실시간 퍼지 시간논리구조를 이용한 교차로 네트워크의 모델링과 제어

본 논문은 Crisp Discrete Event System(CDES)에서 다룰 수 없는 특성을 가지는 의료진단이나 교통제어와 같이 애매하거나 불확실한 판단 그리고 관련성이 모호한 판단의 근거들에 의해 결정되어지는 사건들로 이루어진 Fuzzy Discrete Event System(FDES)의 모델링 방법과 그 응용에 대하여 연구하였다. 일반적인 CDES는 모델링 방법이 많이 연구되어져 왔으나, FDES는 발생되어지는 사건들의 정성적인 특성과 적용되어지는 경우가 드문 이유로 거의 연구되어져 있지 않다. 본 논문에서는 Fuzzy Timed Transition Petri Net(FTTPN)으로 FDES인 교통 시스템을 모델링하고 교통 신호제어기를 설계하였다. This paper deals with modeling method and application of Fuzzy Discrete Event System(FDES). FDES have characteristics which Crisp Discrete Event System(CDES) can't deals with and is constituted with the events that is determined by vague and uncertain judgement like biomedical or traffic control. In general, the modeling method of CDES has been studied many times, but that of FDES hasn't been nearly studied by qualitative character and scarcity of applicated system. This paper models traffic system with FDES's character in FTTPN and designs a traffic signal controller.

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.

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

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.

Intelligent Traffic Lights: Green Time Period Negotiaton

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.

Adaptive multi-objective reinforcement learning with hybrid exploration for traffic signal control based on cooperative multi-agent framework

Traffic signal control plays a pivotal role in the management of urban traffic flow. With the rapid advancement of reinforcement learning, the development of signal control methods has seen a significant boost. However, a major challenge in implementing these methods is ensuring that signal lights do not change abruptly, as this can lead to traffic accidents. To mitigate this risk, a time-delay is introduced in the implementation of control actions, but usually has a negative impact on the overall efficacy of the control policy. To address this challenge, this paper presents a novel Traffic Signal Control Framework (PRLight), which leverages an On-policy Traffic Control Model (OTCM) and an Online Traffic Prediction Model (OTPM) to achieve efficient and real-time control of traffic signals. The framework collects multi-source traffic information from a local-view graph in real-time and employs a novel fast attention mechanism to extract relevant traffic features. To be specific, OTCM utilizes the predicted traffic state as input, eliminating the need for communication with other agents and maximizing computational efficiency while ensuring that the most relevant information is used for signal control. The proposed framework was evaluated on both simulated and real-world road networks and compared to various state-of-the-art methods, demonstrating its effectiveness in preventing traffic congestion and accidents.

Cooperative traffic signal control using Multi-step return and Off-policy Asynchronous Advantage Actor-Critic Graph algorithm

Rapid urbanization and increasing ownership of private vehicles have led to severe traffic congestion, which has become a critical challenge in modern urban areas operated with signalized intersections. This study proposes and evaluates a novel AI-based composite framework for traffic signal control optimization, which integrates a Long Short-Term Memory (LSTM) model for traffic flow prediction, a Deep Neural Network (DNN) for vehicle speed estimation, and a Genetic Algorithm (GA) for signal phase optimization under open-loop and closed-loop objectives. It has been validated through high-fidelity microscopic simulations (i.e. VISSIM), calibrated with field data at the atypical six-leg intersection in Busan, Korea. This study found that while the overall performance of the framework for 24 hours exhibits clear trade-offs (i.e. slight total traffic throughput decrease of 2.2%), its primary value lies in its demonstrated ability to effectively mitigate severe congestion during peak hours (i.e. speed increase of up to 38.5% during the evening peak), delivering significant gains in traffic quality in need. This study also revealed that the system actively performs traffic flow redistribution as an intelligent traffic control strategy. The contributions of this study are threefold. First, it provides a methodological blueprint for an interpretable, modular control framework that decouples prediction processes from optimization, offering a transparent alternative to end-to-end Reinforcement Learning (RL) models. Second, this study offers crucial empirical insight for the evaluation of adaptive traffic control systems. Their true effectiveness may be best measured by peak hour performance rather than 24-hour averages. Finally, this study makes a conceptual contribution by identifying and quantifying the ‘reality gap’ between the predictions of an offline-trained optimizer and the outcomes of a high-fidelity simulation, highlighting a fundamental challenge for the field. HIGHLIGHTS A hierarchical AI framework is an alternative to reinforcement learning model Closed-loop outperforms open-loop in optimizing real time traffic signal control A speed stabilization objective is adopted over a speed maximization approach Leading to tangible gains in traffic quality during peak hour over 24-hour average

Dynamic traffic assignment (DTA) applications require traffic signal control data that are typically difficult to obtain and cumbersome to code in the required format. In addition, the evaluation of future scenarios requires future traffic signal settings consistent with the forecast demand. These signal settings are not known a priori and are costly to estimate. Intuitively, future signal timings need to be reasonably optimized so as to represent what the traffic management agency will do. In the literature, integration between traffic control and DTA models has been formulated as a bi-level or single-level optimization problem with system or user optimal constraints. Most existing solution procedures require certain nested structure with an inner-loop algorithm solving the problem of user-equilibrium or system-optimal assignment and the outer-loop algorithm searching for the optimal signal-timing settings. Most of these solution approaches remain only research tools without practical use because of computational intractability. This research proposes an efficient solution algorithm to the problem. An adaptive traffic signal control model is embedded in a simulation-based DTA model. For each inbound approach at an intersection of interest, the adaptive model uses upstream information and a dynamic rolling-horizon approach to project traffic flow conditions for a dynamic but short (projection) period. The adaptive model provides the signal settings during the entire process of traffic flow simulation and for every iteration of the solution algorithm. Thus, during the entire solution process, the experienced travel times and resulting traffic assignment flows are based on the adaptive (demand-responsive) signal settings, allowing the DTA flows and the adaptive signal settings to be generated simultaneously in a single-loop algorithmic structure. Simulation experiments illustrate the capabilities of the proposed approach.

Adaptive traffic signal control (ATSC) based on deep reinforcement learning (DRL) has shown promising prospects to reduce traffic congestion. Most existing methods keeping traffic signal phases fixed adopt two agent actions to match a four-phase suffering unstable performance and undesirable operation in a four-phase signalized intersection. In this paper, a Double Deep Q-Network (DDQN) with a dual-agent algorithm is proposed to obtain a stable traffic signal control policy. Specifically, two agents are denoted by two different states and shift the control of green lights to make the phase sequence fixed and control process stable. State representations and reward functions are presented by improving the observability and reducing the leaning difficulty of two agents. To enhance the feasibility and reliability of two agents in the traffic control of the four-phase signalized intersection, a network structure incorporating DDQN is proposed to map states to rewards. Experiments under Simulation of Urban Mobility (SUMO) are carried out, and results show that the proposed traffic signal control algorithm is effective in improving traffic capacity.

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.

In order to make it possible to effectively represent deterministic uncertainties and vagueness as well as the human subjective observation and judgement inherent to many real-world problems, especially those in biomedicine, we introduce, in this paper, fuzzy states and fuzzy events and generalize (crisp) discrete event systems (DES) to fuzzy DES. The largely graph-based current framework of the crisp DES is unsuitable for the expansion, and we have thus reformulated it using state vectors and event transition matrices which can be extended to fuzzy vectors and matrices by allowing their elements to take values between 0 and 1. To measure information related to fuzzy DES, we generalize the crisp DES observability. The new observability allows one to determine whether or not the system output observed is sufficient for decision making. Finally, we extend the optimal control of DES to fuzzy DES. The new fuzzy DES theory is consistent with the existing theory, both at the conceptual and the computation levels, in that the former contains the latter as a special case when the memberships must be either 0 or 1. Numerical examples are provided to illustrate the theoretical development.

This study proposes a new multi-input multi-output optimal bilinear signal control method in which a bilinear dynamic model approximation is used to capture the nonlinear dynamics of the urban traffic networks. With signal green time splits as the control input and traffic delay changes as the output for each intersections in the network, a bilinear system model was developed, which, on the basis of linear system modeling, takes interactions among traffic delays and signal timing splits into consideration. Based on the bilinear system modeling framework, we conducted two steps in each time interval to derive traffic control strategies: (1) we used the normalized least-squared algorithm to estimate system parameters; and (2) we solved an online optimization problem to obtain the updated traffic control inputs for the signal timing that minimizes future traffic delays. We evaluated the proposed method in a microscopic traffic simulation environment (VISSIM) with a 35-intersection network of Bellevue city in Washington. Two different traffic demand patterns: (1) normal traffic demands; and (2) time-varying traffic demands were simulated to compare the performance of different control strategies. Experimental results show that (1) the proposed bilinear system model can better describe traffic system dynamics than linear-model based methods, such as our previously developed linear-quadratic regulator control; and (2) the proposed method outperforms the state-of-the-art signal control strategies, namely the max-pressure and the self-organizing traffic light control methods. We have also shown that the proposed method is applicable to all other possible network layouts and signal controller phasing structures. IEEE