Ιδρυματικό Αποθετήριο
Πολυτεχνείο Κρήτης
Πολυτεχνείο Κρήτης
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| URI | http://purl.tuc.gr/dl/dias/3DB1185E-0097-4479-BD69-36AAC483DCEC | - |
| Αναγνωριστικό | https://doi.org/10.1016/j.trc.2023.104364 | - |
| Αναγνωριστικό | https://www.sciencedirect.com/science/article/pii/S0968090X23003546 | - |
| Γλώσσα | en | - |
| Μέγεθος | 21 pages | en |
| Τίτλος | Modified dynamic programming algorithms for GLOSA systems with stochastic signal switching times | en |
| Δημιουργός | Typaldos Panagiotis | en |
| Δημιουργός | Τυπαλδος Παναγιωτης | el |
| Δημιουργός | Papageorgiou Markos | en |
| Δημιουργός | Παπαγεωργιου Μαρκος | el |
| Εκδότης | Elsevier | en |
| Περιγραφή | The research leading to these results has received funding from the European Research Council under the European Union’s Horizon 2020 Research and Innovation programme/ ERC Grant Agreement n. [833915], project TrafficFluid. | en |
| Περίληψη | A discrete-time stochastic optimal control problem was recently proposed to address the GLOSA (Green Light Optimal Speed Advisory) problem in cases where the next signal switching time is decided in real time and is therefore uncertain in advance. The corresponding numerical solution via SDP (Stochastic Dynamic Programming) calls for substantial computation time, which excludes problem solution in the vehicle’s on-board computer in real time. In this context, the present paper concentrates on the challenge of developing numerical algorithms to solve efficiently the stochastic GLOSA problem. As a first attempt to overcome the computation time bottleneck, a modified version of Dynamic Programming, known as Discrete Differential Dynamic Programming (DDDP) was recently employed for the numerical solution of the stochastic optimal control problem and was demonstrated to achieve results equivalent to those obtained with the ordinary SDP algorithm, albeit with significantly reduced computation times. After an outline of the DDDP approach, the present work considers a second modified version of Dynamic Programming, known as Differential Dynamic Programming (DDP). For the stochastic GLOSA problem, it is demonstrated that DDP achieves quasi-instantaneous (extremely fast) solutions in terms of CPU times, which allows for the proposed approach to be readily executable online, in an MPC (Model Predictive Control) framework, in the vehicle’s on-board computer. The novel numerical approach is tested and demonstrated by use of realistic examples and is compared to the SDP and DDDP solutions. It should be noted that DDP does not require discretization of variables, hence the obtained solutions may be slightly superior to the standard SDP solutions. | en |
| Τύπος | Peer-Reviewed Journal Publication | en |
| Τύπος | Δημοσίευση σε Περιοδικό με Κριτές | el |
| Άδεια Χρήσης | http://creativecommons.org/licenses/by-nc-nd/4.0/ | en |
| Ημερομηνία | 2024-06-28 | - |
| Ημερομηνία Δημοσίευσης | 2023 | - |
| Θεματική Κατηγορία | GLOSA | en |
| Θεματική Κατηγορία | Stochastic Dynamic Programming | en |
| Θεματική Κατηγορία | Discrete Differential Dynamic Programming (DDDP) | en |
| Θεματική Κατηγορία | Differential Dynamic Programming (DDP) | en |
| Θεματική Κατηγορία | Traffic light advisory | en |
| Βιβλιογραφική Αναφορά | P. Typaldos and M. Papageorgiou, “Modified dynamic programming algorithms for GLOSA systems with stochastic signal switching times,” Transp. Res. Part C Emerging Technol., 2023, doi: 10.1016/j.trc.2023.104364. | en |
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