On-road sensor configuration design for traffic flow prediction using fuzzy neural networks and Taguchi method

On-road sensors provide proactive traffic control centers with current traffic flow conditions in order to forecast the future conditions. However, the number of on-road sensors is usually huge, and not all traffic flow conditions captured by these sensors are useful for predicting future traffic flow conditions. The inclusion of all captured traffic flow conditions is an ineffective means of predicting future traffic flow. Therefore, the selection of appropriate on-road sensors, which are significantly correlated to future traffic flow, is essential, although the trial and error method is generally used for the selection. In this paper, the Taguchi method, which is a robust and systematic optimization approach for designing reliable and high-quality models, is proposed for determinations of appropriate on-road sensors, in order to capture useful traffic flow conditions for forecasting. The effectiveness of the Taguchi method is demonstrated by developing a traffic flow predictor based on the architecture of fuzzy neural networks which can perform well on traffic flow forecasting. The case study was conducted based on traffic flow data captured by on-road sensors located on a Western Australia freeway. The advantages of using the Taguchi method can be indicated: (a) traffic flow predictors with high accuracy can be designed; and (b) development time of traffic flow predictors is reasonable

A hierarchical methodology for vessel traffic flow prediction using Bayesian tensor decomposition and similarity grouping

Accurate vessel traffic flow (VTF) prediction can enhance navigation safety and economic efficiency. To address the challenge of the inherently complex and dynamic growth of the VTF time series, a new hierarchical methodology for VTF prediction is proposed. Firstly, the original VTF data is reconfigured as a three-dimensional tensor by a modified Bayesian Gaussian CANDECOMP/PARAFAC (BGCP) tensor decomposition model. Secondly, the VTF matrix (hour ✕ day) of each week is decomposed into high- and low-frequency matrices using a Bidimensional Empirical Mode Decomposition (BEMD) model to address the non-stationary signals affecting prediction results. Thirdly, the self-similarities between VTF matrices of each week within the high-frequency tensor are utilised to rearrange the matrices as different one-dimensional time series to solve the weak mathematical regularity in the high-frequency matrix. Then, a Dynamic Time Warping (DTW) model is employed to identify grouped segments with high similarities to generate more suitable high-frequency tensors. The experimental results verify that the proposed methodology outperforms the state-of-the-art VTF prediction methods using real Automatic Identification System (AIS) datasets collected from two areas. The methodology can potentially optimise relation operations and manage vessel traffic, benefiting stakeholders such as port authorities, ship operators, and freight forwarders