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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 陳柏華 | zh_TW |
| dc.contributor.advisor | Albert Y. Chen | en |
| dc.contributor.author | 陳宜萱 | zh_TW |
| dc.contributor.author | Yi-Hsuan Chen | en |
| dc.date.accessioned | 2025-09-18T16:07:36Z | - |
| dc.date.available | 2025-09-19 | - |
| dc.date.copyright | 2025-09-18 | - |
| dc.date.issued | 2025 | - |
| dc.date.submitted | 2025-08-08 | - |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/99827 | - |
| dc.description.abstract | 與交通相關的空氣污染 (TRAP) 由氣體和顆粒物的混合物組成，包括 PM2.5、PM1.0、超細懸浮微粒 (UFP)、黑碳 (BC)、一氧化碳 (CO) 和二氧化氮 (NO2) 等，主要由車輛排放。本研究旨在分析影響住宅區道路附近空氣污染程度的因素，並提供住戶監測污染物的低成本方法。利用低成本攝影機和基於影像的車流辨識模型，我們實現了汽車、機車、公車超過 90%，和卡車約66%的交通量辨識準確度。此外，我們還利用監測站的 PM2.5 數據校準了微型感測器，確定將兩個微型感測器與氣象資料結合可產生最佳結果。即使感測器數據有限，模型仍保持高精度（MAPE 17-18%），證實了經濟高效的空氣品質監測的可行性。本研究表明，風速、風向、溫度和濕度顯著影響 TRAP 水平，污染物濃度在強風下降低，並隨氣象條件變化。我們也發現，汽車、機車、公車和卡車的交通量，對不少 TRAP汙染量起著至關重要的作用。而車輛在紅燈處等待也會加劇污染。這些發現強調了微型感測器在廣泛、經濟實惠的空氣品質監測方面的潛力，並為交通繁忙道路附近的居民提供了可行的建議，以減少接觸有害污染物。 | zh_TW |
| dc.description.abstract | Traffic-related air pollution (TRAP) consists of a mixture of gases and particulate matter, including PM2.5, PM1.0, ultrafine particles (UFP), black carbon (BC), carbon monoxide (CO), and nitrogen dioxide (NO2), primarily emitted from vehicles. This study aims to analyze the factors affecting air pollution levels near residential roads and provide low-cost methods for residents to monitor pollutants. By utilizing low-cost cameras and an image-based traffic recognition model, we achieved traffic volume recognition accuracies of over 90% for cars, motorcycles, and buses, and about 66% for trucks. Additionally, we calibrated low-cost PM sensors (LCPMS) using PM2.5 data from monitoring stations, determining that combining two LCPMS with meteorological data yields the best results. Even with limited sensor data, the models maintained good accuracy (MAPE 17-18%), confirming the feasibility of cost-effective air quality monitoring. Our findings indicate that wind speed, wind direction, temperature, and humidity significantly influence TRAP levels, with pollutant concentrations decreasing under strong winds and varying with meteorological conditions. We also found that traffic volumes of cars, motorcycles, buses, and trucks play crucial roles in TRAP levels, with vehicles waiting at red lights exacerbating pollution. These findings underscore the potential of LCPMS for widespread, affordable air quality monitoring and provide actionable insights for residents near high-traffic roads to reduce exposure to harmful pollutants. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2025-09-18T16:07:36Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2025-09-18T16:07:36Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 謝辭 i
中文摘要 ii ABSTRACT iii CONTENTS iv LIST OF FIGURES vii LIST OF TABLES xii Chapter 1 Introduction 1 1.1 Background 1 1.2 Objective 2 Chapter 2 Current Literature 4 2.1 Traffic Data Collection 4 2.2 Low-Cost Sensor 5 2.3 Traffic Related Air Pollution (TRAP) 6 2.4 Health impacts of TRAP 7 2.5 Summary 8 Chapter 3 Methodology 10 3.1 Traffic Counting Model 10 3.1.1 YOLO 10 3.1.2 Counting Model 12 3.2 Sensor Calibration 14 3.2.1 PM sensor 15 3.2.2 LCPMS Calibration 17 3.3 Factors Contribution Analysis 18 3.3.1 Multiple linear regression (MLR) 19 3.3.2 Machine Learning Model and SHAP 20 Chapter 4 Results 21 4.1 Study Introduction 21 4.1.1 Location 21 4.1.2 Camera 22 4.1.3 Monitoring Station 23 4.1.4 LCPMS 25 4.2 Traffic Counting 26 4.2.1 Detect Vehicles 27 4.2.2 Accuracy 28 4.2.3 Traffic Data Collection Period 29 4.3 LCPMSs Calibration 32 4.3.1 PM2.5 32 4.3.2 Comparison 38 4.4 Data Analysis 40 4.4.1 Analysis Each Pollutant 41 4.4.2 Comparison 78 Chapter 5 Conclusions & Future Work 119 5.1 Conclusions 119 5.1.1 Image-based Vehicle Counting Model 119 5.1.2 Low-Cost Sensor Calibration 119 5.1.3 Analysis TRAP Contribution by Factors 120 5.2 Future Work 121 References 123 | - |
| dc.language.iso | en | - |
| dc.subject | 機器學習 | zh_TW |
| dc.subject | 交通相關空氣汙染(TRAP) | zh_TW |
| dc.subject | 臨路住宅 | zh_TW |
| dc.subject | 車流影像辨識 | zh_TW |
| dc.subject | 低成本感測器 | zh_TW |
| dc.subject | LCPMS | en |
| dc.subject | image-based traffic recognition model | en |
| dc.subject | machine learning | en |
| dc.subject | residential near road | en |
| dc.subject | TRAP | en |
| dc.title | 基於低成本懸浮微粒感測器與影像辨識車流資料之交通相關空氣汙染物分析 | zh_TW |
| dc.title | Traffic-Related Air Pollution Analysis through LCPMS and Image-Based Data Collection | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 113-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 朱致遠;周建成 | zh_TW |
| dc.contributor.oralexamcommittee | James C. CHU;Chien-Cheng Chou | en |
| dc.subject.keyword | 交通相關空氣汙染(TRAP),機器學習,低成本感測器,車流影像辨識,臨路住宅, | zh_TW |
| dc.subject.keyword | TRAP,machine learning,LCPMS,image-based traffic recognition model,residential near road, | en |
| dc.relation.page | 126 | - |
| dc.identifier.doi | 10.6342/NTU202503277 | - |
| dc.rights.note | 同意授權(限校園內公開) | - |
| dc.date.accepted | 2025-08-12 | - |
| dc.contributor.author-college | 工學院 | - |
| dc.contributor.author-dept | 土木工程學系 | - |
| dc.date.embargo-lift | 2027-08-10 | - |
| 顯示於系所單位： | 土木工程學系 | |
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