| Title |
Applying Deep Recurrent Neural Network to Predict Vehicle Mobility |
| ID_Doc |
43727 |
| Authors |
Liu, W; Shoji, Y |
| Title |
Applying Deep Recurrent Neural Network to Predict Vehicle Mobility |
| Year |
2018 |
| Published |
|
| DOI |
|
| Abstract |
Sensing data gathering and dissemination is one of the most challenging tasks in smart city construction, and vehicles moving around a city have been widely considered as a good candidate to deliver data efficiently and economically. Hence, this paper proposes a deep recurrent neural network-based algorithm to predict vehicle mobility and facilitate vehicle-based sensing data delivery. Extensive evaluations have been conducted by using a large-scale taxi mobility dataset that is obtained from a smart city testbed deployed in Tokyo, Japan. The results have validated that, compared with the most state-of-art algorithms, our proposal can improve the F1-Score of vehicle mobility prediction by a range of 18.3% similar to 24.6%. |
| Author Keywords |
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| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Conference Proceedings Citation Index - Science (CPCI-S) |
| EID |
WOS:000458719700025 |
| WoS Category |
Computer Science, Hardware & Architecture; Engineering, Electrical & Electronic |
| Research Area |
Computer Science; Engineering |
| PDF |
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