Abstract
In some form or another, model predictive traffic control has been proposed for some decades. Because it predicts future traffic conditions, rather than reacting to past conditions in hindsight, it has advantages over adaptive traffic control techniques. However, it has not been widely adopted for a couple of reasons. Firstly, there are doubts about its suitability when fine-grained traffic data is unavailable. This objection can be addressed with the promise of vehicle-to-infrastructure (V2I) communication. Secondly, previous implementations have found that the control algorithm’s computational complexity is exponential or worse. This paper addresses this objection by introducing the A* algorithm to significantly decrease computation time. Other methods for reducing computation time include setting a suitable prediction horizon, clustering vehicles into platoons and implementing an incremental version of the control algorithm. With these methods combined, it is shown through simulation, that a real-time model predictive control algorithm is practical because computation time is manageable without having an adverse effect on total delay.
| Original language | English |
|---|---|
| Title of host publication | Australasian Transport Research Forum 2017 Proceedings |
| Publisher | Australasian Transport Research Forum |
| Number of pages | 17 |
| Publication status | Published - 2018 |
Cite this
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Balancing computational requirements with performance in model predictive traffic control. / Stebbins, Simon; Hickman, Mark ; Kim, Jiwon; Vu, Hai L.
Australasian Transport Research Forum 2017 Proceedings. Australasian Transport Research Forum, 2018.Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Other
TY - GEN
T1 - Balancing computational requirements with performance in model predictive traffic control
AU - Stebbins, Simon
AU - Hickman, Mark
AU - Kim, Jiwon
AU - Vu, Hai L.
PY - 2018
Y1 - 2018
N2 - In some form or another, model predictive traffic control has been proposed for some decades. Because it predicts future traffic conditions, rather than reacting to past conditions in hindsight, it has advantages over adaptive traffic control techniques. However, it has not been widely adopted for a couple of reasons. Firstly, there are doubts about its suitability when fine-grained traffic data is unavailable. This objection can be addressed with the promise of vehicle-to-infrastructure (V2I) communication. Secondly, previous implementations have found that the control algorithm’s computational complexity is exponential or worse. This paper addresses this objection by introducing the A* algorithm to significantly decrease computation time. Other methods for reducing computation time include setting a suitable prediction horizon, clustering vehicles into platoons and implementing an incremental version of the control algorithm. With these methods combined, it is shown through simulation, that a real-time model predictive control algorithm is practical because computation time is manageable without having an adverse effect on total delay.
AB - In some form or another, model predictive traffic control has been proposed for some decades. Because it predicts future traffic conditions, rather than reacting to past conditions in hindsight, it has advantages over adaptive traffic control techniques. However, it has not been widely adopted for a couple of reasons. Firstly, there are doubts about its suitability when fine-grained traffic data is unavailable. This objection can be addressed with the promise of vehicle-to-infrastructure (V2I) communication. Secondly, previous implementations have found that the control algorithm’s computational complexity is exponential or worse. This paper addresses this objection by introducing the A* algorithm to significantly decrease computation time. Other methods for reducing computation time include setting a suitable prediction horizon, clustering vehicles into platoons and implementing an incremental version of the control algorithm. With these methods combined, it is shown through simulation, that a real-time model predictive control algorithm is practical because computation time is manageable without having an adverse effect on total delay.
UR - http://www.scopus.com/inward/record.url?scp=85073078937&partnerID=8YFLogxK
M3 - Conference Paper
AN - SCOPUS:85073078937
BT - Australasian Transport Research Forum 2017 Proceedings
PB - Australasian Transport Research Forum
ER -