Mainline coordinated control is usually based on fixed speed and statistical traffic flow by period. However, in actual operation, the vehicles parked in front of the intersection and the arriving vehicles often fluctuate, and the through-traffic green time is wasted due to phase transition, which leads to mismatches between the signal plans and actual traffic flow requirements, affecting the traffic efficiency of the intersection. To address the above issues, using vehicle–road collaborative control (VRCC), by calculating the phase difference lead time and phase difference of adjacent intersections, the green extension time for the green wave through-traffic phase, and the guiding vehicle speed, the goal of reducing the detention volume of through traffic, reducing the waste of through-traffic green time caused by phase transitions and improving the throughput of through traffic can be achieved. The speed of the green wave traffic flow is increased by guiding vehicles to form saturated platoons during green periods. Finally, PTV VISSIM 4.3 was used for simulation verification, and the results showed that compared to not implementing the control strategy, the average delay on the arterial road was reduced by 85.1%, the average number of stops was reduced by 84.3%, the average travel time was reduced by 34%, and the average queue length was reduced by 62.6%. This significantly improved the efficiency of traffic on the arterial road and effectively reduced congestion.

Active transit signal priority (TSP) is used more conveniently and widely than the other strategies for real-world signal controllers. However, the active TSP strategies of real-world signal controllers use the first-come-first-served rule to respond to any active TSP request and are not effective at responding to the number of bus arrivals. With or without the green extension strategy, the active TSP has little impact on the final green time of priority phase, even in the case where more buses arrive during the priority phase. The reduced green time of early green strategy is relatively large when a bus arrives, and it would be worse when more buses arrive, the active TSP has a big adverse impact on the final green time of the non-priority phase. Therefore, the active TSP strategies of real-world signal controllers cannot handle the downtown intersection where many bus lines converge or where many buses arrive in a signal cycle during the evening rush hour. Traffic engineers need to do much work to optimize the TSP parameters before field application. Consequently, it is necessary to improve the TSP strategy of the real-world signal controllers for the intersections with a lot of bus arrivals. In order to achieve that objective, the authors present the CNOB (cumulative number of buses) TSP strategy based on the Siemens 2070 signal controller. The TSP strategy extends the max call time according to the number of buses in the arrival section when priority phases are active. The TSP strategy truncates the green time according to the number of buses in the storage section when non-priority phases are active. The experiment’s result shows that the CNOB TSP strategy can not only significantly reduce the average delay per person without using TSP optimization but can also reduce the adverse impact on the general vehicles of non-bus-priority approaches for the intersections with a lot of bus arrivals. Additionally, because the system dynamically adjusts, traffic engineers do not need to do much optimization work before the TSP implementation.

In areas with significant changes in traffic demand and high vehicle dispersion at adjacent intersections, such as the surrounding roads of large shopping malls and schools, traffic problems are prone to occur. This is due to the unequal signal cycle lengths used at upstream and downstream intersections, which lead to periodic phase offsets as the cycles progress. To address this, we propose a multi-strategy integrated vehicle–road coordinated control method to tackle traffic flow operational issues caused by the offset characteristics of unequal-cycle adjacent intersections. A multi-strategy combined algorithm and control logic is established, which includes downstream intersection coordinated phase green extension, dynamic offset adjustment, and transitional queue speed guidance. The proposed method can substantially minimize the offset from falling into an incompatible threshold, effectively reducing queuing and early arrival of vehicles in the straight-through direction. It enables arriving vehicles to pass through the intersection without or with minimal stopping. Finally, the effectiveness of the method is validated using simulation experiments. A vehicle–road coordinated simulation verification platform was established, and comparative experiments were designed. The results indicate that the multi-strategy combined vehicle–road coordinated control method proposed in this paper, while ensuring the original through capacity for straight movements, can effectively reduce queue lengths, the number of stops, average vehicle delay, and travel time for single-direction straight lanes. This improvement enhances the efficiency of coordinated movements in the unequal–cycle adjacent intersections.

This paper proposes two sustainable lane-changing (LC) decision-making models for application in intelligent-connected (I-C) and mixed-traffic environments. The models, with their unique approach, calculate the lane-changing impact area of vehicles, assess interactions with other lane-changing vehicles, and consider factors such as lane-changing conflicts, the number of connected collaborative vehicles and the overall benefits of lane-changing to determine the final lane-changing vehicle. The feasibility of lane changing is also considered when selecting the execution decision. The simulation results show that the proposed model can effectively improve the traffic efficiency. Specifically, in a mixed traffic environment, the model still maintains good lane change performance when the proportion of intelligently connected vehicles exceeds 0.85. The model effectively optimises lane changing conflicts, improves lane changing efficiency and sustainability, and contributes to the development of sustainable transportation in the future. Through micro traffic simulation, when the input flow is Q3 and the lane change ratio r is 0.2, the vehicle travel time is reduced by 24.78%, the average delay is reduced by 23.21% and the average speed is increased by 7.77%.

Vehicle-to-infrastructure (V2I) technology enables information interaction between vehicles and among vehicles and infrastructure, significantly enhancing the efficiency of lane-changing processes and stabilising traffic flow. Current research primarily focuses on single lane-changing events in fixed micro-level scenarios or studies involving small-scale vehicle fleets, neglecting the randomness of lane-changing vehicle arrivals and potential conflicts during lane-changing. This paper proposes a lane-changing decision model based on potential conflict analysis, specifically tailored to mandatory lane-changing requirements in high-density traffic conditions. The model comprises sub-models for lane-changing decision triggering, influence range calculation and lane-changing priority determination, capable of dynamically adjusting the lane-changing sequence, mitigating lane-changing conflicts, and improving driving safety and traffic efficiency. Simulation experiments indicate that, when compared to lane-changing patterns in real-world traffic scenarios, this model reduces travel time by 23.30%, delays by 21.95% and the number of stops by 23.84%, thereby providing a novel approach for lane-changing decision-making and control in V2I environments.