This article proposes a person-based traffic signal optimization algorithm that simultaneously optimizes signal phase sequence, cycle length, and green split to minimize total person delay at an intersection. It builds upon previous person-based work that uses a mathematical program to optimize signal timings based on real-time passenger car flows and knowledge of bus arrivals and passenger occupancies. Phase rotation is directly accommodated within the mathematical program by introducing new binary variables to describe the phase sequence. The program can either be solved directly or through an enumeration approach to reduce computational complexity. The proposed method is tested using numerical simulations of an intersection in Athens, Greece. The results reveal that phase rotation can reduce person delay significantly, especially when the intersection flow ratio is relatively low. These reductions in person delays are achieved by implicitly prioritizing the movement of high-occupancy buses to the detriment of lower-occupancy cars. Furthermore, to balance the person delay reduction and phase rotation frequency, a threshold can be applied in the phase sequence selection process to eliminate phase rotations actions with limited benefits.

Traffic signal control is essential for transportation efficiency in road networks. It has been a challenging problem because of the complexity in traffic dynamics. Conventional transportation research suffers from the incompetency to adapt to dynamic traffic situations. Recent studies propose to use reinforcement learning (RL) to search for more efficient traffic signal plans. However, most existing RL-based studies design the key elements - reward and state - in a heuristic way. This results in highly sensitive performances and a long learning process. To avoid the heuristic design of RL elements, we propose to connect RL with recent studies in transportation research. Our method is inspired by the state-of-the-art method max pressure (MP) in the transportation field. The reward design of our method is well supported by the theory in MP, which can be proved to be maximizing the throughput of the traffic network, i.e., minimizing the overall network travel time. We also show that our concise state representation can fully support the optimization of the proposed reward function. Through comprehensive experiments, we demonstrate that our method outperforms both conventional transportation approaches and existing learning-based methods.

The advent of intelligent vehicle technologies holds significant potential to alter the dynamics of traffic flow. Prior work on the effects of such technologies on the formation of self-organized traffic jams has led to analytical solutions and numerical simulations at the mesoscopic scale, which may not yield significant information about the distribution of vehicle cluster size. Since the absence of large clusters could be offset by the presence of several smaller clusters, the distribution of cluster sizes can be as important as the presence or absence of clusters. To obtain a prediction of vehicle cluster distribution, the included work presents a statistical mechanics-inspired method of simulating traffic flow at a microscopic scale via the generalized Ising model. The results of the microscopic simulations indicate that traffic systems dominated by adaptive cruise control ( acc)-enabled vehicles exhibit a higher probability of formation of moderately sized clusters, as compared with the traffic systems dominated by human-driven vehicles; however, the trend is reversed for the formation of large-sized clusters. These qualitative results hold significance for algorithm design and traffic control because it is easier to predict and take countermeasures for fewer large localized clusters as opposed to several smaller clusters spread across different locations on a highway.