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研究生: 楊弘智
Yang, Hongzhi
論文名稱: 使用公車票卡資料挖掘潛在的高需求公車路線
Discovering High Demanding Bus Routes Using Farecard Data
指導教授: 陳良弼
Chen, Arbee L.P.
口試委員: 李官陵
Lee, Guan-Ling
彭文志
Peng, Wen-Chih
學位類別: 碩士
Master
系所名稱: 電機資訊學院 - 資訊系統與應用研究所
Institute of Information Systems and Applications
論文出版年: 2018
畢業學年度: 106
語文別: 英文
論文頁數: 55
中文關鍵詞: 城市計算大數據公車線路優化人群移動模式
外文關鍵詞: urban computing, bus routing, big data, human mobility
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  • 一個有效且完善的公共交通系統是提高城市居民生活質量,促進城市可持性續發展的重要因素之一。而在台灣的絕大多數城市,公共交通的利用效率並不理想。本文利用公車票卡交易資料挖掘市民出行的實際需求,試圖檢測出擁有高出行需求但線路設計不合理的地區對,并期望以此為基礎改善公車線路設計,以達到進一步提高公共交通利用效率的目的。檢測結果包括1) 擁有高出行需求但線路設計不合理的地區對;2) 這些區域對的連接結構與相關性。我們可以將這些檢測結果與目前的城市規劃 (例如捷運線路) 進行比較,考察這些規劃是否能有效緩解目前存在的問題。本研究使用台中市從2016年5月1日至6月30日的真實公車票卡交易資料進行分析,並以台中市目前的城市規劃來評估實驗結果的有效性。


    An effective and perfect public transport system is one of the most important factors to improve the quality of urban residents’ life and to bring a sustainable development in urban areas. In this paper, we detect high demanding region pairs with inconvenient bus route design, such as taking circuitous routes or having too many stops, etc., to improve the utilization efficiency of public transportation services, according to people’s real demands. The detected results consist of 1) region pairs with significant bus route design problems and 2) the linking structure as well as the correlation among these region pairs. We can compare these results to some existing and future urban planning, such as MRT lines, and study whether these planning reduce the current problems. We conduct our method using the bus farecard transaction data from May 1st to June 30th in 2016 in Taichung City, and evaluate our results with the real urban planning of Taichung City.

    Acknowledgement i 摘要 ii Abstract iii Table of Contents iv List of Figures vi List of Tables viii 1. Introduction 1 2. Related Work 5 2.1 Urban Computing 5 2.2 Bus Network Design 6 3. Modeling City-wide Mobility Patterns 9 3.1 Regions Building 9 3.2 Building Region Matrix 15 4. Detecting High Demanding Region Pairs with Inconvenient Bus Route Design 23 4.1 Skyline Detection 23 4.2 Pattern Mining from Skylines 25 5.Datasets and Pre-processing 29 5.1 Datasets 29 5.2 Data Pre-processing 30 6. Evaluation 33 6.1 Evaluation on merge algorithm 33 6.2 Results of Detecting Flawed Region Pairs 44 7. Conclusion 51 Reference 53

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