Lane changing activity has been closely related to capacity degradation of congested freeways near bottlenecks leading to traffic breakdown. This flow reduction witnessed with the increase of lane changing is mainly attributed to speed variations between lanes, speed drops and vehicles' sluggish acceleration when moving from a slow to a faster lane. Thus, implementing speed harmonization (SH) with prediction of lane changing as a proactive control strategy is effective in preventing freeway

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... city drop, especially with the advent of connected and autonomous vehicle technology (CAV) and its inherent continuous capability of collecting and disseminating its individual vehicle trajectory data. This research develops a model predictive SH control that aims at improving bottleneck throughputs while reducing discretionary lane changing. The SH is developed for a mixed environment of CAVs and human-driven vehicles (HVs). The core of this developed strategy is the integration of a lane changing model with a stochastic car-following model to devise a proper speed limit for individual CAVs, thereby suppressing shockwave propagation. The predictive SH strategy is developed as a hierarchal control strategy using both macroscopic and microscopic models to obtain the optimal length of the SH control section and the optimal speed of CAVs with a speed-difference dampening effect. The viability and efficiency of the proposed framework are demonstrated via numerical simulations for different levels of market penetration rates of CAVs. It is found that the SH control strategy can reduce the total travel time by reducing both vehicle-queuing at the bottleneck as well as lane changing maneuvers; meanwhile hedge against the backward shockwaves and, therefore, can smooth traffic. The average travel time is reduced by 10.86%, 16.78% and 25.28% for scenarios 30%, 40%, and 50% CAVs penetration rate, respectively, in case of SH control with model predictive control (MPC) based on CAV and HV behaviour. Moreover, a sensitivity analysis rev [...]