大眾交通運輸在偏鄉地區無論是涵蓋率或是發車頻率都比不上都市區域，除此之外，站與站之間的距離也相當的遙遠。本篇研究主要著重在於提升大眾交通運輸的乘客滿意度並且同時間減少公路客運公司的營運成本。
在本篇研究，首先我們使用鼎東客運2016年的電子票證資料進行研究分析，除了研究乘客的搭乘行為以外，也利用k-平均演算法和聚類分析算法進行時間以及空間的分析。而本篇研究將分群的做法分成兩個部分。第一部分利用k-平均演算法找出學生乘客於一天當中搭乘率最高的不同時間區間，讓我們能夠輕易地找出學生乘客搭乘的尖峰時段，第二部分則利用平均演算法和聚類分析算法找出新的區間客運服務路線。最後的結果顯示，兩種不同的方式分別可以減少公路客運公司在早上尖峰時段45%以及35%的成本，同時間又能夠滿足超過80%以上的乘客需求。

Public transportation routes coverage rate and frequency of service in rural area are usually much lower than those in urban area; in addition to these problems, distances between service bus stops are often far away in rural area. This study aims not only to improve passenger satisfaction but also to reduce bus company operation costs.

In this study, we first analyze the passengers’ travel pattern through the smart card data within the studied area from Diingdong bus corporation smart card data in 2016. A data mining technique with K-means algorithm and Density-based spatial clustering of applications with noise (DBSCAN) are applied to determine the appropriate number of clusters of bus stops. This study would run two step cluster. First step would use K-means algorithm to cluster different time zone about student passenger behavior. It would help to know the peak-hour in a day. Step two would use K-means algorithm and DBSCAN to develop the new short-tern bus route service. The final results show that two different methods can reduce operation costs about 82% and 79%, while satisfying over 80% passengers need.

致謝 i
中文摘要 ii
Abstract i
Contents ii
List of Tables iv
List of Figures v
Chapter 1 Introduction 1
1.1 Background 1
1.2 Motivation 2
1.3 Research Purpose 3
Chapter 2 Literature Review 6
2.1 Data mining in smart card 6
2.2 Smart card based research 8
2.2.1 Smart card fare system 8
2.2.2 Different smart card based research 9
Chapter 3 Model Formulation 15
3.1 Data source 15
3.2 Data Mining tools 16
3.2.1 K-means 16
3.2.2 Density-based spatial clustering of applications with noise 17
Chapter 4 Research Results 19
4.1 Temporal based research results 23
4.1.1 Routes temporal return service research results 23
4.1.2 Routes temporal departure service research results 29
4.2 Spatial based research results 35
4.2.1 Routes spatial return service results 35
4.2.2 Routes spatial departure service results 39
4.3 K-means algorithm results 41
4.4 DBSCAN algorithm results 44
4.5 Determine the two short-tern bus route service 46
Chapter 5 Conclusion and Suggestions 49
5.1 Conclusions 49
5.2 Management implications 49
5.3 Research limitations 50
5.4 Further study suggestions 50
Reference 52
Website 55
Appendix 56

English part

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Website

Wikipedia
https://zh.wikipedia.org/wiki/DBSCAN

Taipei City Public Transportation Office website
http://www.pto.gov.taipei/ct.asp?xItem=1089010&ctNode=12599&mp=117041

Ministry of Transportation and Communications R.O.C.
https://www.motc.gov.tw/