原文传递
Lane-Level Short-Term Freeway Traffic Volume Prediction Based on Graph Convolutional Recurrent Network
| 题名： | Lane-Level Short-Term Freeway Traffic Volume Prediction Based on Graph Convolutional Recurrent Network |
| 正文语种： | eng |
| 作者： | Lu Liu;Zhiyong Cui;Ruimin Ke;Yinhai Wang |
| 作者单位： | Automotive Transportation Technology Research Center Research Institute of Highway Ministry of Transport Beijing 100088 China;School of Transportation Science and Engineering Beihang Univ. Beijing 100191 China;Dept. of Civil Engineering Univ. of Texas at El Paso El Paso TX 79968;Dept. of Civil and Environmental Engineering Univ. of Washington Seattle WA 98105 |
| 关键词： | Lane-level traffic volume forecasting; Lane-segment graph; Graph neural network; Long short-term memory (LSTM) network |
| 摘要： | The postpandemic period has seen a significant increase in traffic volume on freeways, necessitating the implementation of advanced traffic management systems, such as lane-level freeway tolling systems, to predict traffic patterns and alleviate congestion. Although deep learning models have proven effective in predicting traffic states, little research has focused on lane-level traffic prediction, which is crucial for emerging intelligent transportation applications. To address this gap, this study develops a lane-level road segment graph and proposes a lane-based road network traffic volume prediction model, GCN-LSTM, that combines graph convolution network (GCN) and long short-term memory (LSTM). The proposed model employs different graph Laplacian matrices, and the performance of these corresponding derived models is compared with that of existing traffic prediction models. The proposed model is evaluated using traffic volume data collected from inductive loop detectors installed on freeways in the Seattle area, including both high-occupancy toll lanes and general-purpose lanes. The results demonstrate that the GCN-LSTM model with the combinatorial Laplacian matrix outperforms other models. Additionally, the model's prediction performance remains consistent when using input data with various temporal ranges. Furthermore, excluding high-occupancy toll lane data from the dataset improves the prediction accuracy, highlighting the importance of developing specialized models for lane-level traffic prediction tasks. |
| 出版年： | 2023 |
| 期刊名称： | Journal of Transportation Engineering |
| 卷： | 149 |
| 期： | 10 |
| 页码： | 04023102.1-04023102.11 |
相关文献
- Real-Time Movement-Based Traffic Volume Prediction at Signalized Intersections
- Distribution Analysis and Forecast of Traffic Flow of an Expressway Electronic Toll Collection Lane
- Floating Car Data-Based Short-Term Travel Time Forecasting with Deep Recurrent Neural Networks Incorporating Weather Data
- An Integrated Framework for Real-Time Intelligent Traffic Management of Smart Highways
- A hybrid autoregressive fractionally integrated moving average and nonlinear autoregressive neural network model for short-term traffic flow prediction
- Designing a Novel Two-Stage Fusion Framework to Predict Short-Term Origin-Destination Flow
检索历史
应用推荐
