| Autor: |
Feng, Rui, Ma, Ankun, Jing, Zhijia, Gu, Xiaoning, Dang, Pengfei, Yao, Baozhen |
| Zdroj: |
Annals of Operations Research; Dec2024, Vol. 343 Issue 3, p1045-1067, 23p |
| Abstrakt: |
Accurate travel time information is essential for logistics vehicles to reserve the most suitable parking lot in logistics centers. The purpose of this study is to explore how the uncertainty of traffic time prediction affects parking lot reservation near logistics centers. A hybrid model integrating convolutional long short-term memory network and attention mechanism is proposed to provide the reliable information for travel time prediction intervals. Furthermore, a reliability-based parking lot reservation model is developed by explicitly considering logistics vehicles' time probability. Several benchmark models are compared with the proposed traffic speed prediction model. The performance of the parking lot reservation model is illustrated by travel behavior questionnaire data and global positioning system data of collected from Beijing, China. The results illustrate that the proposed prediction model exhibits a better accuracy than benchmark models. Moreover, it is found that travel time prediction interval can improve the reliability and stability of travel time, and provide a reliable time information for the parking lot reservation. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
|
Full text from SpringerLink