, A. and Chung, E., "A Modified Density-Based Scanning Algorithm with Noise for Spatial Travel Pattern Analysis from Smart Card AFC Data", Transportation Research Part C: Emerging Technologies, 2015, In Press.
11.Kurauchi, F., Schmöcker, J.-D., Shimamoto, H. and Hassan, S. M., "Variability of Commuters’ Bus Line Choice: An Analysis of Oyster Card Data", Public Transport, Vol. 6, No. 1, 2014, pp.21-34.
12.Kusakabe, T. and Asakura, Y., "Behavioural Data Mining of Transit Smart Card Data: A Data Fusion Approach", Transportation Research Part C: Emerging Technologies, Vol. 46, 2014, pp.179-191.
13.Lathia, N. and Capra, L., "How Smart is Your Smart Card? Measuring Travel Behaviours, Perceptions and Incentives", Proceedings of the 13th International Conference on Ubiquitous Computing, 2011, pp.291-300.
14.Lee, I., Oh, S.-M. and Min, J. H., "Prospect of Technology for Public Transit Planning Using Smart Card Data", Presented in the 9th World Congress on Railway Research, 2011, pp.1-11.
15.Lee, S., Hickman, M. and Tong, D., "Development of a Temporal and Spatial Linkage Between Transit Demand and Land Use Patterns", Journal of Transport and Land Use, Vol. 6, No. 2, 2013, pp.33-46.
16.Lee, S. and Hickman, M., "Travel Pattern Analysis Using Smart Card Data of Regular Users", Proceedings of the 90th Annual Meeting of the Transportation Research Board, No. 11-4258, 2011.
17.Long, Y. and Thill, J.-C., "Combining Smart Card Data and Household Travel Survey to Analyze Jobs-Housing Relationships in Beijing", Computers, Environment and Urban Systems, 2015, In Press.
18.Long, Y., Zhang, Y. and Cui, C., "Identifying Commuting Pattern of Beijing Using Bus Smart Card Data", Acta Geologica Sinica, Vol. 67, No. 10, 2012, pp.1339-1352.
19.Ma, X.-L., Wang, Y.-H., Chen, F. and Liu, J.-F., "Transit Smart Card Data Mining for Passenger Origin Information Extraction", Journal of Zhejiang University Science C, Vol. 13, No. 10, 2012, pp.750-760.
20.Ma, X.-L., Wu, Y.-J., Wang, Y.-H., Chen, F. and Liu, J.-F., "Mining Smart Card Data for Transit Riders’ Travel Patterns", Transportation Research Part C: Emerging Technologies, Vol. 36, 2013, pp.1-12.
21.Morency, C., Trépanier, M. and Agard, B., "Analysing the Variability of Transit Users Behaviour with Smart Card Data", Presented in the 2006 Intelligent Transportation Systems Conference IEEE, 2006, pp.44-49.
22.Morency, C., Trépanier, M. and Agard, B., "Measuring Transit Use Variability with Smart Card Data", Transport Policy, Vol. 14, No. 3, 2007, pp.193-203.
23.Munizaga, M., Devillaine, F., Navarrete, C. and Silva, D., "Validating Travel Behavior Estimated from Smart Card Data", Transportation Research Part C: Emerging Technologies, Vol. 44, 2014, pp.70-79.
24.Munizaga, M. A. and Palma, C., "Estimation of a Disaggregate Multimodal Public Transport Origin–Destination Matrix from Passive Smart Card Data from Santiago, Chile", Transportation Research Part C: Emerging Technologies, Vol. 24, 2012, pp.9-18.
25.Nasiboglu, E., Kuvvetli, U., Ozkilcik, M. and Eliiyi, U., "Origin-Destination Matrix Generation Using Smart Card Data: A Case Study for Izmir", Presented in the 2012 IV International Conference, 2012, pp.1-4.
26.Nishiuchi, H., King, J. and Todoroki, T., "Spatial-Temporal Daily Frequent Trip Pattern of Public Transport Passengers Using Smart Card Data", International Journal of Intelligent Transportation Systems Research, Vol. 11, No. 1, 2013, pp.1-10.
27.Park, J., Kim, D.-J. and Lim, Y., "Use of Smart Card Data to Define Public Transit Use in Seoul, South Korea", Transportation Research Record: Journal of the Transportation Research Board, 2008, pp.3-9.
28.Pelletier, M.-P., Trépanier, M. and Morency, C., "Smart Card Data Use in Public Transit: A Literature Review", Transportation Research Part C: Emerging Technologies, Vol. 19, 2011, pp.557-568.
29.Robinson, S., Narayanan, B., Toh, N. and Pereira, F., "Methods for Pre-Processing Smart Card Data to Improve Data Quality", Transportation Research Part C: Emerging Technologies, Vol. 49, 2014, pp.43-58.
30.Seaborn, C., Attanucci, J. and Wilson, N. H., "Analyzing Multi-modal Public Transport Journeys in London with Smart Card Fare Payment Data", Transportation Research Record: Journal of the Transportation Research Board, Vol. 2121, No. 1, 2009, pp.55-62.
31.Shi, X. and Lin, H., "The Analysis of Bus Commuters’ Travel Characteristics Using Smart Card Data: the Case of Shenzhen, China", Presented in the Transportation Research Board 93rd Annual Meeting, No.14-2571, 2014.
32.Sun, L., Lee, D.-H., Erath, A. and Huang, X., "Using Smart Card Data to Extract Passenger''s Spatio-Temporal Density and Train''s Trajectory of MRT System", Proceedings of the ACM SIGKDD International Workshop on Urban Computing, 2012, pp.142-148.
33.Tao, S., Corcoran, J., Mateo-Babiano, I. and Rohde, D., "Exploring Bus Rapid Transit Passenger Travel Behaviour Using Big Data", Applied Geography, Vol. 53, 2014, pp.90-104.
34.Tao, S., Rohde, D. and Corcoran, J., "Examining the Spatial–Temporal Dynamics of Bus Passenger Travel Behaviour Using Smart Card Data and the Flow-comap", Journal of Transport Geography, Vol. 41, 2014, pp.21-36.
35.Trépanier, M. and Morency, C., "Assessing Transit Loyalty with Smart Card Data", Presented in the 12th World Conference on Transport Research, 2010, pp.11-15.
36.Trépanier, M., Morency, C. and Agard, B., "Calculation of Transit Performance Measures Using Smart Card Data", Journal of Public Transportation, Vol. 12, No. 1, 2009, pp.79-96.
37.Yuan, N. J., Wang, Y., Zhang, F., Xie, X. and Sun, G., "Reconstructing Individual Mobility from Smart Card Transactions: A Space Alignment Approach", Presented in the 2013 IEEE 13th International Conference on Data Mining (ICDM), 2013, pp.877-886.
38.Zhang, D., Zhang, X. and Wang, J., "Commuter Travel Identification Based on Bus IC Data", Procedia-Social and Behavioral Sciences, Vol. 96, 2013, pp.1547-1555.
39.Zhong, C., Manley, E., Arisona, S. M., Batty, M. and Schmitt, G., "Measuring Variability of Mobility Patterns from Multiday Smart Card Data", Journal of Computational Science, Vol. 9, 2015, pp.125-130.
40.丁筱珊,「消費者使用多功能智慧卡動機之區隔研究」,國立成功大學電信管理研究所碩士論文,2008年。
41.朱正忠,「悠遊卡真的方便嗎?」,國立中央大學資訊管理學系碩士論文,2011年。
42.吳沛樵,「非接觸式智慧卡對政府效益之分析」,國立臺灣大學土木工程學研究所碩士論文,2007年。
43.吳珮華,「以地理資訊系統結合資料探勘方法從事 ATM 設點分析」,國立政治大學資訊科學研究所碩士論文,2009年。
44.李志仁,「探討悠遊卡資料擴充對捷運廣告客戶行銷效益影響之研究」,國立臺灣大學管理學院碩士在職專班資訊管理組碩士論文,2010年。
45.林至晟,「台中市公車捷運系統接駁公車最佳班距分析」,逢甲大學運輸科技與管理學系碩士在職專班碩士論文,2012年。
46.林祥生、陳秋廷,「運用智能卡數據挖掘運輸產業情報之初探」,第十五屆海峽兩岸都市交通學術研討會論文集,中國公路學會,2007年。
47.林祥生、邱詩淳、劉益豪,「應用悠遊卡資料挖掘公車乘客之需求特性」,中華民國運輸學會第20屆學術論文研討會論文集,2005年,頁367-387。
48.邱詩淳,「運用悠遊卡及資料探勘求解公車營運改善方案」,中華大學運輸科技與物流管理學系碩士論文,2006年。
49.張恭碩,「非接觸式智慧卡於停車收費之效益分析」,國立臺灣大學土木工程學研究所碩士論文,2007年。
50.曾憲雄、蔡秀滿、蘇東興、曾秋蓉、王慶堯,資料探勘,旗標出版股份有限公司,2005年。
51.廖宜靖,「台鐵運用智慧卡票證系統之策略與效益評估」,國立臺灣大學土木工程學研究所碩士論文,2008年。
52.羅惟元,「以悠遊卡交易資料探索公車路線之旅客起迄」,淡江大學運輸管理研究所碩士論文,2008年。


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