E-viri
Recenzirano
-
Zhang, Xu; Wen, Shunjie; Yan, Liang; Feng, Jiangfan; Xia, Ying
Computer journal, 01/2024, Letnik: 67, Številka: 1Journal Article
Abstract Accurate traffic flow prediction is valuable for satisfying citizens’ travel needs and alleviating urban traffic pressure. However, it is highly challenging due to the complexity of the urban geospatial structure and the highly nonlinear temporal and spatial dependence on human mobility. Most existing works proposed to rely on strict periods (e.g. daily and weekly) and separate the extraction of temporal and spatial features. Besides, most Recurrent Neural Network (RNN)-based models either fail to capture variations of spatial–temporal features in adjacent timestamps or ignore details of closeness. In this paper, we propose a Multi-attention based Hybrid-convolution Spatial-temporal Recurrent Network (MHSRN) for region-based traffic flow prediction. In MHSRN, we leverage a hybrid-convolution module to capture both shifting features and rich information at the nearest timestamps, and we apply the downsampling procedure to reduce the computation of RNN-based model. Furthermore, we propose to adopt a space-aware multi-attention module to re-perceive global and local spatial–temporal features. We conduct extensive experiments based on three real-world datasets. The results show that the MHSRN outperforms other challenging baselines by approximately 0.2–8.1% in mean absolute error on all datasets. On datasets other than TaxiBJ, the MHSRN reduces the root mean square error by at least 2.8% compared with the RNN-based model.
![loading ... loading ...](themes/default/img/ajax-loading.gif)
Vnos na polico
Trajna povezava
- URL:
Faktor vpliva
Dostop do baze podatkov JCR je dovoljen samo uporabnikom iz Slovenije. Vaš trenutni IP-naslov ni na seznamu dovoljenih za dostop, zato je potrebna avtentikacija z ustreznim računom AAI.
Leto | Faktor vpliva | Izdaja | Kategorija | Razvrstitev | ||||
---|---|---|---|---|---|---|---|---|
JCR | SNIP | JCR | SNIP | JCR | SNIP | JCR | SNIP |
Baze podatkov, v katerih je revija indeksirana
Ime baze podatkov | Področje | Leto |
---|
Povezave do osebnih bibliografij avtorjev | Povezave do podatkov o raziskovalcih v sistemu SICRIS |
---|
Vir: Osebne bibliografije
in: SICRIS
To gradivo vam je dostopno v celotnem besedilu. Če kljub temu želite naročiti gradivo, kliknite gumb Nadaljuj.