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研究生： 陳信男
Shin-nan Chen
論文名稱： 私有停車位於不同情況條件之車輛指派適用方法之研究
A Comparative Study on the Approaches for Private Parking Space Assignments Different Scenarios
指導教授： 周碩彥
Shuo-Yan Chou
口試委員: 王孔政
Kung-Jeng Wang
喻奉天
Vincent F. Yu
學位類別： 碩士
Master
系所名稱： 管理學院 - 工業管理系
Department of Industrial Management
論文出版年： 2014
畢業學年度： 102
語文別： 英文
論文頁數： 78
中文關鍵詞： 私人停車位 、車輛指派 、貪婪演算法 、模擬 、類神經網路
外文關鍵詞： Private Parking Space, Vehicle Assignment, Greedy Algorithm, Simulation, Artificial Neural Network
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    • 隨著經濟發展，都市化程度快速以及交通設施建設的速度不及交通工具數量增長速度，因此交通阻塞、交通事故增加以及環境污染等問題日趨嚴重，其中停車問題即是造成交通堵塞的重要原因之一。停車問題中，停車位扮演著重要的角色。停車位大致分成公有及私有停車位兩種。然而，公有停車位數量固定而停車需求日益增加，因此若能將私有停車位有效利用，那將有助於紓解停車問題。智慧停車系統(Intelligent Parking System, IPS)是智慧運輸系統(Intelligent Transportation System, ITS)中具有發展性的一項功能，IPS可以提供完善停車場管理功能。
    有鑑於上述各項原因，本研究著重在探討私有停車場建構於智慧停車系統(IPS)的效益分析，效益指標包括使用率、停車費用以及停車位使用數量。將資料來源區分成確定性與隨機性兩種，分別應用模擬與類神經網路、排程理論與貪婪演算法進行研究。期望透過類神經網路的學習能力與演算法最佳化能力，提供未來評估發展私有停車位的參考依據。研究結果顯示，類神經網路具有良好的預測能力以及貪婪演算法有效改善預約排程的效率。

    Parking problems is one of main causes of traffic congestion. In parking problems, parking spaces play an important role since due to difficulties in increasing the number of them which heavily depends on constraints of budget and spaces. If the private parking spaces can be effectively used, the pressure of parking problems can be alleviated.
    Due to all reasons, this study focuses on discussion the performance of private parking lot under impacts of intelligent parking system. Indicators include the utilization efficiency, parking fees, and the total number of parking spaces be occupied. The data source can be divided into two kinds of deterministic and stochastic. Deterministic model employed scheduling theory and the greedy algorithm while stochastic model adopt simulation model and apply artificial neural network. The results show that the artificial neural network has good predictive ability and the improvement the efficiency is effective with application of greedy algorithm.

    致謝……I 摘要……II Abstract……III Table of Contents……IV List of Figures……VI List of Tables……VIII Chapter 1 Introduction……1 1.1 Background and Motivation……1 1.2 Objective and Contribution……3 1.3 Methodology……4 1.4 Organization of Thesis……4 Chapter 2 Literature Review……6 2.1 Intelligent Parking System……6 2.1.1 Parking Reservation and Price Strategies……7 2.1.2 Parking guidance and information system……8 2.1.3 Revenue management……9 2.2 Artificial Neural Network……9 2.3 Scheduling Problem……12 2.4 Greedy Algorithm……16 Chapter 3 Model and Algorithm……18 3.1 Problem Description……18 3.2 Problem Assumption……20 3.3 Stochastic Model……21 3.3.1 Simulation Model……21 3.3.2 Artificial Neural Network Model……24 3.3.3 Performance Evaluation……25 3.4 Deterministic Model……26 3.4.1 Mathematical Model……28 3.4.2 Reservation Process……29 3.4.3 Longest Processing Time Algorithm……31 3.4.4 Greedy Algorithm……32 Chapter 4 Evaluation Result……34 4.1 Pre-Data Generate……34 4.2 Stochastic Simulation Result……36 4.2.1 Parameter Setting……36 4.2.2 Result of ANN……38 4.3 Deterministic Model Result……50 Chapter 5 Conclusion and Future Research……56 5.1 Conclusion……56 5.2 Future Research……57 References……58 Appendix……61

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