Multi-Task Fusion Deep Learning Model for Short-Term Intersection Operation Performance Forecasting
| Autor: | Xuedong Yan, Fengxiao Li, Shurong Li, Deqi Chen, Liwei Wang, Xiaobing Liu |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
grid model
Computer science intersections Science Big data 010501 environmental sciences computer.software_genre 01 natural sciences Fuzzy logic Bottleneck multi-task fusion deep learning model Intersection 0502 economics and business Cluster analysis floating car data 0105 earth and related environmental sciences 050210 logistics & transportation business.industry Deep learning 05 social sciences Floating car data Grid General Earth and Planetary Sciences Artificial intelligence Data mining business computer |
| Zdroj: | Remote Sensing Volume 13 Issue 10 Remote Sensing, Vol 13, Iss 1919, p 1919 (2021) |
| ISSN: | 2072-4292 |
| DOI: | 10.3390/rs13101919 |
| Popis: | Urban road intersection bottleneck has become an important factor in causing traffic delay and restricting traffic efficiency. It is essential to explore the prediction of the operating performance at intersections in real-time and formulate corresponding strategies to alleviate intersection delay. However, because of the sophisticated intersection traffic condition, it is difficult to capture the intersection traffic Spatio-temporal features by the traditional data and prediction methods. The development of big data technology and the deep learning model provides us a good chance to address this challenge. Therefore, this paper proposes a multi-task fusion deep learning (MFDL) model based on massive floating car data to effectively predict the passing time and speed at intersections over different estimation time granularity. Moreover, the grid model and the fuzzy C-means (FCM) clustering method are developed to identify the intersection area and derive a set of key Spatio-temporal traffic parameters from floating car data. In order to validate the effectiveness of the proposed model, the floating car data from ten intersections of Beijing with a sampling rate of 3s are adopted for the training and test process. The experiment result shows that the MFDL model enables us to capture the Spatio-temporal and topology feature of the traffic state efficiently. Compared with the traditional prediction method, the proposed model has the best prediction performance. The interplay between these two targeted prediction variables can significantly improve prediction accuracy and efficiency. Thereby, this method predicts the intersection operation performance in real-time and can provide valuable insights for traffic managers to improve the intersection’s operation efficiency. |
| Databáze: | OpenAIRE |
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