Integrated Control Of Mixed Traffic Networks Using Model Predictive Control

Author : Mohammad Karimi
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (133 download)

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Download or read book Cooperative Traffic Control Framework for Mixed Vehicular Flows written by Mohammad Karimi and published by . This book was released on 2020 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: A prompt revolution is foreseen in the transportation sector, when the current conventional human-driven vehicles will be replaced by fully connected and automated vehicles. As a result, there will be a transition period where both types will coexist until the later type is fully adopted in the traffic networks. This new mix of traffic flow on the existing transportation network will require developing a new ecosystem able to accommodate both types of vehicles in traffic network environments of the future. A major challenging issue related to the emerging mixed transportation ecosystem is the lack of an adequate model and control framework. This is especially important for modeling traffic safety and operations at network bottlenecks such as highway merging areas. Therefore, the main goal of this thesis is to develop a microscopic modeling and hierarchical cooperative control framework specifically for mixed traffic at highway on-ramps. In this thesis, a two-level hierarchical traffic control framework is proposed for mixed traffic at highway merging areas. In this regard, for the lower level of the proposed framework, this thesis establishes a set of fundamental trajectory-based cooperative control algorithms for different merging scenarios under mixed traffic conditions. We identify six scenarios, consisting of triplets of vehicles, defined based on the different combinations of CAVs and conventional vehicles. For each triplet, different consecutive movement phases along with corresponding desired distance and velocity set-points are defined. Via the movement phases, the CAVs engaged in each triplet cooperate to calculate their optimal-smooth trajectories aiming at facilitating the merging maneuver while complying with the realistic constraints related to the safety and comfort of vehicle occupants. The vehicles in each triplet are modeled by a distinct system, and a Model Predictive Control scheme is employed to calculate the cooperative optimal control inputs (acceleration values) for CAVs, accounting for conventional vehicles' uncertainties. In the next step of the thesis, for the higher level of the proposed framework, a merging sequence determination and triplets' formation methodology is developed based on predicting the arrival time of vehicles into the merging area and according to the priority in choosing different triplet types. To model the merging maneuvers when two consecutive triplets share a vehicle, the interactions between triplets of vehicles are also investigated. In order to develop a microscopic traffic simulator, we analytically formulate different vehicles' driving behaviors under cooperative (i.e., the proposed traffic control framework) and non-cooperative (i.e., normal) operation modes and discuss the switching conditions between these driving modes. To evaluate the effectiveness of the proposed framework, first, each triplet is simulated in MATLAB and evaluated for different sets of system initial values. Without a need for readjusting the algorithm for different initial values, the simulation results show that the proposed cooperative merging algorithms ensure smooth merging maneuvers while satisfying all the prescribed constraints, e.g., speed limits, safe distances, and comfortable acceleration and jerk values. Moreover, a simulator is developed in MATLAB for the entire framework (including both the higher and lower level of the framework) to evaluate the impact of all the triplets on continuous mixed traffic flow. Different penetration rates of CAVs under different traffic flow conditions are evaluated through the developed simulator. The simulation results show that the proposed cooperative methodology, comparing to the non-cooperative operation, can improve the average travel time of merging vehicles without disturbing the mainstream flow, provide safer merging maneuvers by avoiding the merging vehicles to stop at the end of the acceleration lane, and guarantee smooth motion trajectories for CAVs (i.e., derivable position and speed along with limited changes in acceleration values). Generally, the results emphasize that the proposed cooperative traffic control framework can improve the mixed traffic conditions in terms of both traffic safety and operations. Moreover, the simulator provides a tool for the transportation community to evaluate their existing infrastructures under different penetration rates of CAVs and examine different traffic control plans for a mixed traffic environment. As the merging maneuver is only one application of gap-acceptance models, other types of maneuvers (e.g., lane changing, vehicle turning, etc.) can be similarly modelled. Thus, we can extend the proposed framework to the multi-lane highways, roundabouts, and urban area intersections. Furthermore, the arrival time prediction of the vehicles can be improved to elevate the performance of the proposed framework during the very congested traffic conditions.