An optimization similarity fuzzy inference method for traffic signal control at an isolated intersection

City intersection traffic signal control is an important method to improve the efficiency of road network and alleviate traffic congestion. This paper researches traffic signal fuzzy control method on a single intersection. A two-stage traffic signal control method based on traffic urgency degree is proposed according to two-stage fuzzy inference on single intersection. At the first stage, calculate traffic urgency degree for all red phases using traffic urgency evaluation module and select the red light phase with large traffic urgency as the next phase to switch. At the second stage, green delay of the current green phase is determined by fuzzy inference based on the amount of vehicles of current green phase and next green phase. The average vehicle delays are used to evaluate the performance of the fuzzy signal controller. Finally, comparisons have been made with pretimed controller and fuzzy logic controller without considering the urgency of red phase. Simulation results show the performance of our proposed method.

The prediction and control of short-term traffic flow have become one of the key fields of urban traffic research. However, there are some issues to be addressed, such as the low accuracy of current urban road short-term traffic flow prediction and the high traffic congestion rate resulted from most urban road traffic signal control methods. In this paper, we propose a short-term traffic flow prediction method based on graph convolution neural network and Seq2Seq (Sequence to Sequence) model, which can excavate the spatial and temporal relationships among road traffic flows and jointly perform multi-step predictions to realize a more accurate prediction of short-term traffic flows. In addition, a regional traffic signal control method based on the back-pressure algorithm is proposed, which is based on single-point traffic signal control to establish a distributed regional traffic signal coordination control model, thus effectively realizing dynamic traffic control with a lower congestion rate. Compared to other related methods, we compare and simulate the current short-term traffic flow prediction and urban road traffic signal control methods via actual experiments to demonstrate the practicability of our method. Based on the two method, we conduct two experiment using real world data and compare our method with the current traffic flow prediction and traffic signal control method. After that, we get three conclusions. First, our short-term traffic flow prediction method is clearly outperformed than the LSTM algorithm and ARIMA algorithm in terms of root mean square error, average absolute error and average absolute percentage error. Second, compared with the timing traffic signal control method, our regional traffic signal control method greatly reduces the average waiting time, average travel time and average number of waiting vehicles. Besides, compared to the single-point traffic signal control method, the experiment presents that our method makes a significant improvement in reducing the traffic congestion rate.

Research on optimization method for traffic signal control at intersections in smart cities based on adaptive artificial fish swarm algorithm

In the past, the Webster optimal cycle time formula was limited to calculate the optimal cycle from historical data for fixed-time traffic signal control. This paper focuses on the design of an adaptive traffic signal control based on fuzzy logic with Webster and modified Webster’s formula. These formulas are used to calculate the optimal cycle time depending on the current traffic situation which applying in the next cycle. The alternation of the traffic condition between two successive cycles is monitored and handled through the fuzzy logic system to compensate the fluctuation. The obtained optimal cycle time is used to determine adaptively the effective phase green times i.e. is used to determine adaptively the maximum allowable extension limit of the green phase in the next cycle. The SUMO traffic simulator is used to compare the results of the proposed adaptive control methods with fuzzy logic-based traffic control, and fixed-time Webster and modified Webster-based traffic control methods. The proposed methods are tested on an isolated intersection. In this study, real field-collected data obtained from three, four, and five approaches intersections in Kilis/Turkey are used to test the performance of the proposed methods. In addition, to examine the efficiency of the proposed techniques at heavy demands, the arbitrary demands are generated by SUMO for a four approaches intersection. The obtained simulation results indicate that the proposed methods overperform the fixed time and fuzzy logic-based traffic control methods in terms of average vehicular delay, speed, and travel time.

Reinforcement learning is an effective method for adaptive traffic signal control in urban transportation networks. As the number of training rounds increases, the optimal control strategy is learned, and the learning capabilities of deep neural networks are further enhanced, thereby avoiding the limitations of traditional signal control methods. However, when faced with the sequential decision tasks of regional signal control, it encounters issues such as the curse of dimensionality and environmental non-stationarity. To address the limitations of traditional reinforcement learning algorithms applied to multiple intersections, the mean field theory is applied. This models the traffic signal control problem at multiple intersections within a region as interactions between individual intersections and the average effects of neighboring intersections. By decomposing the Q-function through bilateral estimation between the agent and its neighbors, this method reduces the complexity of interactions between agents while preserving global interactions between the agents. A traffic signal control model based on Mean Field Multi-Agent Reinforcement Learning (MFMARL) was constructed, containing two algorithms: Mean Field Q-Network Area Traffic Signal Control (MFQ-ATSC) and Mean Field Actor-Critic Network Area Traffic Signal Control (MFAC-ATSC). The model was validated using the SUMO simulation platform. The experimental results indicate that across different metrics, such as average speed, the mean field reinforcement learning method outperforms classical signal control methods and several existing approaches.

In order to overcome the shortcomings of traffic signal fixed-time control method, a fuzzy control algorithm for urban traffic signal is proposed. The signal phase switching order is adjustable. The improved quantum particle swarm optimization(QPSO) is also introduced to optimize fuzzy control rules of traffic signal controller. Take four-phase traffic signal commonly used in current practice for example. Compared with traffic signal fixed-time control and single fuzzy control method, the control method put forward in this paper can reduce the vehicles’ average delay time in junction. The simulation results show that the proposed algorithm is proved to be an effective and practicable method for urban traffic self-adaptive control.

Traffic signal systems are an essential tool for traffic management in road networks. For the design of traffic signal control, it is not only necessary to address the diverse requirements from different road users but also to consider the various impacts on traffic flow quality, traffic safety, environment, and economic efficiency. However, current evaluation methods for road traffic signal control, such as the assessment of the level of service in guidelines and performance indices in optimisation methods, often evaluate from the one-dimensional perspective - mainly traffic-related aspects. Decisions made accordingly often lack a fair balance of different impacts on all road user groups. Therefore, this thesis aims to address this gap by developing an evaluation method for road traffic signal control that incorporates multidimensional criteria for various road users in a unified framework, hereby termed as Darmstadt Method of Traffic Signal Evaluation (D-MoTSE). Its applicability is analysed through case studies. As a basis for the method development, the basics of traffic signal control and evaluation methods were reviewed and discussed. The literature review concentrates on answering three questions: how to design a traffic signal program, which criteria and road user groups are relevant and how they are considered in the existing evaluation methods. Multiple parameters corresponding to traffic flow quality, traffic safety and environmental impacts are selected as the evaluation criteria in the developed evaluation method. The traffic-related parameters are distinguished for different traffic modes. The multidimensional evaluation criteria are first determined using appropriate simulation or calculation methods. Later on, they are converted into monetary values using established cost rates, and further aggregated to calculate the total cost. During the aggregation, particular weighting factors can be applied to reflect the political or planning preferences for specific criteria or road user groups. The cost and weighting factors can be adjusted dynamically under different situations. Superordinate effects that are of high significance at a macroscopic level can be taken into consideration as and when necessary and in the case that the relevant data are available. The developed evaluation method was applied to four individual traffic signal systems in the City of Darmstadt, Germany, as case studies. The results show that the number of persons that are present at a traffic signal system has a significant impact on the design of traffic signal control. The distribution of the related cost components differs significantly depending on the type of intersection and the traffic signal program. Furthermore, energy consumption and environmental costs take up at least one-third of the total cost, and therefore, should not be neglected in the evaluation of traffic signal control. The evaluation results can be used for comparing alternative traffic signal programs and selecting the optimum solution among them. Recommendations for designing traffic signal control can be derived accordingly. At signalised pedestrian crossings, integration into the coordination with neighbouring intersections can significantly reduce the delay costs for motorised private transport but may lead to higher costs for crossing pedestrians (and cyclists). A signal program with coordination is the optimum solution under the equal weighting of all evaluation criteria. Higher particular weight for pedestrians (and cyclists) is necessary to further reduce the delays for crossing pedestrians (and cyclists). However, it should be emphasised that generally particular weights should only be adjusted moderately in special cases with the support of plausible planning or political reasons. At signalised intersections, it can be observed that public transport priority can but does not necessarily cause disadvantages for the whole traffic. Instead, it leads to a shift of delays from public transport to other modes. The case studies revealed that no general recommendations can be provided for the design of traffic signal control at signalised intersections. The appropriate solution varies from case to case. A further implementation of the developed evaluation method in practice can assist transport engineers and authorities with the development, optimisation, revision and quality management of traffic signal control, both in the planning and operation stage. The chances and the challenges for its implementation are discussed in this thesis.

Urban traffic congestion is often concentrated at urban intersections. An urban road traffic signal control system is needed to prevent problems such as driving delays caused by frequent traffic congestions on trunk lines, exhaust emissions owing to frequent start and stop of vehicles, and fuel wastage due to long idling times. Maximizing the traffic capacity of an intersection and reducing the delay rate of vehicles has always been a problem for traffic control research. The coordinated control of urban traffic signals is regarded as a multi-objective optimization problem. A mathematical model for urban trunk traffic is studied herein. An average delay model, average queue length model, total delay calculation model for vehicles at intersections, and vehicle exhaust emission model are established to obtain an optimization model for a new traffic trunk coordinated control system. In addition, our study combines the fuzzy control theory with the adaptive sequencing mutation multi-objective differential evolution algorithm (FASM-MDEA). This new optimization method for traffic signal control at urban intersections is proposed as a solution for the traffic flow optimization model to solve the problem of traffic signal coordination and control of urban trunk lines. The simulation results demonstrate the effectiveness of the model optimization algorithm proposed in this study.

Optimizing traffic control systems at traffic intersections can reduce network-wide fuel consumption as well as improve traffic flow. While traffic signals have conventionally been controlled based on predetermined schedules, various adaptive control systems have been developed recently using advanced sensors such as cameras, radars, and LiDARs. By utilizing rich traffic information enabled by the advanced sensors, more efficient or optimal traffic signal control is possible in response to varying traffic conditions. This paper proposes an optimal traffic signal control method to minimize network-wide fuel consumption utilizing real-time traffic information provided by advanced sensors. This new method employs a priority metric calculated by a weighted sum of various factors, including the total number of vehicles, total vehicle speed, vehicle waiting time, and road preference. Genetic Algorithm (GA) is used as a global optimization method to determine the optimal weights in the priority metric. In order to evaluate the effectiveness of the proposed method, a traffic simulation model is developed in a high-fidelity traffic simulation environment called SUMO, based on a real-world traffic network. The traffic flow within this model is simulated using actual measured traffic data from the traffic network, enabling a comprehensive assessment of the novel optimal traffic signal control method in realistic conditions. The simulation results show that the proposed priority metric-based real-time traffic signal control algorithm can significantly reduce network-wide fuel consumption compared to the conventional fixed-time control and coordinated actuated control methods that are currently used in the modeled network. Additionally, incorporating truck priority in the priority metric leads to further improvements in fuel consumption reduction.

This paper deals with an offset control of traffic signals using a cellular automaton traffic model. A stochastic optimal control method for distributed traffic signals is modified to achieve coordinated traffic signal control with the proposed offset control method. In the proposed coordinated traffic signal control method, splits of each cycle and common cycle length are calculated using a modified stochastic optimal control method, and then the offset is calculated using an estimation method based on a modified CA traffic model at intervals. Also, simulations are carried out at multiple intersections using a micro traffic simulator. The effectiveness of the proposed coordinated control method is proved by comparing with other traffic signal control methods such as pre-timed signal control, two types of the traditional coordinated control and distributed control.

As a new optimization technique for discrete dynamic systems, approximate dynamic programming (ADP) for the optimization control of a simple traffic signalized intersection is proposed. ADP combines the concepts of reinforcement learning and dynamic programming, and it is an effective and practical approach for real-time traffic signal control. This paper aims at minimizing the average number of vehicles waiting in the queue or the vehicles average waiting time at isolated intersection by using the action-dependent ADP (ADHDP). ADHDP signal controller is designed with neural networks to learn and achieve a near optimal traffic control policy by measuring the traffic states. As shown by the comparison with other traffic control methods, the simulation results indicate that the approach is efficient to improve traffic control at a simple intersection.

Modern roundabouts have been used among many countries. But when the circulatory roadway of a roundabout has more than two lanes, the disorder of the traffic cause big problems. The weaving section where the vehicles enter or leave the roundabout appears to be the critical bottleneck. In this paper, a new method of traffic signal control for modern roundabout is put forward to solve the problem. Traffic signals are installed to control the traffic flow of entries and the left-turn traffic flow on circulatory roadway. Left-turn vehicles on circulatory roadway will stop before red signal to avoid weaving. The methodology including geometric design and traffic signal control of this new method is presented followed by the evaluation of the operational analysis with indexes of capacity and delay. This method has been successfully applied in Xiamen city to solve the very serious traffic congestion problem. By this method the green time of signal and the running lane of circulatory roadway are utilized optimally.

With the development of information technology and the coming of the internet of things, traffic signal control has brought new opportunities and challenges. This paper presents a method of traffic network signal control, which used to optimize traffic signal timing, reduce the delay of vehicles within the network. The method is divided into two levels: intersection control level and network control level. Intersection control level uses a dynamic programming based adaptive algorithm. Network control level uses an optimization algorithm based on conflict decision tree, using the A* algorithm, rolling optimization and feedback control strategies to enhance the real-time, reliability and robustness of the algorithm. The effectiveness of the system has evaluated through an actual traffics network simulation conducted with VISSIM. Results show that the proposed method is significantly better than the time-of-day method in travel time, average delay and other parameters.

Approach to ensure set traffic safety level at signalized intersections

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