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| Content Provider | IET Digital Library |
|---|---|
| Author | Chen, Chuan Ming Pi, Dechang Fang, Zhuoran |
| Abstract | A novel clustering algorithm is presented for monitoring and analysing traffic conditions in real-time and automatically. The existing methods concentrate on analysis of traffic flow based on historical information, and they cannot provide timely analysis of traffic conditions. Regarding the vehicles on the roads as data points, a K-means grid-density clustering algorithm is proposed based on an artificial immune network to partition the vehicles data into proper clusters, and marks the densities for monitoring and analysing the traffic conditions. Simulated experimental results show that the proposed algorithm obtains higher efficiency and stability than traditional methods. |
| Starting Page | 1272 |
| Ending Page | 1273 |
| Page Count | 2 |
| ISSN | 00135194 |
| Volume Number | 49 |
| e-ISSN | 1350911X |
| Issue Number | Issue 20, Sep (2013) |
| Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/el/49/20 |
| Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/10.1049/el.2013.2514 |
| Journal | Electronics Letters |
| Access Restriction | Open |
| Rights Holder | © The Institution of Engineering and Technology |
| Subject Keyword | Artificial Immune K Means Grid Density Clustering Algorithm Artificial Immune Network Artificial Immune System Data Handling Technique History Information Monitoring Optimisation Technique Pattern Clustering Pattern Recognition Real Time Monitoring Stability Timely Analysis Traffic Engineering Computing Traffic Flow Urban Traffic Condition Vehicle Data |
| Content Type | Text |
| Resource Type | Article |
| Subject | Electrical and Electronic Engineering |
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