Predicting the traffic of an article, as measured by page views, is of great importance to content providers. Articles with increased traffic can improve advertising revenue and expand a provider’s user base. We propose a broadly applicable methodology incorporating meta-data and joint forecasting across articles, that involves solving a large optimization problem through the Alternating Direct...

This study aims to investigate the traffic information forecasting based on the data mining technology. As well known, useful knowledge in traffic management system often hides in a large amount of traffic data. Generally, prior data pattern labels have been used to train the Artificial Neural Network (ANN) to identify the traffic conditions in the traffic information forecasting. The performan...

To ensure the fishing boat security in the coastal area of Fujian, it is imperative to develop efficient forecasting control system to manage the boats operation in a safe and orderly process. The core issue of establishing the forecasting control system is to predict the relationship of the real time traffic flow and the boat security condition. However, literature review shows that limited re...

Traffic flow forecasting has been regarded as a key problem of intelligent transport systems. In this work, we propose a hybrid multimodal deep learning method for short-term traffic flow forecasting, which jointly learns the spatial-temporal correlation features and interdependence of multi-modality traffic data by multimodal deep learning architecture. According to the highly nonlinear charac...

Deep learning approaches have reached a celebrity status in artificial intelligence field, its success have mostly relied on Convolutional Networks (CNN) and Recurrent Networks. By exploiting fundamental spatial properties of images and videos, the CNN always achieves dominant performance on visual tasks. And the Recurrent Networks (RNN) especially long short-term memory methods (LSTM) can succ...

A traffic condition forecasting system using floating car data in conjunction with a data mining method is proposed. A register network is used to describe the congestion model. The register network denotes the dual graph of the actual road link connections. In this forecasting system, estimation and learning agents alternately calculate the results to improve the forecasting accuracy. To forec...

interpret and analyze the resulting model. Autoregressive integrated moving average (ARIMA) models, which encompass RW, random-trend models; auto-regressive models; and exponential weighted moving averages are linear time series models that have been quite popular thanks to their ability to exploit temporal dependence in prediction errors (4, 5). Linear models that exploit both spatial and temp...

It is an important to quickly and accurately forecasting road network traffic flow in intelligent transportation systems, Aiming at the forecasting problem of short-term traffic flow, this paper proposed a traffic flow prediction algorithm, which based on traffic flow sequence partition and neural network model. Firstly, the algorithm divided the traffic flow into different patterns and time se...

Short-term traffic flow forecasting is an important aspect of the ITS as traffic predication can alleviate congestion, which causes drivers to incur a longer traveling time and economical loses. In addition, traffic congestion increases the pollution and the fuel usage. Thus, it is one of the severe problems in Metropolitan areas. Further, in tunnels the forecasting may help scheduling the vent...