Urban flows prediction from spatial-temporal data using machine learning: A survey <s> Traditional machine learning methods <s> The literature on short-term traffic flow forecasting has undergone great development recently. Many works, describing a wide variety of different approaches, which very often share similar features and ideas, have been published. However, publications presenting new prediction algorithms usually employ different settings, data sets, and performance measurements, making it difficult to infer a clear picture of the advantages and limitations of each model. The aim of this paper is twofold. First, we review existing approaches to short-term traffic flow forecasting methods under the common view of probabilistic graphical models, presenting an extensive experimental comparison, which proposes a common baseline for their performance analysis and provides the infrastructure to operate on a publicly available data set. Second, we present two new support vector regression models, which are specifically devised to benefit from typical traffic flow seasonality and are shown to represent an interesting compromise between prediction accuracy and computational efficiency. The SARIMA model coupled with a Kalman filter is the most accurate model; however, the proposed seasonal support vector regressor turns out to be highly competitive when performing forecasts during the most congested periods. <s> BIB001 </s> Urban flows prediction from spatial-temporal data using machine learning: A survey <s> Traditional machine learning methods <s> The ability to timely and accurately forecast the evolution of traffic is very important in traffic management and control applications. This paper proposes a non-parametric and data-driven methodology for short-term traffic forecasting based on identifying similar traffic patterns using an enhanced K-nearest neighbor (K-NN) algorithm. Weighted Euclidean distance, which gives more weight to recent measurements, is used as a similarity measure for K-NN. Moreover, winsorization of the neighbors is implemented to dampen the effects of dominant candidates, and rank exponent is used to aggregate the candidate values. Robustness of the proposed method is demonstrated by implementing it on large datasets collected from different regions and by comparing it with advanced time series models, such as SARIMA and adaptive Kalman Filter models proposed by others. It is demonstrated that the proposed method reduces the mean absolute percent error by more than 25%. In addition, the effectiveness of the proposed enhanced K-NN algorithm is evaluated for multiple forecast steps and also its performance is tested under data with missing values. This research provides strong evidence suggesting that the proposed non-parametric and data-driven approach for short-term traffic forecasting provides promising results. Given the simplicity, accuracy, and robustness of the proposed approach, it can be easily incorporated with real-time traffic control for proactive freeway traffic management. <s> BIB002 </s> Urban flows prediction from spatial-temporal data using machine learning: A survey <s> Traditional machine learning methods <s> Summary ::: ::: Traffic flow prediction is an essential part of intelligent transportation systems (ITS). Most of the previous traffic flow prediction work treated traffic flow as a time series process only, ignoring the spatial relationship from the upstream flows or the correlation with other traffic attributes like speed and density. In this paper, we utilize a linear conditional Gaussian (LCG) Bayesian network (BN) model to consider both spatial and temporal dimensions of traffic as well as speed information for short-term traffic flow prediction. The LCG BN allows both continuous and discrete variables, which enables the consideration of categorical variables in traffic flow prediction. A microscopic traffic simulation dataset is used to test the performance of the proposed model compared to other popular approaches under different predicting time intervals. In addition, the authors investigate the importance of spatial data and speed data in flow prediction by comparing models with different levels of information. The results indicate that the prediction accuracy will increase significantly when both spatial data and speed data are included. Copyright © 2016 John Wiley & Sons, Ltd. <s> BIB003 </s> Urban flows prediction from spatial-temporal data using machine learning: A survey <s> Traditional machine learning methods <s> This paper presents a graph processing based traffic estimation system, GPTE, which is able to achieve high accuracy and high scalability to support city scale traffic estimation. GPTE benefits from its non-linear traffic correlation modeling and the graph-parallel processing framework built on clustered machines. By representing the road network as a property graph, GPTE decomposes the numerous computations involved in non-linear models to vertices and performs traffic estimation via neural network modeling and iterative information propagation. This paper presents our experiences in designing and implementing GPTE on top of the Spark, an emerging cluster computing framework. Extensive experiments are performed with real-world data input from Singapore's transport authority. Experimental results show that GPTE achieves as high as 88 percent accuracy in traffic estimation and up to $8\times$8× performance gain in computation efficiency with the optimization techniques applied. Comparison study demonstrates that GPTE outperforms the baseline solutions by 34 percent on accuracy and 46 percent on processing time. <s> BIB004
Support vector regression (SVR) model is usually used in traffic flow prediction, for example, SVR with RBF kernel multiplied by a seasonal kernel has been used in traffic flow forecasting with high prediction accuracy and computational efficiency BIB001 . As one of non-parametric and data-driven methods, an enhanced K-nearest neighbor(K-NN) algorithm applied in short-term traffic flow prediction based on identify similar traffic patterns BIB002 . Zhu et al. studied a linear conditional Gaussian (LCG) Bayesian network(BN) model for short-term traffic flow prediction, which considers spatial-temporal characteristics as well as speed information BIB003 . To tackle the task of estimating the number of people who moved between cells, Akagi et al.  developed a probabilistic model based on collective graphical models, which has considered movements to remote cells. As presented in Figure 4 , the proposed method is an unsupervised learning method and only needs input variables, which are spatiotemporal population data. Liu et al. BIB004 developed a graph processing framework based traffic estimation (GPTE), which can capture traffic correlation from taxi data and enable advanced traffic estimation at city-scale based on graph-parallel processing method. From these previous traditional machine learning-based methods, we can conclude that these models mainly focus on shortterm traffic flow prediction and receive high prediction accuracy. However, traffic data explosion due to the increase of traffic sensors and the rapid development of intelligent transport systems in recent years, traditional machine learningbased methods are restricted with mining the deep, implicit spatial-temporal correlations in the big traffic data.