臺灣博碩士論文加值系統

隨著現今社會交通工具數量不斷地增加，道路交通阻塞已經成為一項日益嚴重的問題 (Wang, 2010)。交通阻塞不僅影響人們日常生活，也影響著經濟、交通以及社會 (Zheng et al., 2014; Younes et al., 2015)。藉由類神經網絡對於阻塞點進行追蹤是建構智能交通神經管理系統中重要的一環，而透過診斷阻塞點進而預測未來阻塞點的發生更被認為是重要的議題之一，因為它可促使駕駛做出不同於日常行徑路線的決定，因而擴大交通網絡及大眾交通系統。
前人已有許多針對城市交通阻塞評估與預測之問題進行探討，而其中一項研究提出應用深度學習理論於大範圍阻塞預測 (Ma et al.,2015)。在深度學習領域中，RNN是一種以序列為基礎並廣為應用的模型，針對NLP問題RNN展現出非常好的應用前景，然而，RNN不僅能解決NLP問題，針對序列建模問題也展現出很優異的性能。Convolutional RNN (CRNN)演算法 (Shi et al., 2017)是DCNN與RNN兩種演算法的結合，針對序列類型目標，與傳統的類神經網絡相比，CRNN更具有許多獨特的優點，因此，本研究應用擴散理論並結合殘差神經網絡，提出殘差擴散神經網絡 (ResDCRNN)演算法以降低誤差進而提升整體預測性能的表現。而針對方法參數之設定，本研究應用高斯過程演算法對參數設定進行優化設計。此外，提出之演算法也應用於印度尼西亞智慧城市雅加達的交通阻塞預測問題進行個案研究。研究架構由數據收集、篩選特徵、預測和性能評價四個部分組成，結果顯示，本研究所提之方法亦具有較佳的表現。

With the steadily growth number of vehicles, road traffic congestion has become an increasingly important problem (Wang, 2010). This traffic congestion problem affects in many aspects to people’s daily lives as well as economy, transport, and society, too (Zheng et al., 2014; Younes et al., 2015). Tracking congestion throughout the network road is a critical component of intelligent transportation network management systems. Diagnosing congestion for predicting traffic congestion has been regarded as one the most important issues as it can lead to informal decisions on the routes that motorists take, and on expanding road networks and public transport. There have been some researches regarding urban traffic congestion estimation and prediction. (Ma et al.,2015) proposed to apply deep learning theory for large-scale congestion prediction.
RNN is a popular sequence-based model that has shown great promise in many NLP tasks. However, despite of solving NLP problems, nowadays, RNN also showed outstanding performance in many sequence modelling tasks. Convolutional RNN (CRNN) (Shi et al., 2017) is a combination of deep convolutional neural network (DCNN) and RNN. For sequence-like objects, CRNN possesses several distinctive advantages over conventional NNs. Thus, this study applies diffusion theory on the architecture and combine it with residual neural network in order to improve the performance. Then, a residual diffusion CRNN (ResDCRNN) is optimized by setting up of hyper-parameters by hiring Gaussian process into the algorithm. In addition, this proposed method will be applied to traffic jam forecasting in case study of smart city Jakarta, Indonesia. The research framework is consisted of four parts including data collection, feature selection, forecasting and performance evaluation. The result indicated that the proposed method also has better performance.

TABLE OF CONTENTS

摘要 ii
ABSTRACT iii
TABLE OF CONTENTS iv
LIST OF FIGURES vi
LIST OF TABLES vii
CHAPTER 1 INTRODUCTION 1
1.1 Background 1
1.2 Research Objectives 4
1.3 Scope and Constraints 4
1.4 Thesis Organization 4
CHAPTER 2 LITERATURE REVIEW 6
2.1 Urban Traffic Congestion Forecasting 6
2.2 Deep Learning 8
2.3.1 Recurrent Neural Network (RNN) 9
2.3.1.1 Convolutional Recurrent Neural Network (CRNN) 13
2.3.2 Residual Neural Network (ResNet) 14
2.3 Gaussian Processes Algorithm 15
CHAPTER 3 METHODOLOGY 17
3.1 Methodology Framework 17
3.2 Objective Functions 18
3.3 Data Collection 18
3.4 Gaussian Processes Algorithm-based Res-DCRNN Algorithm 19
CHAPTER 4 EXPERIMENTAL RESULTS 24
4.1 Datasets 24
4.2 Parameter Setting 27
4.3 Computational Results 28
4.4 Statistical Hypothesis 30
4.5 Sensitivity Analysis 32
CHAPTER 5 CONCLUSIONS AND FUTURE RESEARCH 33
5.1 Conclusions 33
5.2 Contributions 33
5.3 Future Research 34
REFERENCES 35
APPENDIX 38
Appendix 1. Solution for real instance 38

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