Name
Abstract | ||
|---|---|---|
City-scale traffic speed prediction provides significant data foundation for the intelligent transportation system, which enriches commuters with up-to-date information about traffic condition. However, predicting on-road vehicle speed accurately is challenging, as the speed of the vehicle on the urban road is affected by various types of factors. These factors can be categorized into three main aspects, which are temporal, spatial, and other latent information. In this paper, we propose a novel spatio-temporal model named L-U-Net based on U-Net as well as long short-term memory architecture and develop an effective speed prediction model, which is capable of forecasting city-scale traffic conditions. It is worth noting that our model can avoid the high complexity and uncertainty of subjective features extraction and can be easily extended to solve other spatio-temporal prediction problems such as flow prediction. The experimental results demonstrate that the prediction model we proposed can forecast urban traffic speed effectively. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ACCESS.2019.2902185 | IEEE ACCESS |
Keywords | Field | DocType |
Convolutional neural network,long short-term memory neural network,spatio-temporal modeling,traffic speed prediction | Data modeling,Computer science,Flow (psychology),Feature extraction,Real-time computing,Intelligent transportation system,Traffic conditions,Memory architecture,Distributed computing | Journal |
Volume | ISSN | Citations |
7 | 2169-3536 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kun Niu | 1 | 21 | 10.19 |
| Huiyang Zhang | 2 | 2 | 1.04 |
| Tong Zhou | 3 | 448 | 76.83 |
| cheng cheng | 4 | 8 | 6.71 |
| Chao Wang | 5 | 895 | 190.04 |