Multi-headed CNN on Short-term Traffic Flow Forecasting in Taoyuan Urban Area

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黃鐙毅(Tens-I Huang) 查詢紙本館藏

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資訊工程學系

論文名稱

(Multi-headed CNN on Short-term Traffic Flow Forecasting in Taoyuan Urban Area)

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摘要(中)

隨著城鄉交通流量的不斷增加，交通信息在智慧型運輸系統中發揮著重要作用。在過去幾年中，許多不同的方法已用於短期交通流量預測。城市交通流量預測有助於為交通壅堵提供早期預警，並作為旅行時間預測的特徵。
本文提出了一種交通流預測系統，用於預測和視覺化桃園市區的小時交通流量。研究使用了從安裝在交叉路口的RFID 車流檢測器收集而來的整年交通數據。為了解決由定期維護或設備故障引起的數據缺失問題，三種不同的缺漏值填補技術被用來清除未校正的數據。兩個基於深度學習的交通流量預測模型，即遞歸神經網路（RNN）和卷積神經網路（CNN），用於預測短期交通流量。最後，設置了一個RShiny在線監平台來演示我們的結果。為了評估所提出的模型的性能，採用了幾個評估指標來驗證我們的模型。實驗結果表明，CNN 模型的準確率在所有模型中表現最佳。

摘要(英)

With the steady increasing of both rural and urban traffic flow, traffic information plays an important role in the Intelligent Transport System. Over the last few years, many different methodologies have been used for short-term traffic flow forecasting. The urban traffic flow forecast helps to provide an early warning for traffic congestion and works as a feature for travel time prediction.
In this thesis, we propose a traffic flow prediction system to predict and visualize hourly traffic flow in Taoyuan urban area. A full year traffic data collected from RFID traffic flow detectors installed at the intersections are used in our research. To solve the missing data problem caused by scheduled maintenance or device malfunction, three imputation techniques are carried out to cleanse the uncorrected data. Two deep learning-based traffic flow prediction models, i.e., Recurrent Neural Network and Convolutional Neural Network (CNN), are used to forecast the short-term traffic flow. Finally, a R-Shiny online dashboard is set up to demonstrate our results. To assess the performance of the proposed model, several evaluation metrics are adopted to validate our models. The experiment results show that the accuracy of the CNN model is the highest among all of the models.

關鍵字(中)

★ 智慧型運輸系統
★ 車流量
★ 預測
★ 深度學習
★ 卷積神經網絡

關鍵字(英)

★ ITS
★ Traffic flow
★ Forecasting
★ Deep learning
★ Convolutional neural network

論文目次

1 Introduction 1
2 RelatedWork 4
2.1 Urban traffic flow prediction 4
2.1.1 Regression model 5
2.1.2 Traditional machine learning and neural networks 6
2.1.3 Deep learning 7
2.2 Freeway traffic flow prediction 8
3 Preliminary 11
3.1 Data source 11
3.2 Machine learning techniques 13
3.2.1 Regression techniques 13
3.2.2 Deep learning techniques 14
3.3 Missing value imputation 20
3.3.1 Mean or Median imputation 20
3.3.2 Linear interpolation 20
3.3.3 Deep learning approach 21
4 Design 22
4.1 System Model 22
4.2 Data Collection 24
4.3 Missing Data Imputation 25
4.4 Prediction Models 29
4.4.1 Training data preparing 29
4.4.2 Feature scaling 30
4.4.3 RNN model 31
4.4.4 CNN model 32
4.4.5 Model regularization 35
4.5 Visualization 36
5 Performance 40
5.1 Data Collection 40
5.2 Evaluation metrics 41
5.3 Experimental Results 44
5.3.1 Imputation methods 44
5.3.2 RNN models 45
5.3.3 CNN models 48
5.3.4 Comparison between different models 51
6 Conclusions 54
Reference 55

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指導教授

孫敏德(Min-Te Sun)

審核日期

2019-7-25

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