# Meta-Extreme Learning Machine for Short-Term Traffic Flow Forecasting
## 1 Introduction
A novel multi-diffusion convolution block constructed from the diffusion convolution of attention mechanism and bidirectional diffusion convolution was proposed by [PERSON] et al [28]. [PERSON] et al. also proposed a novel long short-term memory (LSTM) network enhanced by temporal-aware convolutional context (TCC) blocks and a new loss-switch mechanism (LSM) in [29]. In addition, [PERSON] et al. proposed making an extension to the LSTM neural network by introducing the attention mechanism into it for short-term traffic flow prediction [30; 31]. The advantages of neural networks include flexibility, strong ability to deal with nonlinear and complex problems, and parallel computing [32; 33; 34], but there are still shortcomings. For example, as a non-parametric method of the ANN, its parameters change with data points. The forecasting accuracy of the ANN is limited by the sample size. Therefore, to train a successful ANN model, a huge amount of raw data is required [35; 36]. Various prediction schemes have been mentioned above, but they have shortcomings. We would like to propose a new model to improve on the shortcomings as well as the accuracy of traffic flow prediction. In summary, the task of traffic flow forecasting is to train a network model \(g\) whose role is to accurately map traffic flow data \(T\) to future traffic flow data \(P\), e.g., \(P=g(T)\). However, there is some difficulty in finding and training the optimal mapping function \(g^{*}\) that is adapted to all datasets and achieves the best performance. In order to address these issues, we rethink the potential of evolutionary algorithms to develop models that embed a model capable of automatic optimization in a traffic flow prediction model, resulting in a new hybrid model. For example, a model \(g\) that automatically determines the best parameters in a basic forecasting model \(G\) of traffic flow data is expressed as \(g^{*}=G(X,g)\). In this case, we take into account the full potential of optimization algorithms and obtain an effective model that can be optimized in a data-driven fashion over and over again by a simple hybrid of optimization algorithms and forecasting models. In this paper, we develop a short-term traffic flow forecasting model called ABC-ELM, employing the artificial bee colony (ABC) algorithm model embedded into the ELM model. The ELM was first proposed in 2004 by [PERSON] et al. [37]. In the ELM model, during initialization the generation of input weights and hidden layer deviations are random; then, the excitation function is calculated through the input data. The Moore-Penrose (MP)generalized calculation inverse calculates the output matrix weights [38]. This characteristic not only makes it much faster than traditional learning models, but also avoids problems in traditional learning models, such as falling into a local optimum, learning rate too large or too small, etc. [39]. However, it also has drawbacks as it is randomly determined that the prediction accuracy of the destined ELM will not be particularly good, and there is an overfitting problem. To solve this problem, we propose embedding and incorporating the ABC algorithm model in the ELM. The ABC algorithm selects the optimal parameters in the process of random parameter determination in the ELM. Currently, ABC is mostly used to optimize training neural networks, such as the hybrid algorithm [PERSON] et al. proposed in [40], which combines -the ABC algorithm and the Levenberg-Marquardt (LM) algorithm, after which the hybrid algorithm is used to train an ANN. In this way, based on the experimental results of predecessors, we do not need to increase or delete the parameters of ABC-ELM to improve the accuracy of the model and at the same time prevent the overfitting problem caused by too many parameters.