Short‐term traffic travel time forecasting using ensemble approach based on long short‐term memory networks
| Title: | Short‐term traffic travel time forecasting using ensemble approach based on long short‐term memory networks |
|---|---|
| Authors: | Xingli Jia, Wuxiao Zhou, Hongzhi Yang, Shuangqing Li, Xingpeng Chen |
| Source: | IET Intelligent Transport Systems, Vol 17, Iss 6, Pp 1262-1273 (2023) |
| Publisher Information: | Wiley, 2023. |
| Publication Year: | 2023 |
| Collection: | LCC:Transportation engineering LCC:Electronic computers. Computer science |
| Subject Terms: | data analysis, intelligent transportation systems, learning (artificial intelligence), transportation, Transportation engineering, TA1001-1280, Electronic computers. Computer science, QA75.5-76.95 |
| More Details: | Abstract To enhance the control effect of intelligent transportation system (ITS) of expressway, avoid blind travel causing traffic congestion, improve road capacity, and achieve the purpose of smooth and efficient operation of the network, 3σ criterion and 2σ cycle removal criterion were used to eliminate the abnormal data of travel time. Thereafter, a combined prediction model of Long Short‐Term Memory (LSTM) based on the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) algorithm and attention mechanism is proposed. Travel time data, collected from the real world, is decomposed into several Intrinsic Mode Functions (IMFs) and a residue with the CEEMDAN. Then, the decomposed sub‐sequence is predicted and the final prediction result is obtained by comprehensive superposition. The results indicate that: the CEEMDAN algorithm has significantly improved the prediction performance. Compared with the AT‐LSTM model, mean absolute percentage error (MAPE) is reduced by 10.75%, 24.84%, 31.80%, 24.98%, 14.29%, and 31.42% when the time window was 3, 6, 9, 12, 15, and 18, respectively, in the dataset of Yaxi expressway. In the dataset of Chengnan expressway, the number of error points with MAPE greater than 40% is significantly reduced after the adoption of the LSTM model. After the introduction of CEEMDAN algorithm, the error at each time point is controlled within 30%. The proposed model controls the error at each time point within 25%. In different datasets, the proposed model has better prediction ability for highly non‐linear travel time, and can better capture the trend abrupt characteristics of rush hour traffic flow. |
| Document Type: | article |
| File Description: | electronic resource |
| Language: | English |
| ISSN: | 1751-9578 1751-956X |
| Relation: | https://doaj.org/toc/1751-956X; https://doaj.org/toc/1751-9578 |
| DOI: | 10.1049/itr2.12331 |
| Access URL: | https://doaj.org/article/a2f43433e2d7441da82109947abe7e40 |
| Accession Number: | edsdoj.2f43433e2d7441da82109947abe7e40 |
| Database: | Directory of Open Access Journals |
| ISSN: | 17519578 1751956X |
|---|---|
| DOI: | 10.1049/itr2.12331 |
| Published in: | IET Intelligent Transport Systems |
| Language: | English |