臺灣博碩士論文加值系統

在都會區尋找路邊停車位是很多駕駛都有的經驗，因為路邊停車具有停車費便宜及離目的地較近的方便性等優點；根據外國文獻研究，找到停車位平均時間為7-15分鐘，在台灣，根據交通部統計處2021年10月公布調查報告指出，在六都駕駛找到停車位平均時間為7-14分鐘，與國外結果相當；根據此國內外的統計結果，尋找車位的時間最少需花費7分鐘，當駕駛人慢速行駛尋找停車位時，除影響交通流暢度外，汽油車排放的廢氣對空氣品質也有相當影響；因此面對此問題，如何將停車位的及時與預測資訊結合未來智慧城市的物聯網，讓駕駛人可以更快速的找到車位並提高車位使用效率是一件相當重要的課題。

全球正處於氣候變遷、人口集中城市化的轉型中，世界先進國家倡導循環經濟與淨零炭排。在環境、社會和治理（Environmental, Social and Governance, ESG）的永續發展意識的潮流中，本論文提出創新的路邊停車位預測系統，與時俱進，基於低功耗節能應用，設計微處理器整合毫米波雷達（Millimeter wave radar, mmWave radar）與4G LTE通訊的系統，此系統使用綠色能源，提供快速建置之優勢外，以毫米波雷達的量測技術結合長短期記憶神經網路（Long Short-Term Memory, LSTM），使用停車位歷史資料，運用人工智慧演算法，開發路邊停車位使用現況與空位預測，提供駕駛者更快速找到停車位。研究開發過程中，我們導入台灣宜蘭縣試辦之路邊智慧停車實際資料，經實驗結果顯示，各時段的停車位時間變化資料經過LSTM深度學習後，各時段均方誤差（Mean-Square Error）逐漸縮小，顯示研究方法在預測準確性具明顯提升；因此，本論文研究貢獻在於開發快速布建綠能停車位偵測系統，提供停車位即時與區間可用車位預測；研究成果貢獻可改善路邊停車問題所引發的交通擁塞與空氣汙染，具有重要的貢獻和價值，對未來智慧城市在智慧交通系統發展也提供相當重要參考。

Locating on-street parking in metropolitan areas is a universal challenge for drivers. The allure of roadside parking lies in its cost-efficiency and proximity to destinations. According to international studies, the average time spent searching for a parking spot ranges from 7 to 15 minutes. In Taiwan, as per the statistics released by the Ministry of Transportation in October 2021, this duration oscillates between 7 and 14 minutes in the major cities, mirroring global trends. This quest, lasting a minimum of seven minutes, not only hinders traffic fluidity but also exacerbates air pollution due to emissions from gasoline vehicles. Addressing this issue and integrating real-time and predictive parking information with the Internet of Things (IoT) for smart cities can enhance parking efficiency and speed, a pressing matter in modern urban planning.
As the world grapples with climate change and increasing urbanization, leading nations champion a circular economy and carbon neutrality. In the backdrop of rising awareness on Environmental, Social, and Governance (ESG) sustainability, this paper introduces a novel roadside parking prediction system. Aligned with contemporary needs, our low-power consumption design incorporates a microprocessor integrated with Millimeter Wave Radar (mm wave radar) and 4G LTE communication. Powered by green energy, this system not only offers rapid deployment advantages but also synergizes with the Long Short-Term Memory (LSTM) neural network. By utilizing historical parking data and artificial intelligence algorithms, it predicts on-street parking availability, assisting drivers in quickly locating vacant spots. Through our research, data from a pilot smart parking project in Yilan County, Taiwan, was incorporated. The experimental outcomes demonstrate that LSTM deep learning consistently reduces the Mean-Square Error (MSE) across various time slots, underlining the efficacy of our predictive model.
In essence, our contribution lies in developing a quick-to-deploy green energy parking detection system, which provides both real-time parking availability and predictive analyses for future vacancies. The outcomes address the consequential traffic congestion and environmental degradation induced by parking dilemmas. With significant implications and value, our findings offer pivotal insights for the evolution of intelligent transport systems in future smart cities.

摘要 .....................................i
Abstract.....................................ii
誌謝 .....................................iv
專利分析報告..................................v
目錄 .....................................vii
表目錄 .....................................ix
圖目錄 .....................................xi
第一章 緒論 .............................1
1.1 研究背景 .............................2
1.2 研究動機與目的........................3
1.3 研究方法.............................4
1.4 論文內容概述組織......................5
第二章 文獻探討.............................7
2.1 文獻探討相關介紹......................7
2.2 文獻回顧.............................9
2.2.1 感測器...............................9
2.2.2 停車位預測...........................12
2.2.3 儲能系統.............................15
2.3 結論.................................17
第三章 背景知識與相關研究....................18
3.1 毫米波雷達...........................18
3.1.1 毫米波雷達介紹.......................18
3.1.2 毫米波雷達距離量測....................21
3.1.3 毫米波雷達訊號處理....................24
3.2 神經網路種類介紹.....................28
3.3.1 感知器...............................29
3.3.2 深度神經網路.........................30
3.3.3 卷積神經網路.........................31
3.3.4 遞迴神經網路.........................32
3.3.5 梯度下降法...........................33
3.3.6 梯度消失.............................34
3.3.7 長短期記憶神經網路架構................35
3.3 開發平台與硬體介紹....................38
3.3.1 開發平台硬體架構......................38
3.3.2 平台韌體架構..........................39
3.3.3 系統軟體功能..........................39
3.4 可程式化充電管理技術...................40
3.5 智慧路邊停車收費機台與預付及預約機制.....42
第四章 系統設計與整合實驗.....................44
4.1 停車偵測系統設計與數據收集..............44
4.1.1 毫米波雷達偵測平台設計..................45
4.1.2 雷達波段與實驗數據收集設計..............47
4.1.3 毫米波雷達訊號處理與數據收集.............51
4.2 長短期記憶神經網路資料收集...............55
4.2.1 長短期神經網路架構與實驗方法.............55
4.2.2 長短期神經網模型訓練流程設計.............56
4.2.3 停車位狀態資料收集規劃..................58
4.2.4 停車位狀態資料標記設計..................59
4.3 可程式化充電管理系統設計與整合...........60
4.3.1 充電管理技術流程設計....................60
4.3.2 儲能系統設計與運作流程..................61
4.3.3 充電管理技術整合與功耗分析..............62
第五章 實作結果與分析.........................64
5.1 長短期記憶神經網路程式架構..............64
5.2 單ㄧ組模型預測結果.....................65
5.2.1 單ㄧ組模型訓練中數據可視覺化應用........65
5.2.2 單ㄧ組模型訓練誤差比較.................71
5.2.3 單一模型預測結果......................76
5.3 四組模型預測結果......................81
5.3.1 四組模型訓練流程與架構.................82
5.3.2 四組模型訓練中數據可視覺化應用..........84
5.3.3 四組模型訓練損失值比較.................89
5.3.4 四組模型預測結果......................93
5.4 分析與討論...........................98
第六章 結論與未來展望.......................99
6.1 結論................................99
6.2 未來研究方向........................100
參考文獻 ...................................101

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