Traffic Signal Control Using an LMI-Based MPC Method

The acceleration of urbanization has left cities around the world facing significant challenges, including traffic congestion and inefficient management. Intelligent transportation systems provide new technological means to address these issues, especially in traffic signal control, where advanced communication technologies play an important role in facilitating the process. The potential of long range radio (LoRa) technology in intelligent traffic signal control systems is attracting growing interest, largely due to its capacity for long-range transmission and low power consumption. This paper explores the potential benefits of LoRa technology in traffic flow monitoring and signal control by reviewing existing studies, especially in the field of long-distance transmission and low-power communication. The study indicates that LoRa technology can effectively boost the efficacy of traffic management, reduce traffic congestion, and provide technical support for the development of future intelligent transportation systems. Nevertheless, the stability of LoRa technology in high-density urban environments and its integration with other technologies still present challenges. Further research can investigate the anti-jamming ability, communication rate optimization, and its potential for wider application of LoRa technology in Intelligent Transport Systems (ITS) to promote more efficient urban traffic management

The traffic congestion problem in urban areas is worsening since traditional traffic signal control systems cannot provide efficient traffic control. Therefore, dynamic traffic signal control in intelligent transportation system (ITS) recently has received increasing attention. This study devised an adaptive and cooperative multi-agent fuzzy system for a decentralized traffic signal control. To achieve this goal we have worked on a model, which has three levels of control. Every intersection is controlled by its own traffic situation, its neighboring intersections recommendations and a knowledge base, which provides the traffic pattern of each intersection in any particular day of the week and hour of the day. The proposed architecture comprises a knowledge base, prediction module and a traffic observer that provide data to real traffic data preparation module, then a decision-making layer takes decision to how long should the intersection green light be extended. The proposed architecture can achieve dynamic traffic signal control. We have also developed a NetLogo-based traffic simulator to serve as the agents' world. Our approach is tested with traffic control of a large connected junction and the result obtained is promising; The average delay time can be reduced by 21.76% compared to the conventional fixed sequence traffic signal and 14.77% compared to the vehicle actuated traffic signal control strategy.

Traffic congestion has substantially induced significant mobility and energy inefficiency. Many research challenges are identified in traffic signal control and management associated with artificial intelligence (AI)‐based models. For example, developing AI‐driven dynamic traffic system models that accurately capture high‐resolution traffic attributes and formulate robust control algorithms for traffic signal optimization is difficult. Additionally, uncertainties in traffic system modeling and control processes can further complicate traffic signal system controllability. To partially address these challenges, this study presents a novel, hybrid neural network model enhanced with a probability density function kernel shaping technique to formulate traffic system dynamics better and improve comprehensive traffic network modeling and control. The numerical experimental tests were conducted, and the results demonstrate that the proposed control approach outperforms the baseline control strategies and reduces overall average delays by 11.64% on average. By leveraging the capabilities of this innovative model, this study aims to address major challenges related to traffic congestion and energy inefficiency toward more effective and adaptable AI‐based traffic control systems.

To effectively alleviate regional congestion in urban road networks brought about by special events or random disturbances, traffic control measures are used to improve the spatial distribution of traffic. This paper focuses on controlling traffic subareas and proposes adjustment strategies for boundary intersections' signal control plans using a Blocking-Discharging method. To overcome the unfavorable factors of conventional control methods, fuzzy control technology is used and an adjustment of the green ratio is selected as the output variable. This serves as an effective solution for optimization at the regional level and leads to further guidance for signal control at the next level. The approach proposed is verified using a road network model of Beijing Olympic Park as a simulation example. It was found that the implementation of Blocking-Discharging strategies has improved traffic conditions of the target subarea, and the integrated use of both strategies is most effective.

At present, traffic signal systems almost exclusively use detection systems that provide input in the form of “contact closures,” or binary ON/OFF states representing vehicle presence in detection zones. This limited information constrains the potential performance of traffic signal control. Advanced control algorithms often make use of estimated vehicle arrival times but such data is challenging to estimate with existing detector data. Connected vehicles (CVs) are able to provide such data directly but it is unlikely that all or even most traffic will be connected within the foreseeable future. Vehicle trajectory data from sensors can fill this gap and facilitate a smooth transition from contact closure-based signal control to trajectory-based signal control. This dissertation seeks to improve this situation by exploring potential applications of vehicle trajectory data within existing traffic signal control infrastructure. One issue is the sensing range at which trajectory data can be acquired for intersection control. To test the sensitivity of advanced control methods to the sensing range, two advanced signal control methods, a self-organizing algorithm that adds to existing actuated control methods, and the Phase Allocation Algorithm proposed in recent research on CV-based signal control, were implemented and tested in simulation under varying volume configurations and sensing ranges. The results show that increasing sensing range is associated with improved performance, with the performance of the two advanced control methods converging at sensing ranges of 1000–1500 ft. Actuated control is an existing function in all modern traffic signal controllers and presents an opportunity for scalable improvement of signal control by replacing contact-closure detection with trajectory data. The second study described in this thesis explores the replacement of conventional passage timer logic, which seeks to measure gaps in traffic, with direct measurement of gaps with trajectory data. A highly customizable and robust signal controller was developed in a simulation framework to facilitate a proof-of-concept study. A trajectory-based control method called “gap seeking” was developed that uses trajectory data to identify gaps in the arriving traffic. A variation on this control method called “advanced gap seeking” searches for gaps at an upstream location. Results of a simulation study demonstrate that the methods are able to deliver significant improvements over conventional control methods. The third study of this dissertation proposes additional trajectory-based actuation methods for green extension and evaluates their performance individually and in combination when implemented under the previously developed gap seeking methods, under conventional control, and in a hybrid detection environment where trajectories are only available on the major street. The proposed methods include type II dilemma zone protection, a method called “queue clearance” that aims to eliminate split failures by extending green until vehicles that arrived in red have cleared the intersection, and two algorithms for accommodating platoons from previous studies, which carry a potential for self-organizing control. Simulation evaluations suggest that these methods individually meet their objectives, with some potential trade-offs in delay. Some combinations of the proposed methods show potential for substantial improvement over existing control methods.

Urban traffic signal control robust optimization against Risk-averse and Worst-case cyberattacks

The integration of autonomous systems in traffic management has become increasingly important as urban populations and vehicle numbers continue to rise, leading to significant congestion. Traditional traffic signal control systems, which rely on fixed timing, are no longer sufficient to handle the dynamic and complex nature of urban traffic. To address these challenges, the proposed explainable Deep Reinforcement Learning (DRL) framework aims to optimize traffic signal control by dynamically adjusting traffic signals based on real-time data. This approach enhances traffic flow efficiency, reduces congestion, and improves overall system performance. The framework leverages Vehicle-to-Everything (V2X) communication, which enables real-time data exchange between vehicles, infrastructure, and other road users, extending the perception range of autonomous vehicles and providing valuable insights for traffic signal optimization. Additionally, the integration of smart infrastructure, such as smart intersections, plays a crucial role in enabling adaptive traffic management and facilitating better coordination across multiple intersections. One of the key advantages of the proposed system is its transparency, achieved through the implementation of explainable AI (XAI) techniques. These mechanisms provide clear insights into the decision-making processes, ensuring that traffic management authorities and system users can understand the rationale behind the system’s decisions. Although challenges such as data accuracy, scalability, and cybersecurity risks remain, the proposed DRL framework shows great promise in revolutionizing traffic management systems. Future research directions include enhancing data collection methods, improving the scalability of the system for larger cities, and further developing explainability features to improve trust and adoption in real-world applications.

A traffic signal system established due to the congestion of vehicles at road intersections. Traffic choking at road convergence becomes a controversy for daily riders and proportionally allowing the violation of traffic rules or other hazards. The conventional traffic signal system has lots of disadvantages. Even the system is automatic but due to fixed time range it gives rise to other problem like traffic violation, air pollution indirectly this would lead to deceitful affair. The signal system has been executed using different types of controller some have fast execution time, a greater number of inputs and outputs, reliability also plays an important role in traffic signal system design. To overcome such issues, we proposed a design in this paper which works on the compactness of vehicles on traffic signals and also detects the traffic signal breach done by the vehicles. This paper shows the simulation of dynamic traffic signal and traffic breach control system with IR sensor or piezo sensor to detect the density of vehicles and traffic breach or jumping of red light. This paper related with an FPGA controller as a VLSI design using Verilog in Xilinx software.

The synergy between optimization and control is a long standing tradition. In fact, this synergy is becoming more and more apparent because of the multi-disciplinary character of the most pressing, current engineering problems along with constant developments of these two fields. Historically, optimization methods have helped the control community to achieve their design goals formalized in some sort of objective function. On the other hand, control theory has provided a setting to interpret complicated aspects of optimization algorithms. In this thesis, we address three problem instances that lie on the boundary of optimization and control. We employ tools from one field to address a problem in the other field. Fundamentally, our proposed methods share a similar character: their analysis techniques are trajectory-based. In simple words, our proposed methods exploit the trajectories generated by the dynamics that represent each problem instance. The first problem focuses on a 2nd-order, damped differential equation (ODE). This ODE along with its numerous variations have been used to develop or analyze various optimization algorithms, known as fast methods. As an alternative to the existing methods, we first amend the underling ODE with two types of state-dependent inputs, and then extend the resulting controlled dynamics to two hybrid control systems. Employing a trajectory-based analysis, both control laws are constructed to guarantee exponential convergence in a suboptimality measure. To show that the trajectories generated by each hybrid control system are well-posed, we demonstrate Zeno-freeness of solution trajectories in both cases. Furthermore, we propose a mechanism to determine a time-discretization step-size such that the resulting discrete-time hybrid control systems are exponentially stable. Event-based implementation of control laws have received a lot of attention during the past decade. The reason for this interest is the hope to reduce the conservatism involved in the traditional periodic implementation. In the second problem of this thesis, we introduce an event-based sampling policy for a constraint-tightening, robust model predictive control (RMPC) method. The triggering mechanism is a sequence of hyper-rectangles constructed around the optimal state trajectories. In particular, the triggering mechanism's nature makes the proposed approach a suitable choice for plants without a centralized sensory node. A key feature of the proposed method is its complete decoupling from the RMPC method's parameters, facilitating a meaningful comparison between the periodic and aperiodic implementation policies. Furthermore, we provide two types of convex formulations to design the triggering mechanism. The last problem we focus on in this thesis is also related to the event-based implementation of a control law. However, the main aim here is to propose an entity that can be utilized by a real-time engineer to schedule tasks in a networked structure. A common entity provided in the literature related to event-triggering approaches is the minimal inter-execution time (to show the avoidance of a Zeno behavior in the closed-loop system). Nonetheless, such a quantity is extremely conservative when used for scheduling purposes. In this problem, we consider an $\mathcal{L}_2$-based triggering mechanism introduced in the literature and propose a framework to construct a timed safety automaton that can capture the triggering instants generated by this mechanism. In our analysis, we borrow some tools from stability analysis of delayed systems along with reachability analysis to construct the desired timed safety automaton.

Introduction Drag on city traffic has escalated to be a pivotal problem in almost all cities in the world, and commuter travel time, fuel consumption and emission have also soared. Fixed-time signal control systems have been traditionally used in traffic signal control, but their optimality highly depends on the time of day they are applied and they are not capable of real-time traffic control. In order to overcome these drawbacks, intelligent and adaptive traffic control approaches are being explored by both scientists and traffic developers. Objective The purpose of this research is to create and assess a dynamic traffic light control system by utilizing Snake Optimization (SO), a novel meta-heuristic algorithm. In order to minimize vehicle queues, cut down on delays, and enhance overall traffic flow, the main goal is to examine how well SO optimizes traffic signal timings based on real-time traffic data. by contrasting conventional static control techniques and meta-heuristics with the SO-based dynamic approach. Proposed Method To find the best traffic light phase durations at intersections, the suggested approach uses the Snake Optimization (SO) algorithm, a bio-inspired meta-heuristic technique. Real traffic data from the Algerian city of Algiers, which depicts realistic traffic volumes and patterns, is used to test the method. The SUMO (Simulation of Urban Mobility) simulator, an open-source program popular for simulating microscopic traffic, is used to construct the experimental framework. Traffic scenario modeling, control algorithm implementation, and performance evaluation in a simulated urban setting are all made possible by SUMO. The following steps are part of the optimization process: gathering of data, using the SUMO simulator to model traffic flow and intersections, incorporating the SO algorithm to modify traffic light timings in real time in response to inputs, evaluation of performance using metrics like vehicle throughput, average waiting time, reduction of CO2 emission and total travel time, and evaluation of the benefits obtained by the SO-based approach through comparison with a baseline static control system and ACO-DSOS meta-heuristic. Conclusion According to the simulation results, the suggested approach, which is based on the Snake Optimization algorithm, performs better than other meta-heuristics and conventional static control systems. The algorithm's effectiveness in controlling traffic flows is validated by quantitative comparisons. According to the study's findings, SO is a practical and successful strategy for maximizing traffic light control in practical settings.

The traffic light control system is a key component of urban traffic management, and its performance directly impacts the smooth flow of traffic and traffic safety. Based on some issues existing in the current traffic signal control systems, this paper proposes a novel traffic light control system based on intelligent algorithms. This system combines artificial intelligence technology with traditional traffic signal control methods to achieve intelligent control of traffic signals, thereby enhancing traffic efficiency and road safety. The paper provides a detailed explanation of the system's design principles, algorithm implementation, and experimental results. Comparative experiments demonstrate that the system has significant advantages in reducing traffic congestion and improving vehicle passage efficiency. Finally, the paper discusses possible future improvement directions and application scenarios, envisioning the development prospects of traffic light control systems in the future.

To utilize the utmost capacities of junctions with traffic signals, having preemptive and predictive capabilities with integrated traffic sensors are important elements for an intelligent traffic signal control system. Recently, an efficient and preemptive traffic signal control strategy known as iterative tuning (IT) has been developed by exploring repetitive characteristic of traffic demand on working days, whereas on weekends or days with special events, severe nonrepetitive disturbances limit the performance of IT strategy practically. This paper proposes a preemptive and predictive methodology, i.e., IT strategy with reactive compensation (ITRC), to reduce network delay time and eliminate unnecessary stops for vehicles. With anticipation of repetitive traffic flows based on historical data, nominal traffic signals are tuned by IT controller over repetitions. The reactive compensation, which is junction-based model predictive control strategy (JMPC), makes adjustments on nominal traffic signals and compensates the nonrepetitive elements. Rigorous analysis provides sufficient condition for guaranteeing the convergence of ITRC. Case study of Commonwealth Avenue in Singapore with heavy traffic from morning peak to evening peak hours is presented, and the simulation results show the effectiveness, robustness, and convergence of ITRC over pure IT strategy and pure JMPC strategy for scenarios with various traffic variations, respectively.

The occurrence of traffic accidents in cities is often accompanied by property losses, environmental pollution, casualties, and congestion. Predicting the spatio-temporal range of accident-induced congestion can mitigate the negative effects by taking appropriate measures to respond to traffic accidents in a timely manner. Unlike most existing traffic accident spatial-temporal prediction strategies that depend on existing traffic models, this paper proposes a model-free method by using the macroscopic road network images, which relieves the restriction of precise modeling of traffic dynamics and the detailed traffic data. Specifically, we first design a digital twin road network to observe the traffic operation from a macro perspective. Then, after designing the structure of the Convolutional LSTM (Conv-LSTM) cell, we stack multiple Conv-LSTM layers to form an encoding-decoding structure to predict spatio-temporal congestion caused by accidents in urban road networks. Finally, the simulation results indicate that the proposed method improves the prediction accuracy compared with the model-based method and the LSTM network model. The proposed strategy provides a new approach to predict the spatio-temporal congestion caused by accidents from a macroscopic perspective.

One of the pivotal challenges presented to urban traffic controllers is the effective utilization of the transport infrastructure, as a result of the growing urbanization and of the increasing number of vehicles. In response to this challenge, here we investigate the use of Automated Planning for reducing congestion and maximizing the exploitation of urban networks, by generating routes for the vehicles navigating through the controlled region. Empirical analysis is performed on four case studies (including maps of actual UK urban areas) with different complexities and volume of vehicles. The results demonstrate the potential of the presented approach, leading towards an intelligent navigation system, which shows to be able to provide effective personalized directions to vehicles.

The existing urban traffic signal control system exhibits certain inefficiencies, including prolonged waiting times and frequent traffic congestion. Research indicates that the integration of neural networks can enhance the effectiveness of intelligent traffic control systems, potentially improving overall traffic flow and reducing delays. Neural network is a highly parallel, nonlinear and highly redundant system. Meanwhile, the self-learning and self-organizing capabilities of neural network have unique advantages for those systems that cannot establish accurate mathematical models. According to the characteristics of complex, variable and random traffic conditions at urban intersections, this paper analyzes the traffic signal control by using neural network control method. This paper introduces a design method of intelligent traffic light based on dynamic time adjustment of traffic flow. By detecting the traffic flow on site, and then allocating the green time of each lane according to the neural network algorithm, the traffic flow dynamic adjustment can be realized, and the traffic capacity of the intersection can be effectively improved. This kind of intelligent control traffic lights can effectively control traffic, and has obvious effects on dreading traffic flow, improving road capacity and reducing traffic accidents.