Optimal Control-Based Eco-Ramp Merging System for Connected and Automated Vehicles

Our current transportation system suffers from a number of problems in terms of safety, mobility, and environmental sustainability. The emergence of innovative intelligent transportation systems (ITS) technologies, and in particular connected and automated vehicles (CAVs), provides many opportunities to address the aforementioned issues. In this paper, we propose a hierarchical ramp merging system that not only generates microscopic cooperative maneuvers for CAVs on the ramp to merge into the mainline traffic flow, but also provides controllability of the ramp inflow rate, thereby enabling macroscopic traffic flow control. A centralized optimal control-based approach is proposed to smooth the merging flow, improve the system-wide mobility, and decrease the overall fuel consumption of the network. Linear quadratic trackers in both finite horizon and receding horizon forms are developed to solve the optimization problem in terms of path planning and sequence determination, where a microscopic vehicle fuel consumption model is applied. Extensive traffic simulation runs have been conducted using PTV VISSIM to evaluate the impact of the proposed system on a segment of SR-91 E in Corona, California. The results confirm that under the regulated inflow rate, the proposed system can avoid potential traffic congestion and improve mobility (e.g., VMT/VHT) up to 147%, with a 47% fuel savings compared to the conventional ramp metering and the ramp without any control approach.

[1]  Dan C. Marinescu,et al.  On-ramp traffic merging using cooperative intelligent vehicles: A slot-based approach , 2012, 2012 15th International IEEE Conference on Intelligent Transportation Systems.

[2]  Leslie N Jacobson,et al.  REAL-TIME METERING ALGORITHM FOR CENTRALIZED CONTROL , 1989 .

[3]  Thorsten Schmidt-Dumont,et al.  Decentralised reinforcement learning for ramp metering and variable speed limits on highways , 2017 .

[4]  Junichi Murata,et al.  Ecological Vehicle Control on Roads With Up-Down Slopes , 2011, IEEE Transactions on Intelligent Transportation Systems.

[5]  Yorgos J. Stephanedes,et al.  Implementation of on-line zone control strategies for optimal ramp metering in the Minneapolis Ring Road , 1994 .

[6]  Riccardo Scarinci,et al.  Control Concepts for Facilitating Motorway On-ramp Merging Using Intelligent Vehicles , 2014 .

[7]  Stephen G. Ritchie,et al.  FREEWAY RAMP METERING USING ARTIFICIAL NEURAL NETWORKS , 1997 .

[8]  Moshe Ben-Akiva,et al.  DEVELOPMENT AND EVALUATION OF A DYNAMIC RAMP METERING CONTROL MODEL , 1997 .

[9]  Jie Huang,et al.  A self-learning system for local ramp metering with queue management , 2017 .

[10]  Gurulingesh Raravi,et al.  Merge Algorithms for Intelligent Vehicles , 2007 .

[11]  Vicente Milanés Montero,et al.  Automated On-Ramp Merging System for Congested Traffic Situations , 2011, IEEE Transactions on Intelligent Transportation Systems.

[12]  Kotagiri Ramamohanarao,et al.  Optimal traffic merging strategy for communication- and sensor-enabled vehicles , 2013, 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013).

[13]  N. B. Goldstein,et al.  A decentralized control strategy for freeway regulation , 1982 .

[14]  Andreas A. Malikopoulos,et al.  Automated and Cooperative Vehicle Merging at Highway On-Ramps , 2017, IEEE Transactions on Intelligent Transportation Systems.

[15]  J. Karl Hedrick,et al.  Automated Vehicle Merging Maneuver Implementation for AHS , 2004 .

[16]  Atsuya Uno,et al.  A merging control algorithm based on inter-vehicle communication , 1999, Proceedings 199 IEEE/IEEJ/JSAI International Conference on Intelligent Transportation Systems (Cat. No.99TH8383).

[17]  W. Marsden.  I and J , 2012 .

[18]  Yao Chen,et al.  AN ADVANCED REAL-TIME RAMP METERING SYSTEM (ARMS): THE SYSTEM CONCEPT , 1994 .

[19]  Jianwei Gong,et al.  Coordinated Ramp Metering with Equity Consideration Using Reinforcement Learning , 2017 .

[20]  P Perovich,et al.  SYSTEM WIDE ADAPTIVE RAMP METERING (SWARM) , 1997 .

[21]  Guoyuan Wu,et al.  Cooperative Ramp Merging System: Agent-Based Modeling and Simulation Using Game Engine , 2019, SAE International Journal of Connected and Automated Vehicles.

[22]  Tom Bellemans,et al.  Model predictive control for ramp metering of motorway traffic: A case study , 2006 .

[23]  Cynthia Taylor,et al.  EVALUATION OF A FUZZY LOGIC RAMP METERING ALGORITHM: A COMPARATIVE STUDY AMONG THREE RAMP METERING ALGORITHMS USED IN THE GREATER SEATTLE AREA , 2000 .

[24]  Aaas News,et al.  Book Reviews , 1893, Buffalo Medical and Surgical Journal.

[25]  Walid Gomaa,et al.  Multi-Agent Reinforcement Learning Control for Ramp Metering , 2014, ICSEng.

[26]  Hikaru Nishira,et al.  Cooperative vehicle path generation during merging using model predictive control with real-time optimization , 2015 .

[27]  Xiao-Yun Lu,et al.  Longitudinal control algorithm for automated vehicle merging , 2000, Proceedings of the 39th IEEE Conference on Decision and Control (Cat. No.00CH37187).

[28]  Andreas A. Malikopoulos,et al.  A Survey on the Coordination of Connected and Automated Vehicles at Intersections and Merging at Highway On-Ramps , 2017, IEEE Transactions on Intelligent Transportation Systems.

[29]  Dirk Helbing,et al.  Enhanced intelligent driver model to access the impact of driving strategies on traffic capacity , 2009, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[30]  Markos Papageorgiou,et al.  ALINEA: A LOCAL FEEDBACK CONTROL LAW FOR ON-RAMP METERING , 1990 .

[31]  R. Stephenson.  A and V , 1962, The British journal of ophthalmology.