Existing signalized intersection control methods allocate intersection resources to each phase from the time dimension based on traffic conflict. Solving the traffic control issues caused by the unbalanced traffic demands at intersections is always a challenge. Recently developed connected and automated vehicle (CAV) technologies render it possible to collect detailed real-time traffic information from vehicles for the optimal control and management of time-space resources at signalized intersections. In this paper, we propose a mixed integer quadratic programming to jointly optimize signal timings and variable guide lane settings for a typical four-arm intersection in the CAV environment. From the time dimension, restrictions of the conventional concept of the signal cycle are overcome.Phase sequences, green start, and duration are freely assigned for each CAV platoon based on the movement-based signal timing. From the space dimension, lane resources can be reasonably allocated in consideration of the dynamic traffic demand distribution. In this study, a demand-response control is realized for the signalized intersection through time-space resources cooperated optimization. Furthermore, vehicle trajectory control is integrated into the collaborative control framework to reduce or eliminate wasted green time. At last, we propose the dynamic control process using the collaborative control framework. Numerical simulation results show that the collaborative control method outperforms over fixed-time and signal optimization control modes in terms of travel time with both under-saturated and over-saturated conditions.
The effect of connected vehicle environment on the transportation systems and the relationship between the penetration rate of connected vehicle and its efficiency are investigated in this study. An example based on the classical two-route network is adopted in this study, in which the drivers consist of two types: informed and uninformed. The advantages and disadvantages of the connected vehicle environment are analyzed, and the concentration phenomenon is proposed and found to be mitigated when only a fraction of drivers are informed. The simulation tool embodying the characteristics of the connected vehicle environment is developed using the multiagent technology. Finally, different scenarios are simulated, such as the zero-information environment, the fullinformation environment, and the connected vehicle environment with various penetration rates. Moreover, simulation results of the global performance of the transportation system are compared. The results show that the connected vehicle environment can efficiently improve the performance of the transportation system, while the adverse effects due to concentration rise out from the excessive informed drivers. An optimal penetration rate of the connected vehicles is found to characterize the best performance of the system. These findings can aid in understanding the effect of the connected vehicle environment on the transportation system.
Toll plazas on expressways often experience congestion owing to the imbalance between traffic demand and supply. To address this issue, this paper designs a proactive traffic control method to relieve congestion at toll plazas based on the model predictive control (MPC) that integrates the dynamic lane configuration, that is, the numbers of electronic toll collection (ETC) and manual toll collection (MTC) lanes, and the variable speed limit (VSL). The proposed method first forecasts short‐term traffic demand in toll plazas based on the long short‐term memory neural network. Then the cell transmission model (CTM) is applied to predict the traffic evolutions in the toll plaza area. At last, the MPC‐based control framework is used to optimize the numbers of ETC and MTC lanes and the variable speed limit to minimize the total vehicle travel time in a rolling horizon. Micro‐simulation tests are carried out in VISSIM to verify the effectiveness of the proposed control method. The simulation results show that the proposed control method can significantly improve the traffic performances at the toll plazas. The findings in this study can lead to field implementation of proactive control at toll plazas.
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