Estimator: An Effective and Scalable Framework for Transportation Mode Classification over Trajectories

Transportation mode classification, the process of predicting the class labels of moving objects transportation modes, has been widely applied to a variety of real world applications, such as traffic management, urban computing, and behavior study. However, existing studies of transportation mode classification typically extract the explicit features of trajectory data but fail to capture the implicit features that affect the classification performance. In addition, most of the existing studies also prefer to apply RNN-based models to embed trajectories, which is only suitable for classifying small-scale data. To tackle the above challenges, we propose an effective and scalable framework for transportation mode classification over GPS trajectories, abbreviated Estimator. Estimator is established on a developed CNN-TCN architecture, which is capable of leveraging the spatial and temporal hidden features of trajectories to achieve high effectiveness and efficiency. Estimator partitions the entire traffic space into disjointed spatial regions according to traffic conditions, which enhances the scalability significantly and thus enables parallel transportation classification. Extensive experiments using eight public real-life datasets offer evidence that Estimator i) achieves superior model effectiveness (i.e., 99% Accuracy and 0.98 F1-score), which outperforms state-of-the-arts substantially; ii) exhibits prominent model efficiency, and obtains 7-40x speedups up over state-of-the-arts learning-based methods; and iii) shows high model scalability and robustness that enables large-scale classification analytics.Comment: 12 pages, 8 figure

Flood prediction using deep learning models

Deep learning has recently appeared as one of the best reliable approaches for forecasting time series. Even though there are numerous data-driven models for flood prediction, most studies focus on prediction using a single flood variable. The creation of various data-driven models may require unfeasible computing resources when estimating multiple flood variables. Furthermore, the trends of several flood variables can only be revealed by analysing long-term historical observations, which conventional data-driven models do not adequately support. This study proposed a time series model with layer normalization and Leaky ReLU activation function in multivariable long-term short memory (LSTM), bidirectional long-term short memory (BILSTM) and deep recurrent neural network (DRNN). The proposed models were trained and evaluated by using the sensory historical data of river water level and rainfall in the east coast state of Malaysia. It were then, compared to the other six deep learning models. In terms of prediction accuracy, the experimental results also demonstrated that the deep recurrent neural network model with layer normalization and Leaky ReLU activation function performed better than other models

Long text analysis using sliced recurrent neural networks with breaking point information enrichment

Sliced recurrent neural networks (SRNNs) are the state-of-the-art efficient solution for long text analysis tasks; however, their slicing operations inevitably result in long-term dependency loss in lower-level networks and thus limit their accuracy. Therefore, we propose a breaking point information enrichment mechanism to strengthen dependencies between sliced subsequences without hindering parallelization. Then, the resulting BPIE-SRNN model is further extended to a bidirectional model, BPIE-BiSRNN, to utilize the dependency information in not only the previous but also the following contexts. Experiments on four large public real-world datasets demonstrate that the BPIE-SRNN and BPIE-BiSRNN models always achieve a much better accuracy than SRNNs and BiSRNNs, while maintaining a superior training efficiency

Long text analysis using sliced recurrent neural networks with breaking point information enrichment

Sliced recurrent neural networks (SRNNs) are the state-of-the-art efficient solution for long text analysis tasks; however, their slicing operations inevitably result in long-term dependency loss in lower-level networks and thus limit their accuracy. Therefore, we propose a breaking point information enrichment mechanism to strengthen dependencies between sliced subsequences without hindering parallelization. Then, the resulting BPIE-SRNN model is further extended to a bidirectional model, BPIE-BiSRNN, to utilize the dependency information in not only the previous but also the following contexts. Experiments on four large public real-world datasets demonstrate that the BPIE-SRNN and BPIE-BiSRNN models always achieve a much better accuracy than SRNNs and BiSRNNs, while maintaining a superior training efficiency

A Novel and Robust Wind Speed Prediction Method Based on Spatial Features of Wind Farm Cluster

Wind energy has been widely used in recent decades to achieve green and sustainable development. However, wind speed prediction in wind farm clusters remains one of the less studied areas. Spatial features of cluster data of wind speed are not fully exploited in existing work. In addition, missing data, which dramatically deteriorate the forecasting performance, have not been addressed thoroughly. To tackle these tough issues, a new method, termed input set based on wind farm cluster data–deep extreme learning machine (IWC-DELM), is developed herein. This model builds an input set based on IWC, which takes advantage of the historical data of relevant wind farms to utilize the spatial characteristics of wind speed sequences within such wind farm clusters. Finally, wind speed prediction is obtained after the training of DELM, which results in a better performance in forecasting accuracy and training speed. The structure IWC, complete with the multidimensional average method (MDAM), is also beneficial to make up the missing data, thus enhancing data robustness in comparison to the traditional method of the moving average approach (MAA). Experiments are conducted with some real-world data, and the results of gate recurrent unit (GRU), long- and short-term memory (LSTM) and sliced recurrent neural networks (SRNNs) are also taken for comparison. These comparative tests clearly verify the superiority of IWC-DELM, whose accuracy and efficiency both rank at the top among the four candidates

A Novel and Robust Wind Speed Prediction Method Based on Spatial Features of Wind Farm Cluster

Wind energy has been widely used in recent decades to achieve green and sustainable development. However, wind speed prediction in wind farm clusters remains one of the less studied areas. Spatial features of cluster data of wind speed are not fully exploited in existing work. In addition, missing data, which dramatically deteriorate the forecasting performance, have not been addressed thoroughly. To tackle these tough issues, a new method, termed input set based on wind farm cluster data–deep extreme learning machine (IWC-DELM), is developed herein. This model builds an input set based on IWC, which takes advantage of the historical data of relevant wind farms to utilize the spatial characteristics of wind speed sequences within such wind farm clusters. Finally, wind speed prediction is obtained after the training of DELM, which results in a better performance in forecasting accuracy and training speed. The structure IWC, complete with the multidimensional average method (MDAM), is also beneficial to make up the missing data, thus enhancing data robustness in comparison to the traditional method of the moving average approach (MAA). Experiments are conducted with some real-world data, and the results of gate recurrent unit (GRU), long- and short-term memory (LSTM) and sliced recurrent neural networks (SRNNs) are also taken for comparison. These comparative tests clearly verify the superiority of IWC-DELM, whose accuracy and efficiency both rank at the top among the four candidates