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題名 基於排隊理論預測等候時間的停車場決策系統
The parking lot decision-making system by predicting waiting times based on queuing theory
作者 陳湘婷
Chen, Hsiang-Ting
貢獻者 蔡子傑
Tsai, Tzu-Chieh
陳湘婷
Chen, Hsiang-Ting
關鍵詞 停車場
預測等待時間
排隊理論
智慧城市
Parking lots
Predicted waiting time
Queueing theory
Smart cities
日期 2025
上傳時間 4-Aug-2025 15:09:53 (UTC+8)
摘要 隨著台灣城市化的迅速推進以及車輛數量的持續攀升，停車問題已成為許多城市普遍面臨的挑戰之一。繁忙的市中心經常出現停車位不足或排隊時間過長的情況，不僅影響駕駛的外出行程效率，還加劇了交通擁堵與環境污染問題。在此背景下，如何有效解決停車場供不應求、使用效率低下，並減少排隊等待時間成為一項亟需解決的重要議題。 本研究針對此問題，提出一種基於排隊時間預測的停車場決策系統，透過分析停車場的即時資料與歷史資料，結合排隊理論模型（如M/M/1模型）進行排隊長度的預測，可推估最後的預計等待時間預測值，為駕駛提供最適當的停車場決策建議，以縮短尋找停車位及排隊等待的時間，改善停車體驗並提升外出交通效率，同時優化停車場資源的分配與利用率。 為提升研究的實用性與精確性，本研究透過台灣政府開放資料（Open Data），選擇高使用率的停車場作為觀察對象，將觀察資料作為模擬的基礎，定義符合現實生活的參數，模擬停車場的使用場景與排隊情況，並將模擬結果與預測值進行比較，驗證系統模型的準確性與適用性，此過程不僅能有效評估系統的預測能力，往後還能洞察停車場管理與駕駛行為模式。 本研究不僅致力於緩解都市交通壓力，也期望能為智慧城市的建設提供實質性的參考建議，為未來的停車場管理與交通規劃提供科學的依據，並促進城市的可持續發展。
Rapid urbanization and a growing number of vehicles in Taiwan have made urban parking a significant challenge. Shortages and long queues in city centers reduce travel efficiency while increasing traffic congestion and pollution. Addressing inefficient parking and reducing wait times has become a critical issue. This study proposes a parking decision system that predicts queuing times. By analyzing real-time and historical data with queueing theory models (e.g., M/M/1), the system forecasts wait times to provide drivers with optimal parking recommendations. The goal is to shorten search and queuing times, enhance the parking experience, improve travel efficiency, and optimize the allocation of parking resources. To validate the model's accuracy and practicality, we use open data from the Taiwanese government for high-usage parking facilities to simulate realistic scenarios. Comparing simulation results with the model's predictions verifies its effectiveness and offers insights into parking management and driver behavior. Ultimately, this research aims to alleviate urban traffic pressure, offer tangible suggestions for smart city development, provide a scientific basis for future parking management, and promote sustainable urban growth.
參考文獻 [1] Hamidreza Tavafoghia, Kameshwar Poollaa,b, Pravin Varaiya, "A Queuing Approach to Parking Modeling, Verification, and Prediction", 2019. [2] Xiaofei Ye, Jinfen Wang, Tao Wang, Xingchen Yan, Qiming Ye, Jun Chen, ”Short-Term Prediction of Available Parking Space Based on Machine Learning Approaches”, 2020. [3] Danielle F. Morey, Giulia Pedrielli, Zelda B. Zabinsky, “A Hybrid Approach Combining Simulation and a Queueing Model for Optimizing a Biomanufacturing System” , pp. 1130-1138, 2025. [4] Pyke Tin , Thi Thi Zin , “A Markovian Game Theoretic Framework for Analysing a Queueing System with Multiple Servers”, 2024. [5] Akhil M Nair, Sreelatha K.S, P.V. Ushakumari, “Application of Queuing Theory to a Railway ticket window”, pp. 154-158, 2021. [6] Thi Thi Zin, Aung Si Thu Moe, Cho Nilar Phyo, Pyke Tin, “Fusion of Strategic Queueing Theory and AI for Smart City Telecommunication System”, pp. 653-657, 2024. [7] Bingxuan Li, Antonio Castellanos, Pengyi Shi, Amy Ward, “Combining Machine Learning and Queueing Theory for Data-Driven Incarceration-Diversion Program Management”, pp. 22920-22926, 2024. [8] Dipta Gomes, Rashidul Hasan Nabil, Kamruddin Nur, “Banking Queue Waiting Time Prediction based on Predicted Service Time using Support Vector Regression”, pp. 145-149, 2020. [9] S P Subhapriya, M. Thiagarajan, “M/M/1/K Loss and Delay Interdependent Queueing Model with Vacation and Controllable Arrival Rates”, pp. 487-493, 2024. [10] Wen Jia, Yu-lin Huang, Qun Zhao, Yi Qi, “Modeling taxi drivers’ decisions at airport based on queueing theory”, 2022. [11] MAI Nguyen Thi1, CUONG Duong Manh, “The Application of Queueing Theory in the Parking Lot: a Literature Review”, 2021. [12] Cuong Duong Manh, Mai Nguyen Thi, “The Queueing Model on the Parking Area:A Case Study at Hanoi University of Scienceand Technology”, 2023. [13] Bei Chen, Fabio Pinelli, Mathieu Sinn, Adi Botea, Francesco Calabrese, “Uncertainty in urban mobility: Predicting waiting times for shared bicycles and parking lots”, 2013. [14] Queuing theory：https://queue-it.com/blog/queuing-theory/ [15] Truncated normal distribution：https://en.wikipedia.org/wiki/Truncated_normal_distribution [16] Sliding windows：https://www.geeksforgeeks.org/dsa/window-sliding-technique/ [17] Open Data：https://data.gov.tw/dataset/128435
描述 碩士
國立政治大學
資訊科學系碩士在職專班
111971006
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0111971006
資料類型 thesis
dc.contributor.advisor 蔡子傑zh_TW
dc.contributor.advisor Tsai, Tzu-Chiehen_US
dc.contributor.author (Authors) 陳湘婷zh_TW
dc.contributor.author (Authors) Chen, Hsiang-Tingen_US
dc.creator (作者) 陳湘婷zh_TW
dc.creator (作者) Chen, Hsiang-Tingen_US
dc.date (日期) 2025en_US
dc.date.accessioned 4-Aug-2025 15:09:53 (UTC+8)-
dc.date.available 4-Aug-2025 15:09:53 (UTC+8)-
dc.date.issued (上傳時間) 4-Aug-2025 15:09:53 (UTC+8)-
dc.identifier (Other Identifiers) G0111971006en_US
dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/158707-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 資訊科學系碩士在職專班zh_TW
dc.description (描述) 111971006zh_TW
dc.description.abstract (摘要) 隨著台灣城市化的迅速推進以及車輛數量的持續攀升，停車問題已成為許多城市普遍面臨的挑戰之一。繁忙的市中心經常出現停車位不足或排隊時間過長的情況，不僅影響駕駛的外出行程效率，還加劇了交通擁堵與環境污染問題。在此背景下，如何有效解決停車場供不應求、使用效率低下，並減少排隊等待時間成為一項亟需解決的重要議題。 本研究針對此問題，提出一種基於排隊時間預測的停車場決策系統，透過分析停車場的即時資料與歷史資料，結合排隊理論模型（如M/M/1模型）進行排隊長度的預測，可推估最後的預計等待時間預測值，為駕駛提供最適當的停車場決策建議，以縮短尋找停車位及排隊等待的時間，改善停車體驗並提升外出交通效率，同時優化停車場資源的分配與利用率。 為提升研究的實用性與精確性，本研究透過台灣政府開放資料（Open Data），選擇高使用率的停車場作為觀察對象，將觀察資料作為模擬的基礎，定義符合現實生活的參數，模擬停車場的使用場景與排隊情況，並將模擬結果與預測值進行比較，驗證系統模型的準確性與適用性，此過程不僅能有效評估系統的預測能力，往後還能洞察停車場管理與駕駛行為模式。 本研究不僅致力於緩解都市交通壓力，也期望能為智慧城市的建設提供實質性的參考建議，為未來的停車場管理與交通規劃提供科學的依據，並促進城市的可持續發展。zh_TW
dc.description.abstract (摘要) Rapid urbanization and a growing number of vehicles in Taiwan have made urban parking a significant challenge. Shortages and long queues in city centers reduce travel efficiency while increasing traffic congestion and pollution. Addressing inefficient parking and reducing wait times has become a critical issue. This study proposes a parking decision system that predicts queuing times. By analyzing real-time and historical data with queueing theory models (e.g., M/M/1), the system forecasts wait times to provide drivers with optimal parking recommendations. The goal is to shorten search and queuing times, enhance the parking experience, improve travel efficiency, and optimize the allocation of parking resources. To validate the model's accuracy and practicality, we use open data from the Taiwanese government for high-usage parking facilities to simulate realistic scenarios. Comparing simulation results with the model's predictions verifies its effectiveness and offers insights into parking management and driver behavior. Ultimately, this research aims to alleviate urban traffic pressure, offer tangible suggestions for smart city development, provide a scientific basis for future parking management, and promote sustainable urban growth.en_US
dc.description.tableofcontents 第一章 緒論 1 第一節 研究動機 1 第二節 文獻探討 3 第二章 方法論 6 第一節 常態分布 6 第二節 排隊理論 9 第三節 滑動視窗 11 第四節 方法理論整合 13 第五節 資料集 15 一、 資料集準備 15 二、 模擬資料生成 21 三、 模擬過程 25 四、 排隊長度預估 30 第六節 模擬架構 34 一、 研究流程 34 二、 決策系統流程 35 第三章 實驗驗證 38 第一節 方法論決策應用實驗 38 第二節 文獻討論與比較 45 第四章 結果與討論 47 第五章 結論 50 參考文獻 53zh_TW
dc.format.extent 7924804 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0111971006en_US
dc.subject (關鍵詞) 停車場zh_TW
dc.subject (關鍵詞) 預測等待時間zh_TW
dc.subject (關鍵詞) 排隊理論zh_TW
dc.subject (關鍵詞) 智慧城市zh_TW
dc.subject (關鍵詞) Parking lotsen_US
dc.subject (關鍵詞) Predicted waiting timeen_US
dc.subject (關鍵詞) Queueing theoryen_US
dc.subject (關鍵詞) Smart citiesen_US
dc.title (題名) 基於排隊理論預測等候時間的停車場決策系統zh_TW
dc.title (題名) The parking lot decision-making system by predicting waiting times based on queuing theoryen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) [1] Hamidreza Tavafoghia, Kameshwar Poollaa,b, Pravin Varaiya, "A Queuing Approach to Parking Modeling, Verification, and Prediction", 2019. [2] Xiaofei Ye, Jinfen Wang, Tao Wang, Xingchen Yan, Qiming Ye, Jun Chen, ”Short-Term Prediction of Available Parking Space Based on Machine Learning Approaches”, 2020. [3] Danielle F. Morey, Giulia Pedrielli, Zelda B. Zabinsky, “A Hybrid Approach Combining Simulation and a Queueing Model for Optimizing a Biomanufacturing System” , pp. 1130-1138, 2025. [4] Pyke Tin , Thi Thi Zin , “A Markovian Game Theoretic Framework for Analysing a Queueing System with Multiple Servers”, 2024. [5] Akhil M Nair, Sreelatha K.S, P.V. Ushakumari, “Application of Queuing Theory to a Railway ticket window”, pp. 154-158, 2021. [6] Thi Thi Zin, Aung Si Thu Moe, Cho Nilar Phyo, Pyke Tin, “Fusion of Strategic Queueing Theory and AI for Smart City Telecommunication System”, pp. 653-657, 2024. [7] Bingxuan Li, Antonio Castellanos, Pengyi Shi, Amy Ward, “Combining Machine Learning and Queueing Theory for Data-Driven Incarceration-Diversion Program Management”, pp. 22920-22926, 2024. [8] Dipta Gomes, Rashidul Hasan Nabil, Kamruddin Nur, “Banking Queue Waiting Time Prediction based on Predicted Service Time using Support Vector Regression”, pp. 145-149, 2020. [9] S P Subhapriya, M. Thiagarajan, “M/M/1/K Loss and Delay Interdependent Queueing Model with Vacation and Controllable Arrival Rates”, pp. 487-493, 2024. [10] Wen Jia, Yu-lin Huang, Qun Zhao, Yi Qi, “Modeling taxi drivers’ decisions at airport based on queueing theory”, 2022. [11] MAI Nguyen Thi1, CUONG Duong Manh, “The Application of Queueing Theory in the Parking Lot: a Literature Review”, 2021. [12] Cuong Duong Manh, Mai Nguyen Thi, “The Queueing Model on the Parking Area:A Case Study at Hanoi University of Scienceand Technology”, 2023. [13] Bei Chen, Fabio Pinelli, Mathieu Sinn, Adi Botea, Francesco Calabrese, “Uncertainty in urban mobility: Predicting waiting times for shared bicycles and parking lots”, 2013. [14] Queuing theory：https://queue-it.com/blog/queuing-theory/ [15] Truncated normal distribution：https://en.wikipedia.org/wiki/Truncated_normal_distribution [16] Sliding windows：https://www.geeksforgeeks.org/dsa/window-sliding-technique/ [17] Open Data：https://data.gov.tw/dataset/128435zh_TW