Abstract
As an important part of Intelligent Transportation System, the scientific traffic signal timing of junction can improve the efficiency of urban transport. This paper presents a novel dynamic traffic signal timing model.
According to the characteristics of the model, hybrid chaotic quantum evolutionary algorithm is employed to solve it. The proposed model has simple structure, and only requires traffic inflow speed and outflow speed are bounded functions with at most finite number of discontinuity points. The condition is very loose and better meets the requirements of the practical real-time and dynamic signal control of junction. To obtain the optimal solution of the model by hybrid chaotic quantum evolutionary algorithm, the model is converted to an easily solvable form. To simplify calculation, we give the expression of the partial derivative and change rate of the objective function such that the implementation of the algorithm only involves function assignments and arithmetic operations and thus avoids complex operations such as integral and differential. Simulation results show that the algorithm has less remain vehicles than Webster method, higher convergence rate and convergence speed than quantum evolutionary algorithm, genetic algorithms and particle swarm optimization.
According to the characteristics of the model, hybrid chaotic quantum evolutionary algorithm is employed to solve it. The proposed model has simple structure, and only requires traffic inflow speed and outflow speed are bounded functions with at most finite number of discontinuity points. The condition is very loose and better meets the requirements of the practical real-time and dynamic signal control of junction. To obtain the optimal solution of the model by hybrid chaotic quantum evolutionary algorithm, the model is converted to an easily solvable form. To simplify calculation, we give the expression of the partial derivative and change rate of the objective function such that the implementation of the algorithm only involves function assignments and arithmetic operations and thus avoids complex operations such as integral and differential. Simulation results show that the algorithm has less remain vehicles than Webster method, higher convergence rate and convergence speed than quantum evolutionary algorithm, genetic algorithms and particle swarm optimization.
| Original language | English |
|---|---|
| Title of host publication | Future Wireless Networks and Information Systems |
| Editors | Ying Zhang |
| Volume | 2 |
| Publisher | Springer |
| Publication date | 2012 |
| Pages | 555-566 |
| ISBN (Print) | 978-3-642-27325-4 |
| ISBN (Electronic) | 978-3-642-27326-1 |
| DOIs | |
| Publication status | Published - 2012 |
| Event | International Conference on Future Wireless Networks and Information Systems, ICFWI - Macao, China Duration: 30 Nov 2011 → 1 Dec 2011 |
Conference
| Conference | International Conference on Future Wireless Networks and Information Systems, ICFWI |
|---|---|
| Country/Territory | China |
| City | Macao |
| Period | 30/11/2011 → 01/12/2011 |
| Series | Lecture Notes in Electrical Engineering |
|---|---|
| Number | 144 |
| ISSN | 1876-1100 |
Bibliographical note
ISSN for LNEE: 1876-1100e-ISSN for LNEE: 1876-1119
Volume title also presented by Springer as "Future Computing, Communication, Control and Management, vol. 2"