| DOI | Trouver le DOI : https://doi.org/10.1145/3557915.3561029 |
|---|
| Auteur | Rechercher : Ramhormozi, Reza Safarzadeh; Rechercher : Mozhdehi, Arash; Rechercher : Kalantari, Saeid; Rechercher : Wang, Yunli1; Rechercher : Sun, Sun1; Rechercher : Wang, Xin |
|---|
| Affiliation | - Conseil national de recherches du Canada. Technologies numériques
|
|---|
| Format | Texte, Article |
|---|
| Conférence | SIGSPATIAL '22: The 30th International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL GIS 2022), Nov. 1-4, 2022, Seattle, Washington |
|---|
| Sujet | truck speed prediction; multi task learning; spatiotemporal models; extreme weather; truck traffic forecasting |
|---|
| Résumé | Truck speed prediction plays a key role in truck transportation management. However, it is a very challenging task since the truck traffic usually shows complex patterns. Most of the existing traffic prediction methods lack the ability to model the dynamic spatial-temporal correlations of truck traffic or ignore contributing contextual factors that impact traffic. Also, truck traffic data is typically sparse and noisy, which makes the truck speed prediction an even more challenging task. How to improve the truck speed prediction by taking advantage of other relevant truck traffic information (such as the truck flow) has not been investigated in depth. Additionally, traffic congestions and poor driving conditions caused by extreme weather conditions can make sudden changes in the general pattern of the truck speed. In this paper, we propose a novel Multi-Task Context Based Gated Recurrent Unit Graph Convolutional Network (MT-C2G) to predict the truck speed under extreme weather conditions. MT-C2G includes four major components: The spatial dependence learning component captures the spatial dependencies shaped by the topological structure of the road network. Truck traffic feature temporal dependence modeling component is built to acquire the temporal dependencies involved in the truck traffic features, and contextual feature temporal dependence modeling component employs a layer of GRU units to capture the temporal dependencies of contextual factors. The multi-task learning component then leverages the information between the truck speed and flow prediction tasks through attention mechanism for improving the performance. Moreover, a data augmentation method SMOTE is utilized to balance the data with the extreme weather conditions. Experiments on two real datasets demonstrate that the proposed MT-C2G fairly outperforms six state-of-the-art traffic prediction methods. |
|---|
| Date de publication | 2022-11-22 |
|---|
| Maison d’édition | ACM |
|---|
| Dans | |
|---|
| Langue | anglais |
|---|
| Publications évaluées par des pairs | Oui |
|---|
| Exporter la notice | Exporter en format RIS |
|---|
| Signaler une correction | Signaler une correction (s'ouvre dans un nouvel onglet) |
|---|
| Identificateur de l’enregistrement | dfcbac75-13f5-4d40-8683-e7acc6d92e20 |
|---|
| Enregistrement créé | 2022-11-24 |
|---|
| Enregistrement modifié | 2022-11-24 |
|---|
