Xiaoguang Yang

TL;DR: A mixed integer linear programming (MILP) model is presented to optimize vehicle trajectories and traffic signals in a unified framework at isolated signalized intersections in a CAV environment to validate the advantages of the proposed control method over vehicle-actuated control in terms of intersection capacity, vehicle delays, and CO2 emissions.

TL;DR: The article concluded that signal control based on multiagent reinforcement learning is a kind of closed-loop feedback adaptive control method, which outperforms many counterparts in terms of real-time characteristic, accuracy, and self-learning and therefore will be an important research focus of control method in future.

TL;DR: This study adopts queueing theory and analytically shows that FCFS-based control is incapable of handling high demand with multiple conflicting traffic streams, and an optimization model is proposed to optimally serve CAVs arriving at an intersection for delay minimization.

TL;DR: Comparative analysis results have shown that the proposed dynamic programming model outperforms the first-come-first-serve policy in terms of reducing bus delays, improving schedule adherence, and minimizing the impacts on other vehicular traffic.

TL;DR: This study establishes a series of long short-term memory neural networks with deep neural layers (LSTM-DNN) using 16 settings of hyperparameters and investigates their performance on a 90-day travel time dataset from Caltrans Performance Measurement System (PeMS).