Towards a practical application of model predictive control to suppress shock waves on freeways

We present the results of the application of model predictive control (MPC) to a micro-simulation model with a scenario where shock waves are present, and a microsimulation model functions as a substitute for the real-world traffic system. Shock waves emerge in most cases from traffic jams at bottlenecks, propagate upstream on the freeway, and can remain existent for a long time and distance. This increases travel time, is potentially unsafe, and increases noise and air pollution. Previously reported results using MPC to eliminate shock waves, showed an improvement of 20% of the total time that the vehicles spent in the network. However, they were based on the assumption that the simulation model (representing the real world) and the prediction model are the same, which may have lead to overoptimistic results. In this paper a micro-simulation model (Paramics 5.1 by Quadstone) is used to represent the real world, which results in a model mismatch between the simulation model and the prediction model. We show by simulation that even in the case of a model mismatch the controller is able to suppress or remove shock waves.

[1]  M. P. Keuken,et al.  Onderzoek naar effecten van de 80 km/u-maatregel voor de A13 op de luchtkwaliteit in Overschie , 2003 .

[2]  Petros A. Ioannou,et al.  Traffic Density Control for Automated Highway Systems , 1997, Autom..

[3]  E J Hardman,et al.  Motorway speed control strategies using SISTM , 1996 .

[4]  Antonella Ferrara,et al.  Nonlinear optimization for freeway control using variable-speed signaling , 1999 .

[5]  Stef Smulders,et al.  Control of freeway traffic flow by variable speed signs , 1990 .

[6]  A. Hegyi,et al.  Optimal Coordination of Variable Speed Limits to Suppress Shock Waves , 2002, IEEE Transactions on Intelligent Transportation Systems.

[7]  Henning Lenz,et al.  Nonlinear Speed-control for a Continuum Theory of Traffic Flow , 1999 .

[8]  Perry Y. Li,et al.  Traffic flow stabilization , 1995, Proceedings of 1995 American Control Conference - ACC'95.

[9]  Andras Hegyi,et al.  Model predictive control for integrating traffic control measures , 2004 .

[10]  E. van den Hoogen,et al.  Control by variable speed signs: results of the Dutch experiment , 1994 .

[11]  A. Hegyi,et al.  Optimal Coordination of Variable Speed Limits to Suppress Shock Waves , 2003, 42nd IEEE International Conference on Decision and Control (IEEE Cat. No.03CH37475).

[12]  R. Sollacher,et al.  Standing waves and the influence of speed limits , 2001, 2001 European Control Conference (ECC).

[13]  Stephen J. Wright,et al.  Nonlinear Predictive Control and Moving Horizon Estimation — An Introductory Overview , 1999 .

[14]  S. Smulders,et al.  Control by variable speed signs-the Dutch experiment. , 1992 .

[15]  Markos Papageorgiou,et al.  Modelling and real-time control of traffic flow on the southern part of Boulevard Peripherique in Paris: Part II: Coordinated on-ramp metering , 1990 .

[16]  Petros A. Ioannou INTELLIGENT VEHICLES: CLOSING THE LOOP WITH THE HIGHWAY , 2006 .

[17]  Eduardo F. Camacho,et al.  Model predictive control in the process industry , 1995 .

[18]  Jan M. Maciejowski,et al.  Predictive control : with constraints , 2002 .

[19]  Thomas Parisini,et al.  Neural approximations for feedback optimal control of freeway systems , 2001, IEEE Trans. Veh. Technol..

[20]  J K Wilkie,et al.  USING VARIABLE SPEED LIMIT SIGNS TO MITIGATE SPEED DIFFERENTIALS UPSTREAM OF REDUCED FLOW LOCATIONS , 1997 .