Optimal Traffic Control Research Articles

Research on integrated simulation platform for urban traffic control connecting simulation and practice

Though effective in theoretical simulation, the established traffic control models and optimization algorithms will result in model mismatch or even control strategy failure in actual application. However, they are commonly adopted in traffic signal control research, resulting in the unavailability of many exceptional control algorithms in practice. Simulation should function as a bridge between theoretical research and actual application, allowing the gap between the two to be communicated and made up for. However, an effective connection between the two has yet to be established to enable simulation methods in existing traffic control research. To this end, we designed and developed a simulation platform for "Online Application—HILS (Hardware-in-the-Loop Simulation)—Practice" integration over traffic signal control. In this paper, the architecture and characteristics of the integrated simulation platform were described. Besides, the function of each module of the platform was detailed, followed by listing simulation examples for six complex scenarios, with the active control scenario being selected for simulation comparison analysis. The findings demonstrated extensive road network simulation with the integrated simulation platform, multidimensional control variables, control strategies with support, as well as stable and reliable operation. It can be used to verify several sorts of traffic control simulation with variable dimensions.

МОДЕЛЬ ПРОГНОЗИРОВАНИЯ ТРАНСПОРТНОГО ПОТОКА НА ОСНОВЕ НЕЙРОННЫХ СЕТЕЙ ДЛЯ ПРЕДСКАЗАНИЯ ТРАФИКА НА ДОРОГАХ

In connection with the industrialization of modern society, the growth of the transport systemsof our country, an increase in certain necessary for the development of the needs of the citizensof our country, the number of vehicles of various types continues to increase every year veryfast, causing huge traffic jams on transport roads, especially in large cities and megacities. Thus,forecasting traffic flows is an important and necessary component of optimal traffic control inmodern conditions of transport network development. As a solution to this problem, this articleaims to analyze and describe the application of artificial intelligence methods, in particular neuralnetworks, which represent a modern approach to modeling in complex and nonlinear situationsthat arise when predicting a traffic flow model. The shown accuracy method is based on the developmentof a neural network to predict the daily traffic flow. The expected traffic flow is then comparedwith the actual dataset recorded on the road section and provided by the infrastructuremanager. In fact, neural networks are able to learn from past situations and predict future situationson the transport network. In this study, various neural network structures were examined, and the simulation results showed that the best predictions were obtained using the multilayerperceptron architecture, which has a good generalization system with a root mean square error of0.00927 with the current set of vehicles. The first part of the article is devoted to defining variousconcepts related to the current research area, including a review of the literature on traffic predictionand neural networks. The second part is devoted to describing the problem of traffic congestionusing forecasting problems and presenting the proposed solution method with an emphasis onartificial neural networks as a means of forecasting demand and its various structures. Then, numericalexperiments are illustrated by analyzing the forecast results after the formation and testingof various neural network architectures.

Evaluation of the tram intelligent system management by an analysis of its key performance indicators for an optimal mixed traffic control in Algeria

Evaluation of the tram intelligent system management by an analysis of its key performance indicators for an optimal mixed traffic control in Algeria

Considerations on reducing fuel consumption and emissions by optimizing traffic control in congested urban areas

The balance between city development, as, for example, an increasing number of working places and emissions control, is a challenge in our days in the urban environment. All new developments in the urban area, either commercial, office or residential, will generate a specific demand for mobility. This new demand will put supplementary pressure on the urban transport system in the influence area. Therefore, approaching it from a macro to micro dimension using transport models would be a benefit for both, as optimizing the traffic control in main junctions in the area of the development will reduce the traffic congestion and delays. The paper deals with effects on reducing fuel consumption and emissions as an effect of optimization of traffic control in main junctions in the areas of new developments.

Retracted] Intelligent Transportation Design Based on Iterative Learning

Most of the existing traffic optimization control methods are based on accurate mathematical models. As an uncertain and complex system, the urban traffic system faces difficulty in accurately calibrating the model parameters. Therefore, the existing methods become very difficult in the actual application process. Based on the massive data contained in the urban traffic system and the repetitive characteristics of traffic flow, this paper proposes a hierarchical traffic signal control method for urban road network based on iterative learning control. The simulation results show that the algorithm can achieve better control effect and can solve the problem of urban traffic congestion more effectively than traditional traffic control methods.

A single-layer approach for joint optimization of traffic signals and cooperative vehicle trajectories at isolated intersections

A joint control approach that simultaneously optimizes traffic signals and trajectories of cooperative (automated) vehicle platooning at urban intersections is presented in this paper. In the proposed approach, the signal phase lengths and the accelerations of the controlled platoons are optimized to maximize comfort and minimize travel delay within the signal cycle, subject to motion constraints on speeds, accelerations and safe following gaps. The red phases are initially considered as logic constraints, and then recast as several linear constraints to enable efficient solutions. The proposed approach is solved by mixed integer linear programming (MILP) techniques after linearization of the objective function. The generated outputs of the MILP problem are the optimal signal timings and the optimal accelerations of all vehicles. This joint control approach is flexible in incorporating multiple platoons and traffic movements under different traffic demand levels and it does not require prespecified terminal conditions on position and speed at the signal cycle tail. The performance of the proposed control approach is verified by simulation at a standard four-arm intersection under the balanced and unbalanced vehicle arrival rates from different arms, taking the released traffic movement numbers, turning proportions, signal cycle lengths and the controlled vehicle numbers into account. The simulation results demonstrate the platoon performance of the joint controller (such as split, merge, acceleration and deceleration maneuvers) under the optimal signals. Based on the simulation results, the optimal patterns of trajectories and signals are explored, which provide insights into the optimal traffic control actions at intersections in a cooperative vehicle environment. Furthermore, the computational performance of the proposed control approach is analyzed, and the benefits of the proposed approach on the average travel delay, throughput, fuel consumption, and emission are proved by comparing with the two-layer approaches using the car following model, the signal optimization models, and the state-of-the-art approach.

Design and Evaluation of a New Machine Learning Toolbox for Optimal Traffic Light Control with SUMO and Tensorflow

Design and Evaluation of a New Machine Learning Toolbox for Optimal Traffic Light Control with SUMO and Tensorflow

Bi-Level Optimization Model for Urban Traffic Control

AbstractThe urban traffic control optimization is a complex problem because of the interconnections among the junctions and the dynamical behavior of the traffic flows. Optimization with one control variable in the literature is presented. In this research optimization model consisting of two control variables is developed. Hierarchical bi-level methodology is proposed for realization of integrated optimal control. The urban traffic management is implemented by simultaneously control of traffic light cycles and green light durations of the traffic lights of urban network of crossroads.

Intelligent optimization of dynamic traffic light control via diverse optimization priorities

Given the distribution difference of the vehicle flow in different urban areas, coordinating the optimization priorities of crossroads in the dynamic control of traffic lights is vital. To reduce traffic congestion at crossroads, in this study, an intelligent diverse optimization priority method (IDOPM) was developed for dynamic traffic light control at crossroads, where diverse optimization priorities can be flexibly and efficiently assigned to different crossroads. The IDOPM mainly consists of a dynamic state constructor and an optimization priority assigner. The dynamic state constructor controls the transformation of the signal combinations of traffic lights. The signal combinations of traffic lights at crossroads are abstracted as cells to be controlled by formulated rules. By designing duration particles and enhancing particles, the optimization priority assigner reconstructs the quantum particle swarm algorithm to assign crossroads with different optimization priorities. The results obtained by comparison with state-of-the-art methods via extensive experiments confirmed the outstanding optimization performance of the proposed IDOPM in dynamic traffic light control.

Boosted Genetic Algorithm Using Machine Learning for Traffic Control Optimization

Traffic control optimization is a challenging task for various traffic centers around the world and the majority of existing approaches focus only on developing adaptive methods for normal (recurrent) traffic conditions. Optimizing the control plans when severe incidents occur still remains an open problem, especially when a high number of lanes or entire intersections are affected. This paper presents a novel methodology for optimizing the traffic signal timings in signalized urban intersections, under non-recurrent traffic incidents. With the purpose of producing fast and reliable decisions, we combine the fast running Machine Learning (ML) algorithms and the reliable Genetic Algorithms (GA) into a single optimization framework. Firstly, we deploy a typical GA algorithm by considering the phase duration as the decision variable and the objective function as the total travel time in the network. We fine tune the GA for crossover, mutation, fitness calculation and obtain the optimal parameters. Secondly, we train several regression models to predict the total travel time in the studied traffic network, and select the best performing model which we further hyper-tune. Lastly, we propose a new algorithm BGA-ML combining the GA algorithm and the extreme-gradient decision-tree (XGBT), which is the best performing regression model, together in a single optimization framework. Comparison and results are generated by two experiments (one synthetic and one from real urban traffic network) and show that the new BGA-ML is much faster than the original GA algorithm and can reduce the total travel time by almost half when used under incident conditions.

A System Optimal Speed Advisory Framework for a Network of Connected and Autonomous Vehicles

The technological advancements involving information and communication technologies (ICT), such as Connected and Automated Vehicles (CAVs) and the Intelligent Transport Systems (ITS), have enabled new efficient traffic control and management strategies to mitigate traffic congestion. Specifically, the combined traffic flow-speed advisory systems based on CAVs and ITS technologies could provide the individual vehicle with the optimal speed to reduce the fuel consumption, the number of stops, simultaneously reduce the network-wide traffic congestion and improve road safety. This article develops a novel bi-level control framework underpinned by the mutual interaction between a system optimal traffic flow control strategy at a network level and a speed control policy for an individual vehicle at a link level within a connected traffic environment. Our framework proposes the novel group-based method to guarantee the consistency and interaction between the macroscopic and microscopic models. To this end, it efficiently optimizes vehicular trajectories while meeting the network-wide objectives which have not been investigated previously in the literature. We propose an efficient algorithm for this problem that iteratively solves mixed-integer linear programming (MILP) models for each upper and lower level. Numerical results indicate the effectiveness of the proposed speed advisory method in vehicular emission reduction, favorable network queue formation, and its positive influence on traffic flow patterns over the network.

Design and Implementation of an Emergency Traffic Light Pre-emption System Using PLC Automation

As traffic on the urban Ghanaian road network increases, congestion and delays are becoming more severe. Traffic signals are the most common method by which right of way is dynamically allocated to conflicting movements. Ghanaian traffic light control system is mainly designed by using devices such as timers, relays etc. The critical timing operation is required to be carried out under the existence of heavy traffic situations. This does not give this conventional traffic light control system the flexibility to control throughput to favor emergency vehicles. This conventional practice leads to many problems and subsequent delay for a long time. With the help of a PLC, the requirement of fast automation and effective optimization of traffic light control even in times of emergency can be achieved.At intersections, capacity bottlenecks are formed in urban road networks because at these locations, capacity must be shared by competing traffic movements. For this reason, this thesis introduces an execution and implementation of T-junction emergency traffic control system using SEIMENS S7-200 PLC. Programming in PLC is written in ladder logic with the help of STEP7 MICROWIN software.

An optimization model and traffic light control scheme for heterogeneous traffic systems

In this paper, one type of heterogeneous traffic network, which consists of signalized intersections and non-signalized ones, is analyzed and modelled with a macroscopic model and logic constraints, which involves the evolution of the number of vehicles on each link and the leaving flow dynamics at each intersection. We propose a novel model describing the dynamic behaviors of such a system and the proposed model is validated via road tests in Singapore traffic network and simulations in VISSIM. A piecewise linear model is adopted to describe the traffic flow dynamics at each signalized intersection, while a platooning-like first-in-first-out (FIFO) model is proposed to depict the dynamics at each non-signalized intersection. A traffic light control problem for a heterogeneous traffic network is formulated based on the proposed macroscopic model as a mixed integer programming problem. Simulations indicate that the proposed optimal traffic control scheme could outperform the fixed-time traffic light control strategy and conventional actuated traffic light control scheme. Besides, we demonstrate potentials in developing a systematic planning approach on deciding which traffic intersections require signal control to ensure good traffic control performance.

Optimized Feedback-based Traffic Congestion Pricing and Control for Improved Return on Investment (ROI)

Traffic congestion is a serious problem in any developing society. One of the approaches used in addressing this problem is congestion pricing. In this paper, the effects of social behavior on congestion pricing and control were considered and a scenario of a 1 x 2 traffic tolling system is used. Also, this work considers the rate of return on investment (RoI) of toll facilities in order to justify the worthiness of the design to investors. In earlier works on feedback-based traffic congestion pricing, the traffic parameters in the logit expression were selected arbitrarily and this made it difficult for traffic designers to arrive at optimum parameters within a reasonable amount of time. In order to address this challenge, the traffic parameter problem is formulated into a traffic congestion control optimization problem whose goal is to maximize the congestion price. The constraints are boundaries for the traffic parameters and the investment boundary conditions. The fitness of the formulated optimization problem was determined using genetic algorithm (GA). A number of simulations were performed by considering different multiplication factors and results were obtained for each multiplication factor (m.f). The simulation results justify the exactness of the formulated optimization problem and the superior performance of this work over the one that involves manually selection of traffic parameters.

Real-time signal queue length prediction using long short-term memory neural network

Optimal traffic control and signal planning can significantly reduce traffic congestion and potential delays at intersections. However, a major challenge to optimize traffic signal timing is to accurately predict traffic before commencing the next cycle. An optimal strategy cannot be achieved with a poor prediction of future traffic. In this study, using a deep learning approach, we develop a data-driven real-time queue length prediction technique. We consider a connected corridor where information from vehicle detectors (located at the intersection) will be shared to consecutive intersections. We assume that the queue length of an intersection in the next cycle will depend on the queue length of the target and two upstream intersections in the current cycle. We use InSync adaptive traffic control system data to train a long short-term memory neural network model capturing time-dependent patterns of a queue of a signal. To avoid overfitting and select the best combination of hyperparameters, we use sequential model-based optimization technique. Our experiment results show that the proposed model performs very well to predict the queue length. Although we run our experiments predicting the queue length for a single movement, the proposed method can be applied for other movements as well.

DeepGuard: Efficient Anomaly Detection in SDN With Fine-Grained Traffic Flow Monitoring

Software-Defined Networking (SDN) leverages the implementation of reliable, flexible and efficient network security mechanisms which make use of novel techniques such as artificial intelligence (AI) and machine learning (ML). In particular, these techniques - together with SDN - are the key enablers for the design of anomaly detection methods which are based on efficient traffic flow monitoring. In this paper, we tackle this problem by proposing an efficient anomaly detection framework, denoted as DeepGuard, which improves the detection performance of cyberattacks in SDN based networks by adopting a fine-grained traffic flow monitoring mechanism. Specifically, the proposed framework utilizes a deep reinforcement learning technique, i.e., Double Deep ${Q}$ -Network (DDQN), to learn traffic flow matching strategies maximizing the traffic flow granularity while proactively protecting the SDN data plane from being overloaded. Afterwards, by implementing the learned optimal traffic flow matching control policy, the most beneficial traffic information for anomaly detection is acquired at runtime—thereby improving the cyberattack detection performance. The performance of the proposed framework is validated by extensive experiments, and the results show that DeepGuard yields significant performance improvements compared to existing traffic flow matching mechanisms regarding the level of traffic flow granularity. In the case of distributed denial-of-service (DDoS) attacks, DeepGuard achieves a remarkable attack detection performance while effectively preventing forwarding performance degradation in the SDN data plane.

A mixed integer programming formulation and scalable solution algorithms for traffic control coordination across multiple intersections based on vehicle space-time trajectories

Thanks to the development of mobile computing, novel traffic data sources are emerging as the promising building blocks for more effective traffic control strategies. It is expected that the vehicle space-time trajectories will become ubiquitously available in foreseeable future. Real-time trajectory data will provide full-spectrum pattern of traffic dynamics among multiple intersections. In this paper, we present a new traffic control representation for multiple intersections. A new multi-intersection phase (MI-phase) is proposed to represent safe vehicle movements across a few tightly connected intersections. All the intersections are also viewed as one integral “super intersection” within which vehicles move according to their planned paths. Through scheduling the sequence and durations of MI-phases over time, the vehicles will be crossing intersections with minimal delays. This approach can provide more flexibilities for traffic control coordination than the traditional Cycle-Split-Offset approach. A linear integer programming formulation is presented for joint optimization of vehicle space-time trajectories and traffic control. We also design a scalable optimization frame for real-world traffic control optimization, referred to as “Lagrangian decomposition with subproblem approximation” approaches. In this new framework, we construct the dynamic network loading based lower bound estimator (DNL-LBE) in which the relaxed constraints and sensitivity to the Lagrangian multiplier prices are explicitly considered while vehicular flows are being loaded. By doing so, the complex controlled dynamic network loading process can be represented through Lagrangian multipliers interfacing with the MI-phase optimization module (then solved by Dynamic Programming). This approach can facilitate price-based search heuristics to find high quality solutions for both vehicular space-time trajectories and traffic control plans without increasing the overall computing complexity. The efficiency of the proposed optimization framework is further improved through multiple advanced computing techniques. In the end, one demonstrative and one real-world example are provided to show the performance of the new approach.

Optimization of Traffic Light Control Using Fuzzy Logic Sugeno Method

Congestion is one of the big problems around the world, especially for big cities. Intersections are the scene of congestion because the lane is the meeting point of two or more roads which has a major influence on the smooth flow of vehicles on the road network. This congestion occurs due to various factors, one of which is the statistical traffic light duration, which does not match traffic conditions. Based on this, there needs to be a development in the timing of a more adaptive green light. This study describes the design of a traffic light controller using the Sugeno method fuzzy logic. This study aims to design a green light duration calculation by applying fuzzy logic that results in adaptive traffic light duration at intersections, by entering the density of each intersection path, which is divided into 4 inputs, namely regulated lane density, opposing lane density I, and opposite lane density. II, the density of the opposite lane III, with the aim of the system being able to produce a duration that is in accordance with the current traffic situation with an output in the form of a green light duration on the regulated lane.

A verification method for identifying critical segments considering highly correlated characteristics of traffic flow

Since critical segments on a transportation network vary over time and are determined by the nature of traffic systems, the identification of critical segments is the basis for realizing area-wide traffic coordination control and regional traffic state optimization. For decades, the identification of critical segments of dynamic traffic flow networks has attracted wide attention. In recent years, some important advances have been made in the related research on the identification of critical segments using the theory of percolation which validates the impact of critical segments by increasing the speed value of critical segments. However, most of them failed to take into account highly correlated characteristics between adjacent segments, which causes identification results cannot be validated effectively and efficiently. In this paper, we improve the existing critical segments identification methods by considering the highly correlated characteristics. A verification method based on ego-networks is proposed that improves the ego-networks speed of critical segments to verify the accuracy of identification results. The experiment shows the method can verify the validity of critical segments recognition results more accurately.

Traffic Light Regulatory System Based on Fuzzy Algorithm Using Microcontroller

Basically, control system for traffic lights only perform control based on a fixed time. In fact, the arrival rate of vehicles at the intersection of the road is not always the same so of course congestion at the crossroads can not be controlled. This study presents a simulation control system which can perform automatic traffic light control based on vehicle arrival rate. In this control, the duration of the flame is not constant but follows the arrival rate of vehicles. The method used to control the flame duration is fuzzy logic algorithm with the Mamdani fuzzy reasoning using Matlab software. Test results based on the simulation of fuzzy logic in fuzzy MatLab toolbox show that the fuzzy logic algorithm can be used to meet the goal of optimal traffic control, the duration of a given time based on the arrival rate of vehicles. The higher rate of arrival of the vehicle, the longer the duration of a given time, and the lower rate of the arrival of the vehicle, the more briefly given time duration.