Perimeter control and route guidance are effective ways to reduce traffic congestion and improve traffic efficiency by controlling the spatial and temporal traffic distribution on the network. This paper presents a multi-scale joint perimeter control and route guidance (MSJC) framework for controlling traffic in large-scale networks. The network is first partitioned into several subnetworks (regions) with traffic in each region governed by its macroscopic fundamental diagram (MFD), which forms the macroscale network (upper level). Each subnetwork, comprised of actual road links and signalized intersections, forms the microscale network (lower level). At the upper level, a joint perimeter control and route guidance model solves the region-based inflow rate and hyper-path flows to control the accumulation of each region and thus maximize the throughput of each region. At the lower level, a perimeter control strategy integrated with a backpressure policy determines the optimal signal phases of the intersections at the regional boundary. At the same time, a route choice model for vehicles is constructed to meet hyper-path flows and ensure the intra-region homogeneity of traffic density. The case study results demonstrate that the proposed MSJC outperforms other benchmarks in regulating regional accumulation, thereby improving throughput.

Human-Driven Vehicles (HDVs) and Connected Automated Vehicles (CAVs) will coexist for a long period. However, existing studies on CAV control find it challenging to stabilize a mixed platoon with HDVs and CAVs considering the uncertainty of HDVs. Besides, most methods are limited to specific platoon compositions and have advantages only in certain penetration rates which hinders application of CAVs. In this study, a novel control framework named Cooperative Adaptive Cruise Control in a mixed platoon (CACCm) is proposed to stabilize mixed platoons as well as smooth the traffic flow. The framework consists of two modules: a control strategy for CAVs in a platoon with random compositions and a control parameter optimization process improving string stability and mitigating oscillations. A reactive controller is designed for a generic platoon by constructing a car-following model with uncertainty and a feedforward filter, which have more adaptability in mixed scenarios. To strengthen stability and mitigate oscillations caused by HDVs, properties of HDVs’ motion in frequency domain are analyzed and its predominant frequency range is extracted using real HDV trajectories from the reconstructed Next Generation Simulation (NGSIM) dataset and Fast Fourier Transformation. Two types of indicators for a mixed platoon including string stability ratio and disturbance damping ratio are defined. Based on this, an optimization process is conducted for control parameters to increase the disturbance suppression and string stability. Verified by real trajectory data simulation experiments, CACCm can significantly mitigate the speed oscillations, and improve comfort, safety and fuel consumption compared with existing approaches.

The research on signal coordination has been greatly enriched over the last decade. However, existing contributions face inherent limitations such as weak connection between objectives and common measurements of effectiveness (MOEs) caused by insufficient modeling of traffic dynamics, invariable phase splits, and great demand on hyperparameters. Meanwhile, nearly all related works are concentrated on scenarios with only under-saturated phases. Therefore, an arterial signal coordination model for minimum level of over-saturation and stops is proposed. Unlike most related works, the proposed model focuses on minimizing phase over-saturation and total stops by estimating queue profile for all phases under variable signal plans. The model is initially formulated as a mixed-integer nonlinear programming (MINLP). By applying linearization techniques, it is then transformed into a mixed-integer linear programming (MILP). Simulation experiments are carried out in SUMO, where an artery is built with eight scenarios of different traffic demand. The results indicate that the model is more competent in reducing average delay (AD), average stops (AS) and average total travel time (ATTT) than Yang’s multi-path progression model for all scenarios. It is also verified to best MP-BAND by managing obvious reduction in AS and showing advantage in decreasing AD and ATTT in most scenarios. Additionally, the proposed model is able to alleviate the level of over-saturation for an intersection by re-allocating phase splits properly, resulting in less over-saturated phases. Intuitive illustrations attest to the effectiveness of the queue estimation in the proposed model, highlighting the theoretical importance of modeling queue length as a variable.

Over the years, reinforcement learning has emerged as a popular approach to develop signal control and vehicle platooning strategies either independently or in a hierarchical way. However, jointly controlling both in real-time to alleviate traffic congestion presents new challenges, such as the inherent physical and behavioral heterogeneity between signal control and platooning, as well as coordination between them. This paper proposes an innovative solution to tackle these challenges based on heterogeneous graph multi-agent reinforcement learning and traffic theories. Our approach involves: 1) designing platoon and signal control as distinct reinforcement learning agents with their own set of observations, actions, and reward functions to optimize traffic flow; 2) designing coordination by incorporating graph neural networks within multi-agent reinforcement learning to facilitate seamless information exchange among agents on a regional scale; 3) applying alternating optimization for training, allowing agents to update their own policies and adapt to other agents’ policies. We evaluate our approach through SUMO simulations, which show convergent results in terms of both travel time and fuel consumption, and superior performance compared to other adaptive signal control methods.

This paper proposes an adversarial reinforcement learning (RL)-based traffic control strategy to improve the traffic efficiency of an integrated network with expressway and adjacent surface streets. The proposed adversarial RL integrates adversarial learning into a multi-agent RL model, namely the multi-agent advantage actor-critic (MA2C), so as to enhance the generalization of the control strategy against the mismatch between an offline-training environment and the real traffic process. In the adversarial RL, the RL model is trained to maximize the network throughput, while the adversarial network, which produces disturbances to observed traffic states, is trained based on the opposite reward of the RL model. The proposed control strategy is tested using the microscopic traffic simulation software, SUMO. To reproduce the difference between an offline-training environment and the real traffic process, two different traffic models in SUMO are used for offline-training and online-testing purposes, respectively. Simulation results demonstrate that the proposed approach performs better in reducing total time spent than the original MA2C approach, as well as a conventional feedback based controller.

Urged by a close future perspective of a traffic flow made of a mix of human-driven vehicles and automated vehicles (AVs), research has recently focused on studying the traffic flow characteristics of Adaptive Cruise Controls (ACCs), the most typical AV. However, in most works, the ACC system is studied under a simplifying and unrealistic assumption, or the ACC system modeled is inaccurate. This paper proposes a general hierarchical control system to model ACC systems with several assumptions based on the deficiencies above. Moreover, a field experiment was conducted, and the corresponding experimental data was used to verify the proposed hierarchical control system and assumptions. In addition, string stability is explored along with sensitivity analyses of control parameters based on an example under the constant time gap policy. The results show that different upper-level controller parameters have different delays, where the delay of the speed is negligible; the introduction of actuator delay and lag in the lower-level controller can significantly improve the model goodness of fit. Furthermore, optimizing the delay and lag in the lower-level controller can significantly enhance the string stability of ACCs than optimizing the control parameters.

With the development of Connected Autonomous Vehicle (CAV) technology, different information flow topologies (IFTs) have been applied to CAV Ad Hoc Networks. Firstly, from the perspective of the controller, a general model is proposed to directly reflect the actual communication effect on the controller instead of simply abstracting it into the optimal time interval, which is more feasible. Secondly, linear stability analysis is carried out based on the general model where different time delays are considered, and stability criterion is obtained for subsequent analysis. Finally, we compare the three main IFTs through numerical simulations and analyze the difference in stability region, robustness, traffic safety, and Eco-driving between platoon Cooperative Adaptive Cruise Control (CACC) controllers based on different IFTs. It is found that adopting CACCs can notably improve the traffic capacity and traffic safety relative to Manual Vehicle (MV) no matter what IFT is adopted. As for the difference between the three IFTs, predecessor-leader following (PLF) and multiple-predecessor-leader following (MPLF) are significantly superior to predecessor following (PF), while the choice between PLF and MPLF depends on the communication bandwidth. With higher communication bandwidth or fewer communication vehicles, MPLF will be a better option; on the contrary, PLF is more suitable.

In order to improve the traffic efficiency of oversaturated arterial traffic, a two-way signal coordinated control method is proposed. Taking traffic speeds, turning flow proportions and the number of lanes into consideration, a traffic flow model for oversaturated arterial is established on the basis of LWR theory, and vehicle trajectory data of arterials is used to verify this model. Then a coordinated control model for two-way oversaturated arterials is introduced, which is composed of a throughput maximization model and a delay minimization model. For the former one, the cycle length and green time at the arterial intersections are optimized to improve the arterial capacity, while the latter is built to realize the signal coordination of the consecutive intersections by optimizing offsets. A multi-objective algorithm is proposed to solve the coordinated control model. First, the throughput maximization model is solved by the standard algorithm of mixed-integer linear programming. Second, the feasible solutions of the signal orders are obtained by a phase pattern searching algorithm. Third, the delay minimization model is solved by a standard algorithm of quadratic programming model. Finally, the proposed model is used to coordinate and optimize the traffic signals on East Zhongshan Road containing 10 intersections in Nanjing, China. A trajectory-based simulation model is put forward to simulate the arterial traffic, demonstrating that the proposed coordinated control model can avoid the spillover and queue retention. After optimization, the capacity increases by 12.6% while the average vehicle delay for through vehicles drops by 23.6% compared to Synchro’s coordinated control.

This paper proposes three route control strategies for autonomous vehicles (AVs) exiting to off-ramps. First, two microscopic lane-changing models are developed to simulate the off-ramp behaviors for AVs. A risk factor λ is introduced in the discretionary lane-changing (DLC) model, and a five-step process is developed for the mandatory lane-changing (MLC) model. Second, three route control strategies are presented, i.e., grading strategy (GS), half-overlap strategy (HOS), and full-overlap strategy (FOS). Finally, the extensive numerical simulations are conducted to analyze the characteristics of each strategy based on the MATLAB platform. The results indicate that the studied area displays significant variability in spatiotemporal dynamics of average velocity and road capacity under different strategies. It is found that the GS is the most effective control strategy for traffic congestion dissipation and travel delay reduction, followed by the HOS and, then, the FOS. The road capacity under the GS is increased by 19.1% and 7.8% compared to the FOS and the HOS, respectively. Furthermore, the GS consumes lower costs in terms of safety, travel efficiency, and route and lane switch frequency compared to the HOS and FOS, which is verified as the optimal strategy according to overall running cost. The findings of this paper can provide useful references for the design of AVs as well as the management of the automated highway systems.