| Reference : A global optimization heuristic for the decomposed static anticipatory network traffi... |
| Scientific journals : Article | |||
| Engineering, computing & technology : Multidisciplinary, general & others | |||
| http://hdl.handle.net/10993/34550 | |||
| A global optimization heuristic for the decomposed static anticipatory network traffic control problem anticipatory network traffic control problem | |
| English | |
Rinaldi, Marco  [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Engineering Research Unit >] | |
| Tampére, Chris [KU Leuven] | |
| Viti, Francesco [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Engineering Research Unit >] | |
| Dec-2017 | |
| Transportation Research Procedia | |
| Elesvier | |
| 20th EURO Working Group on Transportation Meeting, EWGT 2017 | |
| Yes | |
| International | |
| 2352-1465 | |
| Amsterdam | |
| The Netherlands | |
| [en] Traffic control ; User Equilibrium ; Optimization | |
| [en] Developing traffic control strategies taking explicitly into account the route choice behavior of users has been widely recognized
irregularities in the solution space shape, such as non-convexity and non-smoothness. In this work, we propose an extended as a very challenging problem. Furthermore, the inclusion of user behavior in optimization based control schemes introduces strong decomposition scheme for the anticipatory traffic control problem, based upon our previous contributions, which aims at i) reducing irregularities in the solution space shape, such as non-convexity and non-smoothness. In this work, we propose an extended the computational complexity of the problem by approaching it in a controller-by-controller fashion, and ii) internalizing specific decomposition scheme for the anticipatory traffic control problem, based upon our previous contributions, which aims at i) reducing constraints in the objective function, guiding the optimization process away from non-significant minima, such as flat regions. the computational complexity of the problem by approaching it in a controller-by-controller fashion, and ii) internalizing specific Through two small scale test networks and different, randomly chosen initial points, we compare how the proposed extension constraints in the objective function, guiding the optimization process away from non-significant minima, such as flat regions. influences optimization results with respect to our previously developed decomposed approach, as well as centralized schemes. Through two small scale test networks and different, randomly chosen initial points, we compare how the proposed extension influences optimization results with respect to our previously developed decomposed approach, as well as centralized schemes. | |
| http://hdl.handle.net/10993/34550 | |
| 10.1016/j.trpro.2017.12.066 |
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