臺灣博碩士論文加值系統

了解城市中人潮的流動與密度對於智慧城市的建設以及城市的規劃有著非常重要的作用。在許多城市，捷運在公共運輸中發揮著越來越重要的作用，因此本文將專注於捷運人潮流動以及捷運站點人潮密度的研究。預估捷運人潮流動與站點人潮密度傳統的做法是通過分析智能卡（如，悠遊卡）的資料。然而，智能卡只記錄了用戶上車與下車的時間與地點；用戶什麼時候在哪個換乘站換乘不同路線的捷運無法直接從智能卡的資料中獲知。如今，大部分人都有自己的手機，手機蜂窩數據記載了用戶手機連接的基站的信息。我們可以從手機運營商提供的用戶蜂窩數據推測用戶是否乘坐捷運，根據推測的結果預估捷運人潮。為了達到這一目的，我們綜合考慮基站的性質、空間與時間的因素提出一個高效、可拓展的方法來識別用戶搭乘捷運的旅程。基於識別的結果，我們進一步預估搭乘捷運的人潮流動以及人潮密度。我們利用台灣地區最大的手機運營商中華電信提供的用戶資料對我們的方法進行檢驗。實驗結果表明，本文所提出的方法能很好地適用于捷運旅途的識別以及捷運人潮的預估。在本文中，我們提供了一些案例分析來呈現我們方法在捷運人潮預估的應用中的實用性。

Understanding the crowd flow and crowd density is crucial for smart city and urban planning. In this paper, we focus on the study of Mass rapid transit (MRT) that is playing an increasingly important role in many cities. The traditional way to estimate the crowd density and the crowd flow is by using smart card data. However, we can only know the number of passengers entering or exiting the station from smart card data. When and where the passengers change their MRT lines still remain unknown. Nowadays, each user has his/her own mobile phones and from the cellular data of mobile phone service providers, it is possible to know the users' transportation mode and the fine-grained crowd flows. As such, given a set of cellular data, we aim to estimate the crowd flow of MRT passengers and crowd density of stations as well as routes. To achieve these goals, we firstly propose an efficient and scalable approach to detect MRT trips with a pre-defined reference system. We take the cell tower properties, spatial and temporal factors into consideration in our approach. Then based on the detection result, we estimate the crowd flow and crowd density by grouping and counting the MRT trips. Extensive experiments are conducted to evaluate the detection and estimation approaches on a real dataset from Chunghwa Telecom, which is the largest telecommunication company in Taiwan. The results confirmed that our approaches are suitable for MRT trips detection, crowd flow and crowd density estimation. Finally, we provide case studies to present some applications and demonstrate the usefulness of our approaches.

1 Introduction 1
2 Related Works 4
3 Dataset and Data Pre-process 6
4 MRT Trip Detection 8
4.1 Reference System Building . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
4.1.1 KNT Reference System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
4.1.2 DCT Reference System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
4.2 Tower-Station Matching . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
4.3 Time Interval Grouping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
4.4 Station Complement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
5 Crowd Flow and Crowd Density Estimation 14
6 Evaluation 16
6.1 Performance of the estimation approach . . . . . . . . . . . . . . . . . . . . . . 16
6.2 Performance of the detection approach . . . . . . . . . . . . . . . . . . . . . . 17
6.3 Efficiency and Scalability . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
7 Case Studies 22
7.1 Overview of the crowdedness . . . . . . . . . . . . . . . . . . . . . . . . . . . .22
7.2 Crowd density of a station . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
7.3 Crowd density of different lines . . . . . . . . . . . . . . . . . . . . . . . . . 23
7.4 Crowd flow of Origin-Destination Stations . . . . . . . . . . . . . . . . . . . . 24
8 Conclusion 26

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