Real time traffic flow is often difficult to predict precisely because of the nonlinear and stochastic characteristics of the traffic flow data. Intelligent prediction methods such as artificial neural network (ANN), support vector machine (SVM), etc. have been proven effective to discover the nonlinear information hidden in the traffic flow data. Nevertheless, their efficiency limits in the lo...

We extend the Barth, Cram, and Nelson (2001) model with disaggregated accruals by investigating whether cash flow components (core and non-core cash flows) improve cash flow predictability. BCN investigate the role of accrual components in predicting future cash flows while this study extends their study by investigating the role of cash flow components. We propose a cash flow prediction model ...

Real time traffic flow is often difficult to predict precisely because of the complexity, nonlinearity and uncertainty characteristics of the traffic flow data. Intelligent prediction methods such as artificial neural network (ANN), support vector machine (SVM), etc. have been proven effective to discover the nonlinear information hidden in the traffic flow data. Nevertheless, a single predicti...

the objective of this study was to develop a numerical model for the prediction of temperature distribution, effective plastic strain distribution, and especially material flow in friction stir welding of copper plates. the deform-3d software was used by incorporating a lagrangian incremental formulation. threedimensional results of the material flow pattern which were extracted using the point...

During its near-ground hovering phase a Short Take-Off and Vertical Landing (STOVL) aircraft creates a complex three-dimensional flow field between jet streams, the airframe surface and the ground. A proper understanding and numerical prediction of this flow is important in the design of such aircraft. In this paper an experimental facility, used to gather validation data suitable for testing C...

In order to change the insufficiency of traditional network flow prediction and improve its accuracy, the paper proposed a kind of network flow prediction method based on the self-adaptive genetic least square support vector machine optimization. Through analyzing the individual parameter of the LS-SVM principle and self-adaptive remains algorithm, the network flow prediction model structure of...

mathematical simulation of flow toward drains is an important and indispensable stage in drainage design and management. many related models have been developed, but most of them simulate the saturated flow toward drains without a due consideration of the unsaturated zone. in this study, the two dimensional differential equation governing saturated and unsaturated flow in porous media is numeri...

This paper proposes a spatiotemporal deep learning framework, Trajectory-based Graph Neural Network (TrGNN), that mines the underlying causality of flows from historical vehicle trajectories and incorporates into road traffic prediction. The trajectory transition patterns are studied to explicitly model spatial demand via graph propagation along network; an attention mechanism is designed learn...