Fuel consumption prediction for pre-departure flights using attention-based multi-modal fusion

Improper fuel loading decision results in carrying excessive dead weight during flight operation, which will burden the airline operation cost and cause extra waste emission. Existing works mainly focused on the post-event fuel consumption based on flight trajectory. In this work, a novel deep learning model, called FCPNet, is proposed to...

Impacts of the Coordination of DTMs with Different Local Control Objectives Considering Their Spatial Layout

With the steadily increasing road traffic demand, congestion on freeways has become a major problem. With the development of communication technology and automated vehicles, there are more opportunities for DTMs to be studied extensively at the coordination level and cooperation level. In the existing literature, the coordination of RM (ramp...

Operationalizing modular autonomous customised buses based on different demand prediction scenarios

This paper presents a novel framework for customised modular bus systems that leverages travel demand prediction and modular autonomous vehicles to optimise services proactively. The proposed framework addresses two prediction scenarios with different forward-looking operations: optimistic operation and pessimistic operation. A mixed integer...

Data-driven corrosion inhibition efficiency prediction model incorporating 2D–3D molecular graphs and inhibitor concentration

Following the construction of a dataset of cross-category corrosion inhibitors at different concentrations based on 1241 data from 184 research papers, a performance prediction model incorporating 2D–3D molecular graph representation and corrosion inhibitor concentration information was established. This model was shown to effectively predict...

A novel GA-LSTM-based prediction method of ship energy usage based on the characteristics analysis of operational data

Optimization of ship energy efficiency is an efficient measure to decrease fuel usage and emissions in the shipping industry. The accurate prediction model of ship energy usage is the basis to achieve optimization of ship energy efficiency. This study investigates the sequential properties of the actual voyage data from a VLOC. On this basis,...

In-situ damage mechanism investigation and a prediction model for delamination with fibre bridging in composites

Carbon-fibre reinforced composites are susceptible to delamination. Fibre bridging is an important shielding mechanism frequently observed in delamination. The presence of these bridging fibres can significantly increase interlaminar resistance, making it critical to represent this phenomenon for delamination characterization in composite...

A reliable progressive fatigue damage model for life prediction of composite laminates incorporating an adaptive cyclic jump algorithm

In this paper, a reliable progressive fatigue damage model (PFDM) for predicting the fatigue life of composite laminates is proposed by combining the normalized fatigue life model, nonlinear residual degradation models and fatigue-improved Puck criterion. To balance the accuracy of life predictions and computational efficiency, an adaptive...

Turning Maneuver Prediction of Connected Vehicles at Signalized Intersections: A Dictionary Learning-Based Approach

Vehicle-to-Infrastructure (V2I) communication has provided a solution for the improvement of the traffic efficiency of smart city intersections. For example, turning maneuvers prediction at signalized intersections in a connected environment helps traffic command centers time traffic lights and dynamically predict traffic flow. However, the...

Multi-scenario Model Predictive Control based on genetic algorithms for level regulation of open water systems under ensemble forecasts

Operational water resources management needs to adopt operational strategies to re-allocate water resources by manipulating hydraulic structures. Model Predictive Control (MPC) has been shown to be a promising technique in this context. However, we still need to advance MPC in the face of hydrological uncertainties. This study makes the first...

Time series forecasting based on deep extreme learning machine

Multi-layer Artificial Neural Networks (ANN) has caught widespread attention as a new method for time series forecasting due to the ability of approximating any nonlinear function. In this paper, a new local time series prediction model is established with the nearest neighbor domain theory, in which the hybrid Euclidean distance is used as...