When the Winter Olympic Games were held, several roads were divided into exclusive lanes for the Winter Olympics to ensure the smooth passage of Winter Olympic vehicles. This reduced the number of lanes available for private vehicles, which caused a temporary tidal traffic phenomenon that led to traffic congestion and increased exhaust emissions. Temporary reversible lanes were added to the object lane to alleviate the temporary tide traffic phenomenon. A bi-level programming model was developed based on the principle of the minimum construction cost and the minimum total travel time of the road network. Meanwhile, three heuristics algorithms were used to solve the problem. The results show that the reasonable addition of temporary reversible lanes during the Olympic Games can reduce the total system travel cost, solve the temporary tidal traffic phenomenon, and alleviate traffic congestion.

This study presents a TD3-driven Bilateral Control Model (TD3-BCM) aimed at improving the stability of mixed traffic flows in autonomous vehicle (AV) chains. By integrating deep reinforcement learning, TD3-BCM optimizes control strategies to reduce traffic oscillations, smooth speed and acceleration fluctuations, and enhance overall system performance. Stability analysis shows that TD3-BCM effectively suppresses traffic fluctuations, with system stability improving from 1.132 to 1.182 as AV penetration increases. At an AV penetration rate of 40%, TD3-BCM surpasses both Cooperative Adaptive Cruise Control (CACC) and traditional Bilateral Control Model (BCM) approaches in terms of traffic efficiency, safety, and energy use—raising trailing vehicle speed by 12.6%, shortening average headway by 19.0%, increasing Time-to-Collision (TTC) by 87.3%, and lowering fuel consumption by 14.8%. When AV penetration reaches 70%, fuel savings rise to 19.7%, accompanied by further improvements in both traffic stability and safety. TD3-BCM provides a scalable and sustainable solution for intelligent transportation systems, particularly in high-penetration AV environments, by significantly enhancing stability, operational efficiency, and road safety.

During the highway reconstruction and expansion, some lanes are often closed on the construction side to ensure that the construction is carried out normally. The presence of the work zone increases the traffic pressure on the construction side of the highway, causing traffic congestion, increased $C{O}_2$ emissions from motor vehicles, and increasing environmental pollution. The bi-level programming model was developed based on the objective of minimizing the travel time and total $C{O}_2$ emissions of the system so as to solve it using a quantum particle swarm algorithm with high convergence speed and high intelligence to form the lane optimization scheme for the three forms of reclosing and expanding six-lane highways in both directions. The results show that reasonable use of opposite non-construction lanes in the work zone of a partially closed highway expansion can reduce the total system travel cost, alleviate traffic congestion, reduce $C{O}_2$ emissions, and contribute to the sustainable development of transportation, as well as the environment.