Regional Multi-Agent Cooperative Reinforcement Learning for City-Level Traffic Grid Signal Control

This article studies the effective traffic signal control problem of multiple intersections in a city-level traffic system.A novel regional multi-agent cooperative reinforcement learning algorithm called RegionSTLight is proposed to improve the traffic efficiency.Firstly a regional multi-agent Q-learning framework is proposed,which can equivalently decompose the global Q value of the traffic system into the local values of several regions Based on the framework and the idea of human-machine cooperation,a dynamic zoning method is designed to divide the traffic network into several strong-coupled regions according to realtime traffic flow densities.In order to achieve better cooperation inside each region,a lightweight spatio-temporal fusion feature extraction network is designed.The experiments in synthetic real-world and city-level scenarios show that the proposed RegionS TLight converges more quickly,is more stable,and obtains better asymptotic performance compared to state-of-theart models.

Adaptive signal control and coordination for urban traffic control in a connected vehicle environment: A review

Existing signal control systems for urban traffic are usually based on traffic flow data from fixed location detectors.Because of rapid advances in emerging vehicular communication,connected vehicle(CV)-based signal control demonstrates significant improvements over existing conventional signal control systems.Though various CV-based signal control systems have been investigated in the past decades,these approaches still have many issues and drawbacks to overcome.We summarize typical components and structures of these existing CV-based urban traffic signal control systems and digest several important issues from the summarized vital concepts.Last,future research directions are discussed with some suggestions.We hope this survey can facilitate the connected and automated vehicle and transportation research community to efficiently approach next-generation urban traffic signal control methods and systems.

An integrated and cooperative architecture for multi-intersection traffic signal control

Traffic signal control(TSC)systems are one essential component in intelligent transport systems.However,relevant studies are usually independent of the urban traffic simulation environment,collaborative TSC algorithms and traffic signal communication.In this paper,we propose(1)an integrated and cooperative Internet-of-Things architecture,namely General City Traffic Computing System(GCTCS),which simultaneously leverages an urban traffic simulation environment,TSC algorithms,and traffic signal communication;and(2)a general multi-agent reinforcement learning algorithm,namely General-MARL,considering cooperation and communication between traffic lights for multi-intersection TSC.In experiments,we demonstrate that the integrated and cooperative architecture of GCTCS is much closer to the real-life traffic environment.The General-MARL increases the average movement speed of vehicles in traffic by 23.2%while decreases the network latency by 11.7%.

NSGA-Ⅱ based traffic signal control optimization algorithm for over-saturated intersection group

In order to improve the efficiency of traffic signal control for an over-saturated intersection group, a nondominated sorting genetic algorithm Ⅱ（NSGA-Ⅱ） based traffic signal control optimization algorithm is proposed. The throughput maximum and average queue ratio minimum for the critical route of the intersection group are selected as the optimization objectives of the traffic signal control for the over-saturated condition. The consequences of the efficiency between traffic signal timing plans generated by the proposed algorithm and a commonly utilized signal timing optimization software Synchro are compared in a VISSIM signal control application programming interfaces （SCAPI） simulation environment by using real filed observed traffic data. The simulation results indicate that the signal timing plan generated by the proposed algorithm is more efficient in managing oversaturated flows at intersection groups, and, thus, it has the capability of optimizing signal timing under the over-saturated conditions.

Travel time function for basic link considering signal control in network traffic model

In order to describe the travel time of signalcontrolled roads, a travel time model for urban basic roads based on the cumulative curve is proposed. First, the traffic wave method is used to analyze the formation and dispersion of the vehicle queue. Cumulative curves for road entrances and exits are established. Based on the cumulative curves, the travel time of the one-lane road under stable flow input is derived. And then, the multi-lane road is decomposed into a series of single-lane links based on its topological characteristics. Hence, the travel time function for the basic road is obtained. The travel time is a function of road length, flow and control parameters. Numerical analyses show that the travel time depends on the supply-demand condition, and it has high sensitivity during peak hours.

Fuzzy traffic signal control with DNA evolutionary algorithm

In order to optimize the signal control system, this paper proposes a method to design an optimized fuzzy logic controller （FLC） with the DNA evolutionary algorithm. Inspired by the DNA molecular operation characteristics, the DNA evolutionary algorithm modifies the corresponding genetic operators. Compared with the traditional genetic algorithm （GA）, the DNA evolutionary algorithm can overcome weak local search capability and premature convergence. The parameters of membership functions are optimized by adopting the quaternary encoding method and performing corresponding DNA genetic operators. The relevant optimized parameters are combined with the FLC for single intersection traffic signal control. Simulation experiments shows the better performance of the FLC with the DNA evolutionary algorithm optimization. The experimental results demonstrate the efficiency of the nrotmsed method.

Tri-level programming model for combined urban traffic signal control and traffic flow guidance

In order to balance the temporal-spatial distribution of urban traffic flow, a model is established for combined urban traffic signal control and traffic flow guidance. With consideration of the wide use of fixed signal control at intersections, traffic assignment under traffic flow guidance, and dynamic characteristics of urban traffic management, a tri-level programming model is presented. To reflect the impact of intersection delay on traffic assignment, the lower level model is set as a modified user equilibrium model. The middle level model, which contains several definitional constraints for different phase modes, is built for the traffic signal control optimization. To solve the problem of tide lane management, the upper level model is built up based on nonlinear 0-1 integer programming. A heuristic iterative optimization algorithm(HIOA) is set up to solve the tri-level programming model. The lower level model is solved by method of successive averages(MSA), the middle level model is solved by non-dominated sorting genetic algorithm II(NSGA II), and the upper level model is solved by genetic algorithm(GA). A case study is raised to show the efficiency and applicability of the proposed modelling and computing method.

FL-FN-MOGA Based Traffic Signal Control

In this paper, a traffic signal control method based on fuzzy logic (FL), fuzzy-neuro (FN) and multi-objective genetic algorithms (MOGA) for an isolated four-approach intersection with through and left-turning movements is presented. This method has an adaptive signal timing ability, and can make adjustments to signal timing in response to observed changes.The 'urgency degree' term, which can describe the different user's demand for green time is used in decision-making by which strategy of signal timing can be determined. Using a fuzzy logic controller, we can determine whether to extend or terminate the current signal phase and select the sequences of phases. In this paper, a method based on fuzzy-neuro can be used to predict traffic parameters used in fuzzy logic controller. The feasibility of using a multi-objective genetic algorithm ( MOGA) to find a group of optimizing sets of parameters for fuzzy logic controller depending on different objects is also demonstrated. Simulation results show that the proposed methed is effecfive to adjust the signal timing in response to changing traffic conditions on a real-time basis, and the controller can produce lower vehicle delays and percentage of stopped vehicles than a traffic-actuated controller.

Evolvement regularity of signal control parameters of urban road in cold region

In snow-icy road environment, the survey data indicate that the largest decrease in traffic flow running characters occurs when snow and ice begin to accumulate on the road surface. Saturation flow is decreased by 16% , speed is decreased by 30% , and start-up lost time is increased by 27%. Based on the signal control theory of HCM and Webster, the character values of traffic flow in different urban road environments were investigated, and the evolvement regularity of signal control parameters such as cycle, split, green time, offset, yellow time and red time in snow-icy road environment was analyzed. The impact factors and the changes in the scope of signal control parameters were achieved. Simulation results and practical application show that the signal control plan of road enviromnent without snow and ice will increase the vehicle delay, stop length and traffic congestion in snow-icy road environment. Thus, the traffic signal control system should address a suitable signal control plan based on different road environments.

A Survey of Model Predictive Control Methods for Traffic Signal Control

Enhancing traffic efficiency and alleviating(even circumventing) traffic congestion with advanced traffic signal control(TSC) strategies are always the main issues to be addressed in urban transportation systems. Since model predictive control(MPC) has a lot of advantages in modeling complex dynamic systems, it has been widely studied in traffic signal control over the past 20 years. There is a need for an in-depth understanding of MPC-based TSC methods for traffic networks. Therefore, this paper presents the motivation of using MPC for TSC and how MPC-based TSC approaches are implemented to manage and control the dynamics of traffic flows both in urban road networks and freeway networks. Meanwhile, typical performance evaluation metrics, solution methods, examples of simulations,and applications related to MPC-based TSC approaches are reported. More importantly, this paper summarizes the recent developments and the research trends in coordination and control of traffic networks with MPC-based TSC approaches. Remaining challenges and open issues are discussed towards the end of this paper to discover potential future research directions.

A multi process value-based reinforcement learning environment framework for adaptive traffic signal control

Realising adaptive traffic signal control(ATSC)through reinforcement learning(RL)is an important means to easetraffic congestion.This paper finds the computing power of the central processing unit(CPU)cannot fully usedwhen Simulation of Urban MObility(SUMO)is used as an environment simulator for RL.We propose a multi-process framework under value-basedRL.First,we propose a shared memory mechanism to improve exploration efficiency.Second,we use the weight sharing mechanism to solve the problem of asynchronous multi-process agents.We also explained the reason shared memory in ATSC does not lead to early local optima of the agent.Wehave verified in experiments the sampling efficiency of the 10-process method is 8.259 times that of the single process.The sampling efficiency of the 20-process method is 13.409 times that of the single process.Moreover,the agent can also converge to the optimal solution.

Design and Simulation of an Audio Signal Alerting and Automatic Control System

A large part of our daily lives is spent with audio information. Massive obstacles are frequently presented by the colossal amounts of acoustic information and the incredibly quick processing times. This results in the need for applications and methodologies that are capable of automatically analyzing these contents. These technologies can be applied in automatic contentanalysis and emergency response systems. Breaks in manual communication usually occur in emergencies leading to accidents and equipment damage. The audio signal does a good job by sending a signal underground, which warrants action from an emergency management team at the surface. This paper, therefore, seeks to design and simulate an audio signal alerting and automatic control system using Unity Pro XL to substitute manual communication of emergencies and manual control of equipment. Sound data were trained using the neural network technique of machine learning. The metrics used are Fast Fourier transform magnitude, zero crossing rate, root mean square, and percentage error. Sounds were detected with an error of approximately 17%;thus, the system can detect sounds with an accuracy of 83%. With more data training, the system can detect sounds with minimal or no error. The paper, therefore, has critical policy implications about communication, safety, and health for underground mine.

Intersection signal control multi-objective optimization based on genetic algorithm

A signal control intersection increases not only vehicle delay, but also vehicle emissions and fuel consumption in that area. Because more and more fuel and air pollution problems arise recently, an intersection signal control optimization method which aims at reducing vehicle emissions, fuel consumption and vehicle delay is required heavily. This paper proposed a signal control multi-object optimization method to reduce vehicle emissions, fuel consumption and vehicle delay simultaneously at an intersection. The optimization method combined the Paramics microscopic traffic simulation software, Comprehensive Modal Emissions Model （CMEM）, and genetic algorithm. An intersection in Haizhu District, Guangzhou, was taken for a case study. The result of the case study shows the optimal timing scheme obtained from this method is better than the Webster timing scheme.

Adaptive green traffic signal controlling using vehicular communication

The importance of using adaptive traffic signal control for figuring out the unpredictable traffic congestion in today's metropolitan life cannot be overemphasized. The vehicular ad hoc network(VANET), as an integral component of intelligent transportation systems(ITSs), is a new potent technology that has recently gained the attention of academics to replace traditional instruments for providing information for adaptive traffic signal controlling systems(TSCSs). Meanwhile, the suggestions of VANET-based TSCS approaches have some weaknesses:(1) imperfect compatibility of signal timing algorithms with the obtained VANET-based data types, and(2) inefficient process of gathering and transmitting vehicle density information from the perspective of network quality of service(Qo S). This paper proposes an approach that reduces the aforementioned problems and improves the performance of TSCS by decreasing the vehicle waiting time, and subsequently their pollutant emissions at intersections. To achieve these goals, a combination of vehicle-to-vehicle(V2V) and vehicle-to-infrastructure(V2I) communications is used. The V2 V communication scheme incorporates the procedure of density calculation of vehicles in clusters, and V2 I communication is employed to transfer the computed density information and prioritized movements information to the road side traffic controller. The main traffic input for applying traffic assessment in this approach is the queue length of vehicle clusters at the intersections. The proposed approach is compared with one of the popular VANET-based related approaches called MC-DRIVE in addition to the traditional simple adaptive TSCS that uses the Webster method. The evaluation results show the superiority of the proposed approach based on both traffic and network Qo S criteria.

State-Space Equations and the First-Phase Algorithm for Signal Control of Single Intersections

State-space equations were applied to formulate the queuing and delay of traffic at a single intersection in this paper. The signal control of a single intersection was then modeled as a discrete-time optimal control problem, with consideration of the constraints of stream conflicts, saturation flow rate, minimum green time, and maximum green time. The problem cannot be solved directly due to the nonlinear constraints. However, the results of qualitative analysis were used to develop a first-phase signal control algorithm. Simulation results show that the algorithm substantially reduces the total delay compared to fixed-time control.

Urban Intersection Traffic Signal Control Based on Fuzzy Logic

This paper presents a fuzzy logic adaptive traffic signal control method for an isolated four-approach intersection with through and left-turning movements. In the proposed method, the fuzzy logic controller can make adjustments to signal timing in response to observed changes. The 'urgency degree' term that can describe different user's demands for a green light is used in the fuzzy logic decision-making. In addition, a three-level fuzzy controller model decides whether to extend or terminate the current signal phase and the sequence of phases. Simulation results show that the fuzzy controller can adjust its signal timing in response to changing traffic conditions on a real-time basis and that the proposed fuzzy logic controller leads to less vehicle delays and a lower percentage of stopped vehicles.

Image-based traffic signal control via world models

Traffic signal control is shifting from passive control to proactive control, which enables the controller to direct current traffic flow to reach its expected destinations. To this end, an effective prediction model is needed for signal controllers. What to predict, how to predict, and how to leverage the prediction for control policy optimization are critical problems for proactive traffic signal control. In this paper, we use an image that contains vehicle positions to describe intersection traffic states. Then, inspired by a model-based reinforcement learning method, DreamerV2,we introduce a novel learning-based traffic world model. The traffic world model that describes traffic dynamics in image form is used as an abstract alternative to the traffic environment to generate multi-step planning data for control policy optimization. In the execution phase, the optimized traffic controller directly outputs actions in real time based on abstract representations of traffic states, and the world model can also predict the impact of different control behaviors on future traffic conditions. Experimental results indicate that the traffic world model enables the optimized real-time control policy to outperform common baselines, and the model achieves accurate image-based prediction, showing promising applications in futuristic traffic signal control.

Traffic Signals Control with Adaptive Fuzzy Controller in Urban Road Network

An adaptive fuzzy logic controller (AFC) is presented for the signal control of the urban traffic network. The AFC is composed of the signal control system-oriented control level and the signal controller-oriented fuzzy rules regulation level. The control level decides the signal timings in an intersection with a fuzzy logic controller. The regulation level optimizes the fuzzy rules by the Adaptive Rule Module in AFC according to both the system performance index in current control period and the traffic flows in the last one. Consequently the system performances are improved. A weight coefficient controller (WCC) is also developed to describe the interactions of traffic flow among the adjacent intersections. So the AFC combined with the WCC can be applied in a road network for signal timings. Simulations of the AFC on a real traffic scenario have been conducted. Simulation results indicate that the adaptive controller for traffic control shows better performance than the actuated one.

Improving schedule adherence based on dynamic signal control and speed guidance in connected bus system

Purpose–The purpose of this paper is to develop a dynamic control method to improve bus schedule adherence under connected bus system.Design/methodology/approach–The authors developed a dynamic programming model that optimally schedules the bus operating speed at road sections and multiple signal timing plans at intersections to improve bus schedule adherence.First,the bus route was partitioned into three types of sections:stop,road and intersection.Then,transit agencies can control buses in real time based on all collected information;i.e.control bus operating speed on road sections and adjust the signal timing plans through signal controllers to improve the schedule adherence in connected bus environment.Finally,bus punctuality at the downstream stop and the saturation degree deviations of intersections were selected as the evaluation criteria in optimizing signal control plans and bus speeds jointly.Findings–An illustrative case study by using a bus rapid transit line in Jinan city was performed to verify the proposed model.It revealed that based on the proposed strategy,the objective value could be reduced by 73.7%,which indicated that the punctuality was highly improved but not to incur excessive congestion for other vehicular traffic.Originality/value–In this paper,the authors applied speed guidance and the adjustment of the signal control plans for multiple cycles in advance to improve the scheduled stability;furthermore,the proposed control strategy can reduce the effect on private traffics to the utmost extend.

Overview of quantum memory protection and adiabaticity induction by fast signal control

A quantum memory or information processing device is subject to the disturbance from its surrounding environment or the inevitable leakage due to its contact with other systems. To tackle these problems, several control protocols have been proposed for quantum memory or storage. Among them, the fast signal control or dynamical decoupling based on external pulse sequences provides a prevailing strategy aimed at suppressing decoherence and preventing the target systems from the leakage or diffusion process. In this paper, we review the applications of this protocol in protecting quantum memory under the non-Markovian dissipative noise and maintaining systems on finite speed adiabatic passages without leakage therefrom. We analyze perturbative and nonperturbative dynamical equations for leakage and control, including second-order master equation, quantum-state-diffusion equation, and one-component master equation derived from Feshbach PQ-partitioning technique. It turns out that the quality of fast-modulated signal control is insensitive to configurations of the applied pulse sequences. Specifically, decoherence and leakage will be greatly suppressed as long as the control sequence is able to effectively shift the system beyond the bath cutoff frequency, almost independent of the details of the control sequences that could be ideal pulses, regular rectangular pulses, random pulses and even noisy pulses.