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A hybrid autoregressive fractionally integrated moving average and nonlinear autoregressive neural network model for short-term traffic flow prediction
| 题名： | A hybrid autoregressive fractionally integrated moving average and nonlinear autoregressive neural network model for short-term traffic flow prediction |
| 正文语种： | eng |
| 作者： | Xuecai Xu;Xiaofei Jin;Daiquan Xiao;Changxi Ma;S. C. Wong |
| 作者单位： | School of Civil and Hydraulic Engineering Huazhong University of Science and Technology Wuhan China;Shanghai Municipal Engineering Design Institute (Group) Co. Ltd. (Wuhan Branch) Wuhan China;School of Civil and Hydraulic Engineering Huazhong University of Science and Technology Wuhan China;School of Traffic and Transportation Engineering Lanzhou Jiaotong University Lanzhou China;Department of Transportation Engineering University of Hong Kong Pokfulam Road Hong Kong China |
| 关键词： | ARFIMA model; hybrid model; NAR neural network; short-term traffic flow prediction |
| 摘要： | Intelligent traffic control and guidance system is an effective way to solve urban traffic congestion, improve road capacity and guarantee drivers' travel safety, while short-term traffic flow prediction is the core of intelligent traffic control and guidance system. To investigate the long-term memory and the dynamic feature of short-time traffic flow time series, a hybrid model was proposed by integrating autoregressive fractionally integrated moving average (ARFIMA) model and nonlinear autoregressive (NAR) neural network model to predict short-time traffic flow, in which ARFIMA model can address the long-term memory of linear component and NAR neural network can accommodate the dynamic feature of nonlinear residual component. First, the ARFIMA model was employed to predict the linear component of traffic flow, and the results were compared with those of autoregressive integrated moving average (ARIMA) model. Next, the NAR neural network model was adopted to forecast the nonlinear residual components, and the weighted results were considered as the predicted flow of the hybrid model. The proposed hybrid model was validated by using the cross-sectional traffic flow data in California freeways obtained from the open-access PeMS database. The results showed that the ARFIMA model considering the long-term memory can effectively predict the short-term traffic flow, and the prediction accuracy of the hybrid model is better than that of the singular models. The findings provide an alternative for the short-term traffic flow prediction with lower error and higher accuracy. |
| 出版年： | 2023 |
| 期刊名称： | Journal of Intelligent Transportation Systems |
| 卷： | 27 |
| 期： | 1/6 |
| 页码： | 1-18 |
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