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.

Andreoletti, D., Troia, S., Musumeci, F., Silvia, G., Maier, G. A., & Tornatore, M. (2019). Network Traffic Prediction based on Diffusion Convolutional Recurrent Neural Networks. In INFOCOM (pp. 1-6).
Cai, W., Chen, S. & Zhang, D. (2010). A multiobjective simultaneous learning framework for clustering and classification. IEEE Transactions on Nneural Networks, 21(2), 185-200.
Connor, J. T., Martin, R. D. & Atlas, L. E. (1994). Recurrent neural networks and robust time series prediction. IEEE Transactions on Neural Networks, 5(2), 240-254.
Giles, C. L., Miller, C. B., Chen, D., Sun, G. Z., Chen, H. H. & Lee, Y. C. (1992). Extracting and learning an unknown grammar with recurrent neural networks. In Advances in Neural Information Processing Systems (pp. 317-324).
Goodfellow, I., Bengio, Y., Courville, A., & Bengio, Y. (2016). Deep learning (Vol. 1). Cambridge: MIT press.
Forcada, M. L. & Carrasco, R. C. (1995). Learning the initial state of a second-order recurrent neural network during regular-language inference. Neural computation, 7(5), 923-930.
Grall, A., Dieulle, L., Bérenguer, C. & Roussignol, M. (2002). Continuous-time predictive-maintenance scheduling for a deteriorating system. IEEE Transactions on Reliability, 51(2), 141-150.
Graves, A. (2013). Generating sequences with recurrent neural networks. arXiv preprint arXiv:1308.0850.
Graves, A. & Schmidhuber, J. (2005). Framewise phoneme classification with bidirectional LSTM and other neural network architectures. Neural Networks, 18(5-6), 602-610.
Guo, L., Li, N., Jia, F., Lei, Y. & Lin, J. (2017). A recurrent neural network based health indicator for remaining useful life prediction of bearings. Neurocomputing, 240, 98-109.
Hinton, G. E., Osindero, S. & Teh, Y. W. (2006). A fast learning algorithm for deep belief nets. Neural Computation, 18(7), 1527-1554.
Jakarta Smart City, 2018., https://smartcity.jakarta.go.id/, (online accessed: 2018).
Hong, S., Zhou, Z., Zio, E. & Hong, K. (2014). Condition assessment for the performance degradation of bearing based on a combinatorial feature extraction method. Digital Signal Processing, 27, 159-166.
Kalchbrenner, N. & Blunsom, P. (2013). Recurrent continuous translation models. In Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing (pp. 1700-1709).
Krizhevsky, A., Sutskever, I. & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems (pp. 1097-1105).
Lawrence, S., Giles, C. L. & Fong, S. (2000). Natural language grammatical inference with recurrent neural networks. IEEE Transactions on Knowledge and Data Engineering, 12(1), 126-140.
LeCun, Y., Bengio, Y. & Hinton, G. (2015). Deep learning. nature, 521(7553), 436.
Lipton, Z. C., Berkowitz, J. & Elkan, C. (2015). A critical review of recurrent neural networks for sequence learning. arXiv preprint arXiv:1506.00019.
Mikolov, T. (2012). Statistical language models based on neural networks. Presentation at Google, Mountain View, 2nd April.
Mikolov, T. & Zweig, G. (2012). Context dependent recurrent neural network language model. SLT, 12(234-239), 8.
Mirjalili, S. (2016). SCA: a sine cosine algorithm for solving optimization problems. Knowledge-Based Systems, 96, 120-133.
Mukhopadhyay, A., Bandyopadhyay, S. & Maulik, U. (2010). Multi-class clustering of cancer subtypes through SVM based ensemble of pareto-optimal solutions for gene marker identification. PloS one, 5(11), e13803.
Nectoux, P., Gouriveau, R., Medjaher, K., Ramasso, E., Chebel-Morello, B., Zerhouni, N. & Varnier, C. (2012, June). PRONOSTIA: An experimental platform for bearings accelerated degradation tests. In IEEE International Conference on Prognostics and Health Management, PHM'12. (pp. 1-8). IEEE Catalog Number: CPF12PHM-CDR.
Pattara-Atikom, W., Pongpaibool, P. & Thajchayapong, S. (2006, June). Estimating road traffic congestion using vehicle velocity. In ITS Telecommunications Proceedings, 2006 6th International Conference on (pp. 1001-1004). IEEE.
Rasmussen, C. E. (2004). Gaussian processes in machine learning. In Advanced lectures on machine learning (pp. 63-71). Springer, Berlin, Heidelberg.
Saad, E. W., Prokhorov, D. V. & Wunsch, D. C. (1998). Comparative study of stock trend prediction using time delay, recurrent and probabilistic neural networks. IEEE Transactions on Neural Networks, 9(6), 1456-1470.
Salakhutdinov, R. & Larochelle, H. (2010, March). Efficient learning of deep Boltzmann machines. In Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (pp. 693-700).
Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural Networks, 61, 85-117.
Shi, B., Bai, X. & Yao, C. (2017). An end-to-end trainable neural network for image-based sequence recognition and its application to scene text recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(11), 2298-2304.
Singh, S., Tripathy, M. & Anand, R. S. (2014). Subjective and objective analysis of speech enhancement algorithms for single channel speech patterns of Indian and english languages. IETE Technical Review, 31(1), 34-46.
Su, H. & Yu, S. (2007, November). Hybrid GA based online support vector machine model for short-term traffic flow forecasting. In International Workshop on Advanced Parallel Processing Technologies (pp. 743-752). Springer, Berlin, Heidelberg.
Sun, M., Zhang, X. & Zheng, T. F. (2016). Unseen noise estimation using separable deep auto encoder for speech enhancement. IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP), 24(1), 93-104.
Susto, G. A., Schirru, A., Pampuri, S., McLoone, S. & Beghi, A. (2015). Machine learning for predictive maintenance: A multiple classifier approach. IEEE Transactions on Industrial Informatics, 11(3), 812-820.
Sutrisno, E., Oh, H., Vasan, A. S. S. & Pecht, M. (2012, June). Estimation of remaining useful life of ball bearings using data driven methodologies. In Prognostics and Health Management (PHM), 2012 IEEE Conference on (pp. 1-7).
Tawhid, M. A. & Savsani, V. (2017). Multi-objective sine-cosine algorithm (MO-SCA) for multi-objective engineering design problems. Neural Computing and Applications, 1-15.
Tian, K., Zhou, S. & Guan, J. (2017). DeepCluster: A General Clustering Framework based on Deep Learning. Joint European Conference on Machine Learning and Knowledge Discovery in Databases (p. 3). Springer, Cham.
Younes, M. B. & Boukerche, A. (2015). A performance evaluation of an efficient traffic congestion detection protocol (ECODE) for intelligent transportation systems. Ad Hoc Networks, 24, 317-336