text: Traffic prediction is a fundamental and vital task in Intelligence Transportation System (ITS), but it is very challenging to get high accuracy while containing low computational complexity due to the spatiotemporal characteristics of traffic flow, especially under the metropolitan circumstances. In this work, a new topological framework, called Linkage Network, is proposed to model the road networks and present the propagation patterns of traffic flow. Based on the Linkage Network model, a novel online predictor, named Graph Recurrent Neural Network (GRNN), is designed to learn the propagation patterns in the graph. It could simultaneously predict traffic flow for all road segments based on the information gathered from the whole graph, which thus reduces the computational complexity significantly from O(nm) to O(n+m), while keeping the high accuracy. Moreover, it can also predict the variations of traffic trends. Experiments based on real-world data demonstrate that the proposed method outperforms the existing prediction methods.
  @cite_7: Abstract Big data from floating cars supply a frequent, ubiquitous sampling of traffic conditions on the road network and provide great opportunities for enhanced short-term traffic predictions based on real-time information on the whole network. Two network-based machine learning models, a Bayesian network and a neural network, are formulated with a double star framework that reflects time and space correlation among traffic variables and because of its modular structure is suitable for an automatic implementation on large road networks. Among different mono-dimensional time-series models, a seasonal autoregressive moving average model (SARMA) is selected for comparison. The time-series model is also used in a hybrid modeling framework to provide the Bayesian network with an a priori estimation of the predicted speed, which is then corrected exploiting the information collected on other links. A large floating car data set on a sub-area of the road network of Rome is used for validation. To account for the variable accuracy of the speed estimated from floating car data, a new error indicator is introduced that relates accuracy of prediction to accuracy of measure. Validation results highlighted that the spatial architecture of the Bayesian network is advantageous in standard conditions, where a priori knowledge is more significant, while mono-dimensional time series revealed to be more valuable in the few cases of non-recurrent congestion conditions observed in the data set. The results obtained suggested introducing a supervisor framework that selects the most suitable prediction depending on the detected traffic regimes.
  @cite_21: 
  @cite_22: Having access to the future traffic state information is crucial in maintaining successful intelligent transportation systems (ITS). However, predicting the future traffic state is a challenging research subject involving prediction reliability issues. Predictive performance measures, including the accuracy, efficiency, and stability, are generally considered as the most important priorities in the evaluation of prediction modules. Researchers have developed various K-nearest-neighbors-based searching algorithms that find the future state from the historical traffic patterns. Interestingly, there has not been sufficient effort made for improving the performance. For the emerging big data era, incorporating an efficient search strategy has become increasingly important since the applicability of the prediction module in ITS heavily relies on the efficiency of the searching method used. This paper develops a novel sequential search strategy for traffic state predictions. The proposed sequential strategy is found to be outperforming the conventional single-level search approach in terms of prediction measures, which are prediction accuracy, efficiency, and stability. Compared with the conventional approach, the proposed sequential method yields significantly more accurate results via internal hierarchical improvements across sublevels while maintaining excellent efficiency and stability.
  @cite_3: Real-time urban traffic speed estimation provides significant benefits in many real-world applications. However, existing traffic information acquisition systems only obtain coarse-grained traffic information on a small number of roads but cannot acquire fine-grained traffic information on every road. To address this problem, in this paper we study the traffic speed estimation problem, which, given a budget K, identifies K roads (called seeds) where the real traffic speeds on these seeds can be obtained using crowdsourcing, and infers the speeds of other roads (called non-seed roads) based on the speeds of these seeds. This problem includes two sub-problems: (1) Speed Inference - How to accurately infer the speeds of the non-seed roads; (2) Seed Selection - How to effectively select high-quality seeds. It is rather challenging to estimate the traffic speed accurately, because the traffic changes dynamically and the changes are hard to be predicted as many possible factors can affect the traffic. To address these challenges, we propose effective algorithms to judiciously select high-quality seeds and devise inference models to infer the speeds of the non-seed roads. On the one hand, we observe that roads have correlations and correlated roads have similar traffic trend: the speeds of correlated roads rise or fall compared with their historical average speed simultaneously. We utilize this property and propose a two-step model to estimate the traffic speed. The first step adopts a graphical model to infer the traffic trend and the second step devises a hierarchical linear model to estimate the traffic speed based on the traffic trend. On the other hand, we formulate the seed selection problem, prove that it is NP-hard, and propose several greedy algorithms with approximation guarantees. Experimental results on two large real datasets show that our method outperforms baselines by 2 orders of magnitude in efficiency and 40 in estimation accuracy.
summary: There are many previous works @cite_21 considering the traffic condition as a time series and predicting for different segments separately through time series analysis, like Auto-Regressive Moving Average (ARMA) based algorithms (ARIMA, SARIMA). Additionally, some research @cite_22 @cite_3 uses the methods of statistical learning such as Bayesian Network (BN), SVR and GBDT, and adds extra information to assist the training. @cite_7 compares those methods and shows their similar performances. In these approaches, the strong spatiotemporal couplings, which exist in metropolitan circumstance particularly, lead to the dilemma of choices between the computation complexity and the sufficiency of input information.