Name
Abstract | ||
|---|---|---|
Traffic congestion prediction is an important precondition to promote urban sustainable development. Nevertheless, there is a lack of a unified prediction method to address the performance metrics, such as accuracy, instantaneity and stability, systematically. In the paper, we propose a novel approach to predict the urban traffic congestion efficiently with floating car trajectory data. Specially, an innovative traffic flow prediction method utilizing particle swarm optimization algorithm is responsible for calculating the traffic flow parameters. Then, a congestion state fuzzy division module is applied to convert the predicted flow parameters to citizens' cognitive congestion states. We conduct extensive experiments with real floating car data and the experimental results show that our proposed method has advantage in terms of accuracy, instantaneity and stability. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/978-3-319-27122-4_2 | ICA3PP |
Field | DocType | Citations |
Particle swarm optimization,Traffic flow,Simulation,Computer science,Floating car data,Fuzzy logic,Real-time computing,Precondition,Traffic congestion reconstruction with Kerner's three-phase theory,Trajectory,Traffic congestion,Distributed computing | Conference | 1 |
PageRank | References | Authors |
0.39 | 11 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qiuyuan Yang | 1 | 34 | 4.32 |
| jinzhong wang | 2 | 102 | 6.94 |
| Ximeng Song | 3 | 1 | 0.39 |
| Xiangjie Kong | 4 | 425 | 46.56 |
| Zhenzhen Xu | 5 | 80 | 11.66 |
| Benshi Zhang | 6 | 1 | 0.39 |