arXiv · · Significant research
The paper introduces a novel method for short-term, high-resolution traffic prediction, modeling it as a matrix completion problem solved via block-coordinate descent. An ensemble learning approach is used to capture periodic patterns and reduce training error. The method is validated using both simulated and real-world traffic data from Abu Dhabi, demonstrating superior performance compared to other algorithms.
traffic prediction · matrix completion · ensemble learning · Abu Dhabi · high-resolution data
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arXiv ·
Researchers developed a data-driven toolkit for short-term traffic forecasting using high-resolution traffic data from urban road sensors. The method models forecasting as a matrix completion problem, mapping inputs to a higher-dimensional space using kernels and adaptive boosting. Validated using real-world data from Abu Dhabi, UAE, the method outperforms state-of-the-art algorithms.
arXiv ·
Researchers propose a spatio-temporal model for high-resolution wind forecasting in Saudi Arabia using Echo State Networks and stochastic partial differential equations. The model reduces spatial information via energy distance, captures dynamics with a sparse recurrent neural network, and reconstructs data using a non-stationary stochastic partial differential equation approach. The model achieves more accurate forecasts of wind speed and energy, potentially saving up to one million dollars annually compared to existing models.
arXiv ·
Researchers at MBZUAI have introduced a novel approach to enhance Large Multimodal Models (LMMs) for autonomous driving by integrating 3D tracking information. This method uses a track encoder to embed spatial and temporal data, enriching visual queries and improving the LMM's understanding of driving scenarios. Experiments on DriveLM-nuScenes and DriveLM-CARLA benchmarks demonstrate significant improvements in perception, planning, and prediction tasks compared to baseline models.
arXiv ·
A novel wind speed forecasting (WSF) framework is proposed combining Wavelet Packet Decomposition (WPD), Seasonal Adjustment Method (SAM), and Bidirectional Long Short-term Memory (BiLSTM). The SAM method eliminates the seasonal component of the decomposed subseries generated by WPD to reduce forecasting complexity. The model was tested on five years of hourly wind speed observations acquired from the Dumat Al-Jandal wind farm in Al-Jouf, Saudi Arabia, achieving high forecasting accuracy.