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Forecasting the crowd flows of some densely populated areas (functional areas) at urban level is vital for the city and traffic management and much efforts have been put into this field. However, considering the complex dependencies of crowd flows prediction, the current models generally neglected the multi-scale correlation of spatial view and weakened the influence of external features on crowd flows data. In this paper, we proposed a deep learning framework, called MV-RANet, to deal with them and further improve the prediction performance. Firstly, a double-branch residual attention network was utilized to model the spatial correlation between fine-grained regions and coarse-grained functional areas separately with different branch. And furthermore, some human mobility pattern information were incorporated in MV-RANet to capture the correlation between functional areas in a different view. And as for the impact of external factors, we combined them with crowd flows data by multi-channel mechanism, which can make the external factors and spatio-temporal crowd flows data one-to-one mapping, to refine their influence on the crowd flows. We evaluated our model with two different datasets, a large scale Call Detail Records (CDRs) data and Beijing taxicabs' trajectories data (TaxiBJ) (Zhang et al., 2017). The experimental results demonstrate that the proposed MV-RANet consistently outperforms all the baselines. [ABSTRACT FROM AUTHOR]

Traffic guidance and prompt information could induce the change of traffic states on road sections, and in turn the effects of these changes will be transited to their relative upstream and downstream sections, which lead to dynamic variations in traffic states of urban regional road networks. In this paper, the rule of dynamic transition in traffic state of urban road networks under the effect of traffic information is studied. Specifically, the hidden Markov model is selected to represent the dynamic transition process. Then Expectation Maximization (EM) Algorithm is presented for potential traffic state estimation. Finally, verification is carried out through simulation with Variable Message Sign (VMS) selected as the information release terminal and then a certain regional road network in Beijing is chosen as the study object. The results show that the model in this paper can describe transition process of road traffic state under the effect of VMS information, and also can be used for real-time traffic state estimation of urban road networks. The study has both theoretical and practical values in evaluation of service quality of traffic information and in making traffic dispersion and control strategies for traffic management department. [ABSTRACT FROM AUTHOR]

In order to effectively manage the use of existing infrastructures and prevent the emergency caused by the large gathered crowd, the short-term passenger flow forecasting technology becomes more and more significant in the field of intelligent transportation system. However, there are few studies discussing how to predict different kinds of passenger flows in the subway system. In this paper, a novel hybrid model Wavelet-SVM is proposed, and it combines the complementary advantages of Wavelet and SVM models, and meanwhile overcomes their shortcomings respectively. The Wavelet-SVM forecasting approach consists of three important stages. The first stage decomposes the passenger flow data into different high frequency and low frequency series by wavelet. During the prediction stage, the SVM method is applied to learn and predict the corresponding high frequency and low frequency series. In the last stage, the diverse predicted sequences are reconstructed by wavelet. The experimental results show that the approach not only has the best forecasting performance compared with the state-of-the-art techniques but also appears to be the most promising and robust based on the historical passenger flow data in Beijing subway system and several standard evaluation measures. [ABSTRACT FROM AUTHOR]