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Forecasting the traffic flow is greatly significant for traffic safety, energy conservation, and environmental protection. However, in the face of many external uncertainties, making accurate predictions about traffic volumes is a challenging issue. Many previous types of researche only explore the utility of a single factor in their prediction and rarely conduct the multi-factor research. As for the traffic flow prediction, many past types of researche focus primarily on the temporal distribution of the traffic flow on a single point on the road, ignoring the spatial correlation. In terms of global forecasting, it was logically far-fetched to mechanically view traffic as images. In this paper, considering the effects of many exogenous variables and the interaction between monitor sites, we propose a hybrid model to simultaneously predict the traffic flow in multiple positions by combining the layerwise structure and the Markov transition matrix (MTM). More specifically, we employ the layerwise structure to capture the periodicity, trend, and nonlinearity characteristics of traffic flow and, then, generate the MTM that captures the dynamics embodied in the data and produces the corresponding distributions. Considering the spatial correlation of traffic data, the real road network distance was thus introduced in our model. We apply the methodology on the real-world traffic data from Xiamen, and the experimental results show that the satisfactory predictions can be achieved using our model, which demonstrates the value of the transition matrix in traffic forecasts. In addition, we also introduce the point of interest and analyze its impact on the prediction results. INDEX TERMS Traffic flow forecast, layerwise structure, Markov transition matrix, point of interest.

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The acoustic technology has a great potential to measure the concentration of particles in water which is one of the parameters for understanding ocean ecology. To achieve this purpose, the forward acoustic propagation model has been considered as a method to obtain concentration and particle size. The Gaussian lognormal distribution was used to describe the particle matters condition in suspended water. Base on the model, a nonlinear inversion process took values of attenuation into calculation and get parameters of concentration, average radius and variance.