| Title |
Efficient Bus Arrival Time Prediction Based on Spark Streaming Platform |
| ID_Doc |
41546 |
| Authors |
Liu, J; Xiao, GF |
| Title |
Efficient Bus Arrival Time Prediction Based on Spark Streaming Platform |
| Year |
2019 |
| Published |
|
| DOI |
10.1109/cscwd.2019.8791859 |
| Abstract |
In smart city development, the prediction of bus arrival time is a popular research issue, which often uses GPS data and other related bus data to conduct collaborative data analysis. It is of great importance for improving the public transportation services. But the accuracy and the efficiency of bus arrival time prediction is still the major obstacles. In this paper, an optimized particle-filtering algorithm is used to establish a bus arrival time prediction model. To better solve the problem of prediction error and particle optimization in the process of using particle filter algorithms, the prediction model is improved by introducing the latest bus speed for collaborative data analysis, which improves the accuracy of the bus arrival time prediction based on the actual road conditions and can simultaneously predict the arrival time of multiple buses. Based on the above model and the Spark streaming platform, a real-time bus arrival time prediction software system is implemented. The experimental results show that our proposed model and system can accurately predict the bus arrival time and then well promote the bus travel experience for citizens. |
| Author Keywords |
Bus arrival time prediction; particle filter algorithm; Spark streaming; smart city |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Conference Proceedings Citation Index - Science (CPCI-S) |
| EID |
WOS:000502581200070 |
| WoS Category |
Computer Science, Interdisciplinary Applications |
| Research Area |
Computer Science |
| PDF |
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