Abstract
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.
Highlights
Intelligent transportation systems (ITSs) are expected to alleviate traffic problems around the world
This study proposed a hybrid approach by applying genetic algorithm (GA) optimization method to different kinds of neural networks (NNs), such as simple back-propagation multilayer perceptron with ‘‘sigmoid’’ activation function and back-propagation multilayer feed-forward NN with momentum, to optimize network’s architecture
The ability to predict the future values of traffic parameters helps to improve the performance of traffic control systems
Summary
Intelligent transportation systems (ITSs) are expected to alleviate traffic problems around the world. Short-term traffic prediction is a highly researched area within ITS, and the results are used by transportation practitioners to reduce congestion and increase mobility. Efforts in this field started from the application of autoregressive integrated moving average (ARIMA) models and nonparametric techniques for traffic prediction. Basic parametric methods such as ARIMA [1], seasonal autoregressive integrated moving average method (SARIMA) [2] and Kalman filter [3] have been widely used in the literature Developing these algorithms to meet the requirements of current engineering applications has been the subject of many research efforts in the past few decades [4]. Luo et al [5] proposed a hybrid prediction methodology based on improved SARIMA model and multi-input autoregressive (AR) model with genetic algorithm (GA) optimization, in order to provide a better prediction accuracy and reduce the operation time
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