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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Lei He Chunxiao Fu Lin Yang Suisheng Tong Qiang Luo |
| Copyright Year | 2014 |
| Description | Author affiliation: Shanghai Center for Math. Sci., Fudan Univ., Shanghai, China (Qiang Luo) || Coll. of Inf. Syst. & Manage., Nat. Univ. of Defense Technol., Changsha, China (Lei He; Chunxiao Fu; Lin Yang; Suisheng Tong) |
| Abstract | To coordinate the traffic lights for adjacent crossings makes profound challenges to the intelligent control of traffics in modern cities. In particular, the complexity of the model for the multi-crossing traffic is difficult to solve in a time scale acceptable for efficient traffic control. This study proposed to solve the optimization problem of the multi-crossing traffic model by co-evolving particle swarm optimization, and to learn the optimal strategy for multi-crossing traffic control by an artificial neural network (ANN). With this well trained ANN, the realtime control over the multi-crossing traffic lights can be achieved. The systematic simulation results showed us that the proposed approach is a promising control algorithm for the multi-crossing traffic lights. |
| Starting Page | 128 |
| Ending Page | 133 |
| File Size | 682771 |
| Page Count | 6 |
| File Format | |
| ISSN | 21579563 |
| e-ISBN | 9781479951512 |
| DOI | 10.1109/ICNC.2014.6975822 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2014-08-19 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | traffic network Artificial Neural Network Turning Real-time systems Resource management Mathematical model Coordinated optimization algorithm Particle swarm optimization Optimization Biological neural networks time allocation |
| Content Type | Text |
| Resource Type | Article |
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