【論文閱讀】Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction [將交通作為圖像學習: 用于大規模交通網絡速度預測的深度卷積神經網絡]（1）

• Abstract（摘要）
• 1.Introduction
• 參考文獻

Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction
將交通學習為圖像:用于大規模交通網絡速度預測的深度卷積神經網絡

注: 閱讀原文請轉至link.

Abstract（摘要）

Abstract: This paper proposes a convolutional neural network (CNN)-based method that learns traffic as images and predicts large-scale, network-wide traffic speed with a high accuracy. Spatiotemporal traffic dynamics are converted to images describing the time and space relations of traffic flow via a two-dimensional time-space matrix. A CNN is applied to the image following two consecutive steps: abstract traffic feature extraction and network-wide traffic speed prediction. The effectiveness of the proposed method is evaluated by taking two real-world transportation networks, the second ring road and north-east transportation network in Beijing, as examples, and comparing the method with four prevailing algorithms, namely, ordinary least squares, k-nearest neighbors, artificial neural network, and random forest, and three deep learning architectures, namely, stacked autoencoder, recurrent neural network, and long-short-term memory network. The results show that the proposed method outperforms other algorithms by an average accuracy improvement of 42.91% within an acceptable execution time. The CNN can train the model in a reasonable time and, thus, is suitable for large-scale transportation networks.
Keywords: transportation network; traffic speed prediction; spatiotemporal feature; deep learning; convolutional neural network

摘要: 本文提出了一種基于卷積神經網絡(CNN)的方法，該方法將交通作為圖像學習，并以較高的精度預測大規模、全網絡的交通速度。通過二維時空矩陣將交通流的時空動態轉換為描述交通流時空關系的圖像。將CNN應用于圖像，經過兩個連續步驟:抽象交通特征提取和全網交通速度預測。以北京市二環交通網絡和東北交通網絡為例，與普通最小二乘、k近鄰、人工神經網絡和隨機森林四種常用交通網絡算法進行比較，評價了該方法的有效性。三種深度學習體系結構，即堆疊自編碼器、循環神經網絡和長短期記憶網絡。結果表明，在可接受的執行時間內，該方法的平均精度比其他算法提高了42.91%。CNN可以在合理的時間內對模型進行訓練，因此適用于大型交通網絡。
關鍵詞: 交通網絡; 交通速度預測; 時空特征; 深度學習; 卷積神經網絡

1.Introduction

?Predicting the future is one of the most attractive topics for human beings, and the same is true for transportation management. Understanding traffic evolution for the entire road network rather than on a single road is of great interest and importance to help people with complete traffic information in make better route choices and to support traffic managers in managing a road network and allocate resources systematically [1], [2].
?預測未來是人類最具吸引力的話題之一，交通管理也是如此。了解整個路網的交通變化，而不是單一道路上的交通變化，對于幫助擁有完整交通信息的人們更好地規劃路線，并支持交通管理者系統地管理路網和分配資源具有重要意義[1], [2]。

?However, large-scale network traffic prediction requires more challenging abilities for prediction models, such as the ability to deal with higher computational complexity incurred by the network topology, the ability to form a more intelligent and efficient prediction to solve the spatial correlation of traffic in roads expanding on a two-dimensional plane, and the ability to forecast longer-term futures to reflect congestion propagation. Unfortunately, traditional traffic prediction models, which usually treat traffic speeds as sequential data, do not provide those abilities because of limitations, such as hypotheses and assumptions, ineptness to deal with outliers, noisy or missing data, and inability to cope with the curse of dimensionality [2]. Thus, existing models may fail to predict large-scale network traffic evolution.
?然而，大規模網絡流量預測對預測模型的能力要求更高，比如應對網絡拓撲結構帶來的更高計算復雜度的能力，形成更智能、更高效的預測以解決二維平面上道路交通的空間相關性的能力，以及預測更長期未來以反映擁堵傳播的能力。不幸的是，傳統的交通預測模型通常將交通速度視為順序數據，但由于假設和假設、處理離群值的能力、噪聲或缺失數據的能力、以及無法處理維數 [3]的變化等限制，無法提供這些能力。因此，現有的模型可能無法預測大規模網絡流量的變化。

?In the existing literature, two families of research methods have dominated studies in traffic forecasting: statistical methods and neural networks [3].
?在現有的文獻中，交通預測的研究主要有兩類研究方法:統計方法和神經網絡[3]。

?Statistical techniques are widely used in traffic prediction. For example, according to the periodicity of traffic evolutions, nonparametric models, such as k-nearest neighbors (KNN), have been applied to predict traffic speeds and volumes [4–6]. More advanced models were employed, including support vector machines (SVM) [7], seasonal SVM [8], Online-SVM [9], and on-line sequential extreme learning machine [10], to promote prediction accuracy by capturing the high dynamics and sensitivity of traffic flow. SVM performance in large-scale traffic speed prediction was further improved [8] [11]. Multivariate nonparametric regression was also used in traffic prediction [12,13]. Recently, a wealth of literature leverage multiple hybrid models and spatiotemporal features to improve traffic prediction performance. For example, Li et al. [14] proposed a hybrid strategy with ARIMA and SVR models to enhance traffic prediction power by considering both spatial and temporal features. Zhu et al. [15] employed a linear conditional Gaussian Bayesian network (LCG-BN) with spatial and temporal, as well as speed, information for traffic flow prediction. Li et al. [16] studied the chaotic situation of traffic flow based on a Bayesian theory-based prediction algorithm, and incorporated speed, occupancy, and flow for accuracy improvement. Considering the correlations shown in successive time sequences of traffic variables, time-series prediction models have been widely employed in traffic prediction. One of the typical models is the autoregressive integrated moving average (ARIMA) model, which considers the essential traffic flow characteristics, such as inherent correlations (via a moving average) and its effect on the short future (via autoregression). To date, the model, and its extensions, such as the seasonal ARIMA model [17,18], KARIMA model [19], and the ARIMAX model [20], have been widely studied and applied. In summary, statistical methods have been widely used in traffic prediction, and promising results have been demonstrated. However, these models ignore the important spatiotemporal feature of transportation networks, and cannot be applied to predict overall traffic in a large-scale network. SVM usually takes a long time and consumes considerable computer memory on training and, hence, it might be powerless in large data-related applications.
?統計技術廣泛應用于交通預測。例如，根據交通變化的周期性，應用k-nearest neighbors (KNN)等非參數模型來預測交通速度和流量[4–6]。采用支持向量機[7]、季節性支持向量機[8]、在線支持向量機[9]和在線序流極值學習機[10]等更先進的模型，通過捕捉交通流的高動態性和敏感性來提高預測精度。進一步提高了SVM在大規模交通速度預測中的性能[8] [11]。多變量非參數回歸也用于交通預測[12,13]。近年來，大量文獻利用多種混合模型和時空特征來提高交通預測性能。例如Li et al.[14]提出了一種混合ARIMA和SVR模型的策略，通過考慮空間和時間特征來提高交通預測能力。Zhu等人的[15]采用具有時空信息和速度信息的線性條件高斯貝葉斯網絡(LCG-BN)進行交通流預測。Li等[16]基于基于貝葉斯理論的預測算法研究交通流的混沌狀態，并結合速度、占用率和流量來提高精度。由于交通變量在連續時間序列中表現出的相關性，時間序列預測模型被廣泛應用于交通預測中。最典型的模型之一是自回歸綜合移動平均(ARIMA)模型，該模型考慮了交通流的本質特征，如內在相關性(通過移動平均)及其對短期未來的影響(通過自回歸)。到目前為止，該模式及其擴展，如季節性ARIMA模式[17,18]、KARIMA模式[19]和ARIMAX模式[20]，已經得到了廣泛的研究和應用。綜上所述，統計方法在交通預測中得到了廣泛的應用，并取得了良好的效果。然而，這些模型忽略了交通網絡的重要時空特征，無法用于大規模交通網絡的整體交通預測。支持向量機通常需要很長的時間和大量的計算機內存用于訓練，因此，在大型數據相關的應用中可能是無能為力的。

?Artificial neural networks (ANNs) are also usually applied to traffic prediction problems because of its advantages, such as their capability to work with multi-dimensional data, implementation flexibility, generalizability, and strong forecasting power [3]. For example, Huang and Ran [21] used an ANN to predict traffic speed under adverse weather conditions. Park et al. [2] presented a real-time vehicle speed prediction algorithm based on ANN. Zheng et al. [22] combined an ANN with Bayes’ theorem to predict short-term freeway traffic flow. Moretti et al. [23] developed a statistical and ANN bagging ensemble hybrid model to forecast urban traffic flow.
?由于人工神經網絡具有處理多維數據的能力、實現的靈活性、通用性以及較強的預測能力[3]等優點，因此也常被應用于流量預測問題。例如，Huang和Ran[21]使用人工神經網絡預測惡劣天氣條件下的交通速度。Park等人[2]提出了一種基于ANN的實時車速預測算法。Zheng等人[22]將神經網絡與貝葉斯定理相結合來預測短期高速公路交通流。Moretti等人[23]開發了一種統計和人工神經網絡bagging集合的混合模型來預測城市交通流。

?However, the data-driven mechanism of an ANN cannot explain the spatial correlations of a road network particularly well. In addition, compared with deep learning approaches, the prediction accuracy of an ANN is lower because of its shallow architecture. Recently, more advanced and powerful deep learning models have been applied to traffic prediction. For example, Polson and Sokolov [24] used deep learning architectures to predict traffic flow. Huang et al. [25] first introduced Deep Belief Networks (DBN) into transportation research. Then, Tan et al. [26] compared the performance of DBNs with two kinds of RBM structures, namely, RBM with binary visible and hidden units (B-B RBM) and RBM with Gaussian visible units and binary hidden units (G-B RBM), and found that the former outperforms the later in traffic flow prediction. Ma et al. [27] combined deep restricted Boltzmann machines (RBM) with a recurrent neural network (RNN) and formed a RBM-RNN model that inherits the advantages of both RBM and RNN. Lv et al. [28] proposed a novel deep-learning-based traffic prediction model that considered spatiotemporal relations, and employed stack autoencoder (SAE) to extract traffic features. Duan et al. [29] used denoising stacked autoencoders (DSAE) for traffic data imputation. Ma et al. [30] introduced a long short-term memory neural network (LSTM NN) into traffic prediction and demonstrated that LSTM NN outperformed other neural networks in both stability and accuracy in terms of traffic speed prediction by using remote microwave sensor data collected from the Beijing road network.
?然而，神經網絡的數據驅動機制并不能很好地解釋道路網絡的空間相關性。此外，與深度學習方法相比，神經網絡的結構較淺，其預測精度較低。近年來，更先進、更強大的深度學習模型被應用到流量預測中。例如，Polson和Sokolov[24]使用深度學習架構來預測交通流。Huang等人[25]首先將深度信念網絡(Deep Belief Networks, DBN)引入交通研究。然后，Tan等人[26]比較了dbn與兩種RBM結構的性能，即具有二進制可見和隱藏單元的RBM (B-B RBM)和具有高斯可見和二進制隱藏單元的RBM (G-B RBM)，發現前者在交通流預測方面優于后者。Ma等人[27]將深度限制波爾茲曼機(RBM)和循環神經網絡(RNN)結合起來，形成了繼承RBM和RNN優點的RBM-RNN模型。Lv等人[28]提出了一種考慮時空關系的基于深度學習的新型交通預測模型，并采用堆棧自動編碼器(SAE)提取交通特征。Duan等人的[29]使用去噪的堆疊自動編碼器(DSAE)進行交通數據imputation。Ma等人[30]將一種長短期記憶神經網絡(long - short-term memory neural network, LSTM神經網絡)引入到交通預測中，利用北京路網中收集的遠程微波傳感器數據，證明LSTM神經網絡在交通速度預測的穩定性和準確性方面優于其他神經網絡。

?Deep learning methods exploit much deeper and more complex architectures than an ANN, and can achieve better results than traditional methods. However, these attempts still mainly focus on the prediction of traffic on a road section or a small network region. Few studies have considered a transportation network as a whole and directly estimated the traffic evolution on a large scale. More importantly, the majority of these models merely considered the temporal correlations of traffic evolutions at a single location, and did not consider its spatial correlations from the perspective of the network.
?深度學習方法利用了比人工神經網絡更深、更復雜的體系結構，可以獲得比傳統方法更好的結果。然而，這些嘗試仍然主要集中在某一路段或小網絡區域的交通預測上。很少有研究將交通網絡作為一個整體來考慮，直接對大規模的交通演化進行估計。更重要的是，這些模型大多只考慮了單一地點交通演化的時間相關性，而沒有從網絡的角度考慮其空間相關性。

?To fill the gap, this paper introduces an image-based method that represents network traffic as images, and employs the deep learning architecture of a convolutional neural network (CNN) to extract spatiotemporal traffic features contained by the images. A CNN is an efficient and effective image processing algorithm and has been widely applied in the field of computer vision and image recognition with remarkable results achieved [31,32]. Compared with prevailing artificial neural networks, a CNN has the following properties in extracting features: First, the convolutional layers of a CNN are connected locally instead of being fully connected, meaning that output neurons are only connected to its local nearby input neurons. Second, a CNN introduces a new layer-construction mechanism called pooling layers that merely select salient features from its receptive region and tremendously reduce the number of model parameters. Third, normal fully-connected layers are used only in the final stage, when the dimension of input layers is controllable. The locally-connected convolutional layers enable a CNN to efficiently deal with spatially-correlated problems [31,33,34]. The pooling layers makes CNNs generalizable to large-scale problems [35]. The contributions of the paper can be summarized as follows:

• The temporal evolutions and spatial dependencies of network traffic are considered and applied simultaneously in traffic prediction problems by exploiting the proposed image-based method and deep learning architecture of CNNs.
• Spatiotemporal features of network traffic can be extracted using a CNN in an automatic manner with a high prediction accuracy.
• The proposed method can be generalized to large-scale traffic speed prediction problems while retaining trainability because of the implementation of convolutional and pooling layers.

?為了填補這一空白，本文引入了一種基于圖像的方法，將網絡流量表示為圖像，并利用卷積神經網絡(CNN)的深度學習體系提取圖像包含的時空流量特征。CNN是一種高效、有效的圖像處理算法，已廣泛應用于計算機視覺和圖像識別領域，取得了顯著的效果[31,32]。與現有的人工神經網絡相比，CNN在特征提取方面具有以下特點:首先，CNN的卷積層是局部連接的，而不是完全連接的，即輸出神經元只與附近的局部輸入神經元連接;其次，CNN引入了一種新的層構建機制，稱為池化層，它只從接受區域中選擇顯著特征，大大減少了模型參數的數量。第三，在輸入層尺寸可控的情況下，僅在最后階段使用正常的全連通層。局部連接的卷積層使CNN能夠有效地處理空間相關問題[31,33,34]。池化層使CNN可泛化到大規模問題[35]。本文的貢獻可以總結如下:

• 利用所提出的基于圖像的方法和CNN的深度學習體系結構，將網絡流量的時間演化和空間依賴性同時應用到流量預測問題中。
• 利用CNN可以自動提取網絡流量的時空特征，具有較高的預測精度。
• 由于實現了卷積層和池化層，該方法可以在保持可訓練性的前提下推廣到大規模的交通速度預測問題。

?The rest of the paper is organized as follows: In Section 2, a two-step procedure that includes converting network traffic to images and a CNN for network traffic prediction is introduced. In Section 3, four prediction tests are conducted on two transportation networks using the proposed method, and are compared with the other prevailing prediction methods. Finally, conclusions are drawn with future study directions in Section 4.
?本文的其余部分組織如下:在第2節中，介紹了將網絡流量轉換為圖像和用于網絡流量預測的CNN的兩步過程。在第3節中，利用該方法對兩個交通網絡進行了4次預測試驗，并與其他常用的預測方法進行了比較。最后，在第4節得出結論，并指出未來的研究方向。

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總結

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