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Model based urban traffic control, part II : coordinated model predictive controllers

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Abstract
The present paper describes how to use coordination between neighbouring intersections in order to improve the performance of urban traffic controllers. Both the local MPC (LMPC) introduced in the companion paper (Hao et al., 2018) and the coordinated MPC (CMPC) introduced in this paper use the urban cell transmission model (UCTM) (Hao et al., 2018) in order to predict the average delay of vehicles in the upstream links of each intersection, for different scenarios of switching times of the traffic lights at that intersection. The feedback controller selects the next switching times of the traffic light corresponding to the shortest predicted average delay. While the local MPC (Hao et al., 2018) only uses local measurements of traffic in the links connected to the intersection in comparing the performance of different scenarios, the CMPC approach improves the accuracy of the performance predictions by allowing a control agent to exchange information about planned switching times with control agents at all neighbouring intersections. Compared to local MPC the offline information on average flow rates from neighbouring intersections is replaced in coordinated MPC by additional online information on when the neighbouring intersections plan to send vehicles to the intersection under control. To achieve good coordination planned switching times should not change too often, hence a cost for changing planned schedules from one decision time to the next decision time is added to the cost function. In order to improve the stability properties of CMPC a prediction of the sum of squared queue sizes is used whenever some downstream queues of an intersection become too long. Only scenarios that decrease this sum of squares of local queues are considered for possible implementation. This stabilization criterion is shown experimentally to further improve the performance of our controller. In particular it leads to a significant reduction of the queues that build up at the edges of the traffic region under control. We compare via simulation the average delay of vehicles travelling on a simple 4 by 4 Manhattan grid, for traffic lights with pre-timed control, traffic lights using the local MPC controller (Hao et al., 2018), and coordinated MPC (with and without the stabilizing condition). These simulations show that the proposed CMPC achieves a significant reduction in delay for different traffic conditions in comparison to these other strategies.
Keywords
Urban traffic control Cell transmission model Model predictive control Distributed control Coordination control, SIGNAL CONTROL, NETWORK

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Citation

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MLA
Zhenzhen, Hao, et al. “Model Based Urban Traffic Control, Part II : Coordinated Model Predictive Controllers.” TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES, vol. 97, 2018, pp. 23–44.
APA
Zhenzhen, H., Boel, R., & Zhiwu, L. (2018). Model based urban traffic control, part II : coordinated model predictive controllers. TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES, 97, 23–44.
Chicago author-date
Zhenzhen, Hao, René Boel, and Li Zhiwu. 2018. “Model Based Urban Traffic Control, Part II : Coordinated Model Predictive Controllers.” TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES 97: 23–44.
Chicago author-date (all authors)
Zhenzhen, Hao, René Boel, and Li Zhiwu. 2018. “Model Based Urban Traffic Control, Part II : Coordinated Model Predictive Controllers.” TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES 97: 23–44.
Vancouver
1.
Zhenzhen H, Boel R, Zhiwu L. Model based urban traffic control, part II : coordinated model predictive controllers. TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES. 2018;97:23–44.
IEEE
[1]
H. Zhenzhen, R. Boel, and L. Zhiwu, “Model based urban traffic control, part II : coordinated model predictive controllers,” TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES, vol. 97, pp. 23–44, 2018.
@article{8638949,
  abstract     = {The present paper describes how to use coordination between neighbouring intersections in order to improve the performance of urban traffic controllers. Both the local MPC (LMPC) introduced in the companion paper (Hao et al., 2018) and the coordinated MPC (CMPC) introduced in this paper use the urban cell transmission model (UCTM) (Hao et al., 2018) in order to predict the average delay of vehicles in the upstream links of each intersection, for different scenarios of switching times of the traffic lights at that intersection. The feedback controller selects the next switching times of the traffic light corresponding to the shortest predicted average delay. While the local MPC (Hao et al., 2018) only uses local measurements of traffic in the links connected to the intersection in comparing the performance of different scenarios, the CMPC approach improves the accuracy of the performance predictions by allowing a control agent to exchange information about planned switching times with control agents at all neighbouring intersections. Compared to local MPC the offline information on average flow rates from neighbouring intersections is replaced in coordinated MPC by additional online information on when the neighbouring intersections plan to send vehicles to the intersection under control. To achieve good coordination planned switching times should not change too often, hence a cost for changing planned schedules from one decision time to the next decision time is added to the cost function. In order to improve the stability properties of CMPC a prediction of the sum of squared queue sizes is used whenever some downstream queues of an intersection become too long. Only scenarios that decrease this sum of squares of local queues are considered for possible implementation. This stabilization criterion is shown experimentally to further improve the performance of our controller. In particular it leads to a significant reduction of the queues that build up at the edges of the traffic region under control. We compare via simulation the average delay of vehicles travelling on a simple 4 by 4 Manhattan grid, for traffic lights with pre-timed control, traffic lights using the local MPC controller (Hao et al., 2018), and coordinated MPC (with and without the stabilizing condition). These simulations show that the proposed CMPC achieves a significant reduction in delay for different traffic conditions in comparison to these other strategies.},
  author       = {Zhenzhen, Hao and Boel, René and Zhiwu, Li},
  issn         = {0968-090X},
  journal      = {TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES},
  keywords     = {Urban traffic control Cell transmission model Model predictive control Distributed control Coordination control,SIGNAL CONTROL,NETWORK},
  language     = {eng},
  pages        = {23--44},
  title        = {Model based urban traffic control, part II : coordinated model predictive controllers},
  url          = {http://dx.doi.org/10.1016/j.trc.2018.09.025},
  volume       = {97},
  year         = {2018},
}

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