| Title | APPLICATION OF HYBRID DEEP LEARNING MODELS IN FREEWAY MULTI-PERIOD TRAFFIC SPEEDS PREDICTION |
| Author | Zih-Hua Wu, Jau-Ming Su, Liang-Tay Lin, Chien-Yen Chang, Pai-Hsien Hung, Tong-Ling Wu, Yu-Fen Ho |
| Summary | Traffic speed prediction plays a crucial role in traffic congestion mitigation. With the advent of deep learning, transportation researchers are empowered to forecast traffic with an unprecedented accuracy. However, in the context of short-term prediction, such accuracy fails to hold consistent at hourly level; In particular, at peak hour periods. To bridge the gap, this study proposes a novel hybrid model that integrates LSTM, Attention Mechanism, and Bi-LSTM to for traffic prediction in six scenarios: “One week-24 hours”, “One week-Peak hours”, “Weekday-24 hours”, “Weekday - Peak hours”, “Weekend-24 hours”, “Weekend-Peak hours”. By leveraging the strengths of each constituent model, the proposed model demonstrates its capability of capturing peak hour traffic patterns as compared to previous models. |
| Vol. | 53 |
| No. | 4 |
| Page | 281 |
| Year | 2024 |
| Month | 12 |
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