Short-term prediction of traffic flow is one of the most essential elements of all proactive traffic control systems. The aim of this paper is to provide a model based on neural networks (NNs) for multi-step-ahead traffic prediction. NNs’ dependency on parameter setting is the major challenge in using them as a predictor. Given the fact that the best combination of NN parameters results in the minimum error of predicted output, the main problem is NN optimization. So, it is viable to set the best combination of the parameters according to a specific traffic behavior. On the other hand, an automatic method—which is applicable in general cases—is strongly desired to set appropriate parameters for neural networks. This paper defines a self-adjusted NN using the non-dominated sorting genetic algorithm II (NSGA-II) as a multi-objective optimizer for short-term prediction. NSGA-II is used to optimize the number of neurons in the first and second layers of the NN, learning ratio and slope of the activation function. This model addresses the challenge of optimizing a multi-output NN in a self-adjusted way. Performance of the developed network is evaluated by application to both univariate and multivariate traffic flow data from an urban highway. Results are analyzed based on the performance measures, showing that the genetic algorithm tunes the NN as well without any manually pre-adjustment. The achieved prediction accuracy is calculated with multiple measures such as the root mean square error (RMSE), and the RMSE value is 10 and 12 in the best configuration of the proposed model for single and multi-step-ahead traffic flow prediction, respectively.

中文翻译：

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使用自调整进化神经网络的交通量预测

对交通流量的短期预测是所有主动交通控制系统中最重要的元素之一。本文的目的是提供一种基于神经网络的模型，用于多步提前交通预测。神经网络对参数设置的依赖是将其用作预测变量的主要挑战。鉴于NN参数的最佳组合导致预测输出的最小误差这一事实，主要问题是NN优化。因此，可以根据特定的流量行为设置参数的最佳组合。另一方面，强烈要求一种自动方法（适用于一般情况）为神经网络设置适当的参数。本文使用非支配排序遗传算法II（NSGA-II）作为一种用于短期预测的多目标优化程序，定义了一种自我调整的NN。NSGA-II用于优化NN第一层和第二层中神经元的数量，学习率和激活函数的斜率。该模型解决了以自调整方式优化多输出NN的挑战。通过将其应用于城市高速公路的单变量和多变量交通流数据，可以评估发达网络的性能。根据性能指标对结果进行了分析，表明遗传算法也可以对NN进行调整，而无需进行任何手动预调整。可以通过多种方法（例如均方根误差（RMSE），