基於CNN與LSTM機器學習模型之交通事件預測與分析：以桃園市為例

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姓名

黃郁凱(Yu-Kai Huang) 查詢紙本館藏

畢業系所

通訊工程學系

論文名稱

基於CNN與LSTM機器學習模型之交通事件預測與分析：以桃園市為例
(Traffics Event Forecast and Analysis Based on CNN and LSTM Machine Learning Models: A Case Study of Taoyuan City)

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摘要(中)

城市交通日漸繁忙，事故發生機率亦隨之上升，過去有許多研究應用機器學習演算法，預測未來交通事故熱區、車流量或平均車速，然而多數的研究著重於如何提出新穎的機器學習架構。本論文的研究是以桃園市部分行政區為實驗場域，於各行政區取道路較密集的區域，每區域約16 平方公里，利用經典機器學習模型：CNN 與LSTM 組合訓練模型，分析在何種CNN-LSTM 層數組合下，能夠以較低的訓練成本，得到準確率較高的模型組合。

摘要(英)

Urban traffic is getting busy, and the probability of accidents is also rising. In the past, many studies applied machine learning algorithms to predict hot spots of traffic accidents, traffic flow, or average speed. However, most of the research focused on how to propose novel machine learning architectures. The study in this thesis uses some regions of Taoyuan City as experimental fields. By taking dense road areas in these regions, each
area is about 16 square kilometers. This study uses the classic machine learning model: Convolution Neural Network(CNN) and Long Short-Term Memory(LSTM). In sensitivity to varied number of CNN-LSTM layers, this study examines the performance of higher accuracy and lower training cost.

關鍵字(中)

★ 機器學習演算法
★ 時空資料
★ 交通事件預警

關鍵字(英)

★ Machine Learning
★ Spatio-Temporal Data
★ Traffic Event Prediction

論文目次

1 簡介1
1.1 前言 1
1.2 研究動機 2
1.3 方法設計摘要 2
1.4 後續章節架構 3
2 研究背景與相關文獻探討 4
2.1 研究背景 4
2.2 時空資料 5
3 研究方法 6
3.1 卷積神經網路(CNN) 6
3.2 長短期記憶(LSTM) 7
3.3 損失函數 10
3.4 激勵函數 10
3.5 實驗系統架構 11
4 實作與結果分析 13
4.1 實驗環境 13
4.2 實驗設計 15
4.2.1 資料預處理 15
4.2.2 輸入矩陣與輸出矩陣 17
4.2.3 地圖資料取得與應用 18
4.3 實驗結果 20
4.3.1 測試資料準確率 20
4.3.2 GPU 記憶體佔用量 27
4.3.3 系統記憶體使用量 28
4.3.4 模型訓練時間 30
4.4 實驗結果總結 33
5 結論與未來研究34
參考文獻35

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指導教授

胡誌麟(Chih-Lin Hu)

審核日期

2020-8-18

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