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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 蔡欣穆(Hsin-Mu Tsai) | |
| dc.contributor.author | Hao-Ping Wu | en |
| dc.contributor.author | 吳浩平 | zh_TW |
| dc.date.accessioned | 2022-11-24T03:08:39Z | - |
| dc.date.available | 2021-11-03 | |
| dc.date.available | 2022-11-24T03:08:39Z | - |
| dc.date.copyright | 2021-11-03 | |
| dc.date.issued | 2021 | |
| dc.date.submitted | 2021-10-27 | |
| dc.identifier.citation | [1] SEC ’17: Proceedings of the Second ACM/IEEE Symposium on Edge Computing, New York, NY, USA, 2017. Association for Computing Machinery. [2] R. Arnott and E. Inci. An integrated model of downtown parking and traffic conges tion. Journal of Urban Economics, 60(3):418–442, 2006. [3] P. Basu and T. D. C. Little. Networked parking spaces: architecture and applications. In Proceedings IEEE 56th Vehicular Technology Conference, volume 2, pages 1153– 1157 vol.2, 2002. [4] M. Caliskan, A. Barthels, B. Scheuermann, and M. Mauve. Predicting parking lot occupancy in vehicular ad hoc networks. In 2007 IEEE 65th Vehicular Technology Conference VTC2007Spring, pages 277–281, 2007. [5] M. Caliskan, D. Graupner, and M. Mauve. Decentralized discovery of free park ing places. In Proceedings of the 3rd International Workshop on Vehicular Ad Hoc Networks, VANET ’06, page 30–39, New York, NY, USA, 2006. Association for Computing Machinery. [6] J. Cherian, J. Luo, H. Guo, S. Ho, and R. Wisbrun. Parkgauge: Gauging the occu pancy of parking garages with crowdsensed parking characteristics. In 2016 17th IEEE International Conference on Mobile Data Management (MDM), volume 1, pages 92–101, 2016. [7] J. Fu, Z. Chen, R. Sun, and B. Yang. Reservation based optimal parking lot rec ommendation model in internet of vehicle environment. China Communications, 11(10):38–48, 2014. [8] Y. Geng and C. G. Cassandras. New “smart parking'system based on resource al location and reservations. IEEE Transactions on Intelligent Transportation Systems, 14(3):1129–1139, 2013. [9] K. GopalAgrawal, S. Sadhani, R. Ahuja, S. Khandelwal, and S. Koul. Parking navi gation and payment system using ir sensors and rfid technology. International Journal of Computer Applications, 111:5–7, 02 2015. [10] W. Griggs, J. Y. Yu, F. Wirth, F. Häusler, and R. Shorten. On the design of campus parking systems with qos guarantees. 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In 2008 International Symposium on Collaborative Technologies and Systems, pages 48–57, May 2008. [17] R. Lu, X. Lin, H. Zhu, and X. Shen. Spark: A new vanetbased smart parking scheme for large parking lots. In IEEE INFOCOM 2009, pages 1413–1421, 2009. [18] D. Mackowski, Y. Bai, and Y. Ouyang. Parking space management via dynamic performancebased pricing. Transportation Research Procedia, 7:170 – 191, 2015. 21st International Symposium on Transportation and Traffic Theory Kobe, Japan, 57 August, 2015. [19] K. C. Mouskos, J. Tvantzis, D. Bernstein, and A. Sansil. Mathematical formu lation of a deterministic parking reservation system (prs) with fixed costs. In 2000 10th Mediterranean Electrotechnical Conference. Information Technology and Electrotechnology for the Mediterranean Countries. Proceedings. MeleCon 2000 (Cat. No.00CH37099), volume 2, pages 648–651 vol.2, 2000. [20] Z. S. Qian and R. Rajagopal. Optimal dynamic pricing for morning commute park ing. Transportmetrica A: Transport Science, 11(4):291–316, 2015. [21] T. Rajabioun and P. A. Ioannou. Onstreet and offstreet parking availability prediction using multivariate spatiotemporal models. IEEE Transactions on Intelligent Transportation Systems, 16(5):2913–2924, 2015. [22] V. W. s. Tang, Y. Zheng, and J. Cao. An intelligent car park management sys tem based on wireless sensor networks. In 2006 First International Symposium on Pervasive Computing and Applications, pages 65–70, 2006. [23] F. J. Villanueva, D. Villa, M. J. Santofimia, J. Barba, and J. C. López. Crowdsensing smart city parking monitoring. In 2015 IEEE 2nd World Forum on Internet of Things (WFIoT), pages 751–756, Dec 2015. [24] A. Wegener, M. Piorkowski, M. Raya, H. Hellbrück, S. Fischer, and J.P. Hubaux. Traci: An interface for coupling road traffic and network simulators. Proceedings of the 11th Communications and Networking Simulation Symposium, CNS’08, 04 2008. [25] Yanxu Zheng, S. Rajasegarar, and C. Leckie. Parking availability prediction for sensorenabled car parks in smart cities. In 2015 IEEE Tenth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP), pages 1–6, 2015. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/80529 | - |
| dc.description.abstract | "過去數十年，日益成長的都市人口帶給城市交通很大的挑戰。以停車為例，因為路邊車位的低成本與方便性，造成需求大增，而尋找車位的車輛在市區繞行造成交通壅塞，不但造成環境污染且浪費大量的時間及金錢，一套可以在都市環境中精準預測未來路邊車位的供需狀況系統成為大都市的迫切需要，例如台北。利用真實世界中的路邊車位歷史資訊，配合泊松過程模擬車輛進出行為，來預測未來停車格位供需情況，並利用SUMO模擬各項導引策略對於系統表現的影響。 本論文為實作都市路邊車位導引系統主要克服了以下三大挑戰。首先，將城市的路邊車位位置資訊整合並匯入車流模擬軟體，如此才能真實模擬並比較各導引策略的優劣。我們的模擬包含了1,557個路段，總共33,574個停車格位。再者，我們統計了2020年三月至2021年三月台北市所有路邊車位的車輛佔有資料並以泊松過程描述每個路段的車輛進出行為，計算出車輛駛入和駛離車位的頻率，並利用此資料預測未來車位的供需情況。最後，我們透過SUMO分析各策略對於整體使用者找尋車位的時間消長變化。在全部車輛遵循我們的導引下，與傳統貪婪的找尋車位方式相比，有超過百分之六十的車輛能減少找尋車位的總時間，其中有超過一半的車輛能減少超過十分鐘。因著我們系統的通用性，未來只要收集都市車位的歷史資料並將車位位置及都市路網匯入交通模擬軟體，便能對不同導引策略在不同都市進行評估，發展出適合該城市的都市路邊車位導引系統。" | zh_TW |
| dc.description.provenance | Made available in DSpace on 2022-11-24T03:08:39Z (GMT). No. of bitstreams: 1 U0001-2610202116185900.pdf: 3693270 bytes, checksum: 667653b0f337585dfa5265b6f3ec62d7 (MD5) Previous issue date: 2021 | en |
| dc.description.tableofcontents | Acknowledgements 3 摘要 5 Abstract 7 Contents 9 List of Figures 11 List of Tables 13 Chapter 1 Introduction 1 Chapter 2 Related Work 5 2.1 Intelligent Parking System 5 2.2 Parking Assignment Algorithm 6 Chapter 3 System Design 9 3.1 Design Overview 9 3.2 Prediction Model 11 3.2.1 Poisson Process 12 3.2.2 Poisson Distribution 13 3.3 On-Street Parking Guidance 15 3.3.1 Driving Cost 17 3.3.2 Walking Cost 17 3.3.3 Problem Formulation 18 Chapter 4 Implementation 21 4.1 Simulation Framework 21 4.2 Data Set 22 4.2.1 Data Description 23 4.2.2 Data Preprocessing 23 4.2.3 Hierarchical Clustering 25 Chapter 5 Evaluation 27 5.1 Approximate Methods Evaluation 27 5.1.1 Approximate Methods Compared to Near Optimal Solution 29 5.1.2 Computation Time Evaluation 30 5.2 Different Parameters Combinations Evaluation 31 5.2.1 Comparing to Greedy Approach 31 5.2.2 Penetration Ratio Evaluation 34 5.2.3 Different Database Update Periods 36 5.2.4 Influence of Weighting Parameter 38 Chapter 6 Conclusion 43 References 45 | |
| dc.language.iso | en | |
| dc.subject | 泊松過程 | zh_TW |
| dc.subject | 路邊停車 | zh_TW |
| dc.subject | 停車導引 | zh_TW |
| dc.subject | on-street parking | en |
| dc.subject | Poisson process | en |
| dc.subject | parking guidance | en |
| dc.title | 利用真實路邊停車資料預測空位實作路邊停車導引系統 | zh_TW |
| dc.title | Roadside Parking Guidance System based on Availability Prediction Model with Real-World Data | en |
| dc.date.schoolyear | 109-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 詹魁元(Hsin-Tsai Liu),林忠緯(Chih-Yang Tseng),陳柏華 | |
| dc.subject.keyword | 路邊停車,停車導引,泊松過程, | zh_TW |
| dc.subject.keyword | on-street parking,parking guidance,Poisson process, | en |
| dc.relation.page | 48 | |
| dc.identifier.doi | 10.6342/NTU202104246 | |
| dc.rights.note | 同意授權(限校園內公開) | |
| dc.date.accepted | 2021-10-28 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 資訊工程學研究所 | zh_TW |
| 顯示於系所單位： | 資訊工程學系 | |
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