Traffic Control Algorithm Research Articles

Traffic control algorithms for mixed vehicle traffic – A simulation-based investigation

A library of software tools, that implements various motorway traffic control algorithms in a generic way for any network topology given by the user, has been developed and integrated with the advanced simulation environment VSimRTI. The control algorithms have been thoroughly assessed via comprehensive simulation investigations involving a large variety of penetration rates for CAVs, infrastructure types and capabilities, and traffic conditions (free flow, critical, congested). The most promising strategy appears to be an ACC parameter adaptation strategy that is presented together with the simulation evaluation results for a test site at Girona (Spain).

Cooperative Game Based Traffic Control Scheme for the Dual-channel CAN Platform

In recent years, automotive manufactures have installed dozens of electronic controller units (ECUs) for various functions related to safety and convenience of drivers. Each ECU controls specific functions of a vehicle, and exchanges information by using the controller area network (CAN). It is characterized by low cost, efficient communication, and high flexibility. In this work, we aim to study the property of CAN bus, and design a new traffic control algorithm to get an excellent CAN system performance. By employing the concepts of two different cooperative game solutions, our approach explores the impact of ECUs' mutual-interaction relationship and devises a novel two-step game model to take the full advantages of CAN resource sharing process. To adaptively handle different data service requirements, control decisions in our two-step game are mutually dependent each other, and each individual ECU acts cooperatively in the proper collaboration manner. This interactive coordinated process continues until a desirable solution is obtained; it is a practical and suitable approach in real world CAN system operations. Finally, simulation testbed is constructed and the numerical analysis is conducted to demonstrate the performance improvement of our proposed method. In addition, several research challenges are discussed and open issues are also outlined.

Study of Energy Efficiency Benchmarking Parameter based on Real Traffic and Energy Data of a high-speed Traction Lift in Hong Kong

A holistic normalization method (So et al 2005, Lam et al 2006) was developed, which can simultaneously assess both drive efficiency and traffic control performance on a real-time basis of a lift system, termed <J/kg-m> measured in the unit, J/kg/m. Since 2012, it has been adopted by the HKSAR Government as an emerging good engineering practice in the Technical Guidelines of the Building Energy Code. A study of the parameter based on pure simulations using artificial traffic patterns and simple energy patterns was conducted (So et al 2018), recommending that a value of 40 J/kg/m could indicate an efficient system. In this article, the study is conducted again based on real passenger demand and real energy profiles on a bank of seven lifts serving a super-high-rise luxurious office building in Hong Kong, with a conclusion that the previous recommendation is still valid, supported by real data. The study includes variation by simulation in traffic control algorithms and the number of lifts of the bank to study the "undersizing effect" with the same passenger demands throughout a typical weekday, from morning to evening.

A semi-decentralized feudal multi-agent learned-goal algorithm for multi-intersection traffic signal control

Traffic signal control helps to reduce traffic congestion and thus has been studied for a few decades. Algorithms for multi-intersection traffic signal control that adopt hierarchical deep reinforcement learning have been shown to achieve state-of-the-art performance. For these methods, a two-level hierarchical structure is generally used, in which the low-level policies employ latent goals induced by the high-level policies. Existing research either designs the latent goals manually or acquires the latent goals from the environment. However, these latent goals are not optimal, which leads to the non-optimal low-level policies. To improve this, we propose a learned-goal soft actor–critic (LSAC) algorithm, by which the optimal latent goals are automatically learned and then are used in low-level policies. We then propose a semi-decentralized feudal multi-agent (SFM) framework, which can alleviate the problem that existing multi-agent framework faces, i.e., the fast growing state space with the increase of the number of agents. Combining the above two proposed methods, an SFM-LSAC algorithm is proposed in this paper for multi-intersection traffic signal control. Other techniques are also incorporated into the SFM-LSAC algorithm such as attention mechanism. Experimental results in three real-world traffic scenarios illustrate that the SFM-LSAC algorithm outperforms other four state-of-the-art multi-intersection traffic signal control algorithms, i.e., reduces the average intersection delay, the average queue length, the average travel time, meanwhile improves the average flow and average travel speed, thus could be a new promising algorithm for multi-intersection traffic signal control.

A Path-Server Traffic Scheduling Algorithm for Wireless Network Load based on SDN

At present, the research on flow scheduling optimization around SDN has become the focus of international attention. Based on the current traffic control algorithm, especially the shortcomings of traffic scheduling optimization, Path-Server Traffic Scheduling (PSTS) algorithm is proposed in this paper to solve problems. Based on the real-time monitoring of network on Ryu controller, network topology, network load and server load information were obtained to choose the optimal routing scheme, thus achieving the goal of improving the overall network performance. Through the interaction between the components, the work flow of the Ryu controller is designed, and the channel quality information among users is maintained by the link state management module. When the small base station receives the data packet sent by the SDN exchange price, it will temporarily store it in the data cache module. Then, according to the amount of cached data and the channel quality of each user, the optimal time slot of the wireless network resource allocation scheme is comprehensively determined, and the data packet is sent to the corresponding client. In the view of proposed design, the Mininet simulation platform is used to simulate the SDN network in this paper. Based on the simulation platform, the performance of the algorithm is analyzed and compared. Besides, bandwidth utilization, average transmission delay, system utility and terminal usage change, interrupt rate and terminal usage change of different algorithms in SDN wireless network are analyzed and compared through data. All experiments have proved that the research content proposed in this paper has an obvious effect on network load control, which shows network performance.

Joint Traffic Control and Multi-Channel Reassignment for Core Backbone Network in SDN-IoT: A Multi-Agent Deep Reinforcement Learning Approach

Channel reassignment is to assign again on the assigned channel resources in order to use the channel resources more efficiently. Channel reassignment in the Software-Defined Networking (SDN) based Internet of Things (SDN-IoT) is a promising paradigm to improve the communication performance of the network, since it allows software-defined routers (SDRs) with the help of SDN controller to appropriately schedule the traffic loads to meet the better transaction of corresponding channels in one link. However, the existing channel reassignment works have many limitations. In this paper, we develop a joint multi-channel reassignment and traffic control framework for the core backbone network in SDN-IoT. Comparing to classic performance metrics, we design a more comprehensive objection function to maximize the throughput and to minimize packet loss rate and the time delay by scheduling the appropriate traffic loads to corresponding channels in one link. We develop a Multi-Agent Deep Deterministic Policy Gradient (MADDPG)-based traffic control and multi-channel reassignment (TCCA-MADDPG) algorithm to optimize the objection function to achieve traffic control and channel reassignment. To tackle the dynamics and complexity of the core backbone network, we use the traffic prediction result as the part of the channel state information. In order to make better use of the time continuity of the channel state, we add an LSTM layer to the neural network in the experiment to capture the timing information of the channel. Simulation results show that the proposed algorithm converges faster and outperform existing methods.

Lightweight PVIDNet: A Priority Vehicles Detection Network Model Based on Deep Learning for Intelligent Traffic Lights.

Minimizing human intervention in engines, such as traffic lights, through automatic applications and sensors has been the focus of many studies. Thus, Deep Learning (DL) algorithms have been studied for traffic signs and vehicle identification in an urban traffic context. However, there is a lack of priority vehicle classification algorithms with high accuracy, fast processing, and a lightweight solution. For filling those gaps, a vehicle detection system is proposed, which is integrated with an intelligent traffic light. Thus, this work proposes (1) a novel vehicle detection model named Priority Vehicle Image Detection Network (PVIDNet), based on , (2) a lightweight design strategy for the PVIDNet model using an activation function to decrease the execution time of the proposed model, (3) a traffic control algorithm based on the Brazilian Traffic Code, and (4) a database containing Brazilian vehicle images. The effectiveness of the proposed solutions were evaluated using the Simulation of Urban MObility (SUMO) tool. Results show that PVIDNet reached an accuracy higher than 0.95, and the waiting time of priority vehicles was reduced by up to 50%, demonstrating the effectiveness of the proposed solution.

DTCMS: Dynamic traffic congestion management in Social Internet of Vehicles (SIoV)

With the augmentation of traffic exponentially, we observe that traffic congestion does not guarantee road safety or enhance the driving experience. In the recent past, Social Internet of Vehicles (SIoV), a social network paradigm permits social relationships among every vehicle in the network or with any road infrastructure to render a radically useful environment. SIoV is beneficial for the drivers, in improving road safety, avoiding mishaps, and providing a friendly-driving experience. In this paper, we propose a traffic scheduling algorithm to gain the maximum throughput for the flow of vehicles at a road intersection with the formation of social relationships among the vehicles and with the Road Side Units (RSUs). The algorithm estimates the flow rate of vehicles for lanes at the intersections exploiting the volume of traffic moving through the given road. A condition matrix is designed for the consistent movement of traffic considering different routes on the road segments. Social relationships are devised on various aspects of travel needs for a safe, agile, and better driving experience. Simulation results illustrate the efficacy of the proposed scheme with high traffic throughput, service rate and reduce the total travelling time, delay time, and average waiting time in comparison with Dynamic Throughput Maximization Framework and Adaptive Traffic Control Algorithm.

Network-Cognitive Traffic Control: A Fluidity-Aware On-Chip Communication

A novel network-on-chip (NoC) integrated congestion control and flow control scheme, called Network-Cognitive Traffic Control (NCogn.TC), is proposed. This scheme is cognizant of the fluidity levels in on-chip router buffers and it uses this measurement to prioritize the forwarding of flits in the buffers. This preferential forwarding policy is based on the observation that flits with higher levels of fluidity are likely to arrive at their destinations faster, because they may require fewer routing steps. By giving higher priority to forward flits in high-fluidity buffers, scarce buffer resources may be freed-up sooner in order to relieve on-going traffic congestion. In this work, a buffer cognition monitor is developed to rapidly estimate the buffer fluidity level. An integrated congestion control and flow control algorithm is proposed based on the estimated buffer fluidity level. Tested with both synthetic traffic patterns as well as industry benchmark traffic patterns, significant performance enhancement has been observed when the proposed Network-Cognitive Traffic Control is compared against conventional traffic control algorithms that only monitor the buffer fill level.

ST-DeLTA: A Novel Spatial-Temporal Value Network Aided Deep Learning Based Intelligent Network Traffic Control System

Deep learning has emerged as a popular Artificial Intelligence (AI) technique to make conventional cyber physical systems become intelligent and sustainable. Recently, deep learning has been widely used in the network domain. With the aid of powerful deep neural networks, the communication network can carry out packets forwarding actions intelligently to avoid possible failure and congestion. However, with the high computing cost and process limitation in only the static network scenario, the existing deep learning based network traffic control algorithms cannot satisfy the sustainable requirement of next generation large scale dynamic network. To conquer the existing problems, a novel spatial-temporal value network aided deep learning based intelligent traffic control algorithm referred as ST-DeLTA is proposed in this paper. In ST-DeLTA, the value matrix and spatial temporal training model (ST model) are employed to intelligently extract the spatial as well as temporal features of traffic patterns and make adaptive packets forwarding decision in large scale and dynamic networks. The mathematical analysis gives the computing cost reduction of our proposal, and the computer simulation demonstrates that our proposal has significantly better training and network performance compared with traditional algorithms in terms of training accuracy, transmission throughput, and average packets loss rate.

BACKPRESSURE BASED TRAFFIC SIGNAL CONTROL CONSIDERING CAPACITY OF DOWNSTREAM LINKS

Congestion is a kind of expression of instability of traffic network. Traffic signal control keeping traffic network stable can reduce the congestion of urban traffic. In order to improve the efficiency of urban traffic network, this study proposes a decentralized traffic signal control strategy based on backpressure algorithm used in Wi-Fi mesh networks for packets routing. Backpressure based traffic signal control algorithm can stabilize urban traffic network and achieve maximum throughput. Based on original backpressure algorithm, the variant parameter and penalty function are considered to balance the queue differential and capacity of downstream links in urban traffic network. For each traffic phase of intersections, phase weight is computed using queue differential and capacity of downstream links, which fixed the deficiency of infinite queue capacity in original backpressure algorithm. It is proved that the extended backpressure traffic signal control algorithm can maintain stability of urban traffic network, and also can prevent queue spillback, so as to improve performance of whole traffic network. Simulations are carried out in Vissim using Vissim COM programming interface and Visual Studio development tools. Evaluation results illuminate that it can get better performance than the backpressure algorithm just based on queue length differential in average queue length and delay of traffic network.

Multi-Agent Deep Reinforcement Learning for Urban Traffic Light Control in Vehicular Networks

As urban traffic condition is diverse and complicated, applying reinforcement learning to reduce traffic congestion becomes one of the hot and promising topics. Especially, how to coordinate the traffic light controllers of multiple intersections is a key challenge for multi-agent reinforcement learning (MARL). Most existing MARL studies are based on traditional $Q$ -learning, but unstable environment leads to poor learning in the complicated and dynamic traffic scenarios. In this paper, we propose a novel multi-agent recurrent deep deterministic policy gradient (MARDDPG) algorithm based on deep deterministic policy gradient (DDPG) algorithm for traffic light control (TLC) in vehiclar networks. Specifically, the centralized learning in each critic network enables each agent to estimate the policies of other agents in the decision-making process and each agent can coordinate with each other, alleviating the problem of poor learning performance caused by environmental instability. The decentralized execution enables each agent to make decisions independently. We share parameters in actor networks to speed up the training process and reduce the memory footprint. The addition of LSTM is beneficial to alleviate the instability of the environment caused by partial observable state. We utilize surveillance cameras and vehicular networks to collect status information for each intersection. Unlike previous work, we have not only considered the vehicle but also considered the pedestrians waiting to pass through the intersection. Moreover, we also set different priorities for buses and ordinary vehicles. The experimental results in a vehicular network show that our method can run stably in various scenarios and coordinate multiple intersections, which significantly reduces vehicle congestion and pedestrian congestion.

Performance Analysis of Priority-Based Access Class Barring Scheme for Massive MTC Random Access

In a massive machine-type communication (MTC) environment, a traffic control algorithm is essential to solving traffic congestion problems. Additionally, as MTC applications have diverse quality-of-service requirements, traffic control should be applied in a way that guarantees diverse requirements. Therefore, we propose a priority-based access class barring (PACB) scheme, in which MTC devices are divided into multiple classes according to their delay requirements, and a different access class barring (ACB) factor is used for each class. This article suggests an approach to choose multiple ACB factors that can maximize the number of successful access events and can also guarantee the low delay requirement of the high-priority class. Then, an analytical model is proposed for the ideal case, and throughput, drop probability, and access delay are derived to evaluate the performance. For a realistic environment, we also suggest an estimation algorithm for the PACB scheme, in which a base station estimates the number of active devices for each class. Numerical and simulation results showed that the analytical model is consistent with the simulation results. Additionally, the performance of the estimation algorithm was similar to that of the ideal case. Furthermore, the proposed PACB scheme can decrease the delay of a high-priority class and improve the throughput and drop probability compared to existing algorithms.

Monte Carlo Tree Search-Based Mixed Traffic Flow Control Algorithm for Arterial Intersections

A model-free approach is presented, based on the Monte Carlo tree search (MCTS) algorithm, for the control of mixed traffic flow of human-driven vehicles (HDV) and connected and autonomous vehicles (CAV), named MCTS-MTF, on a one-lane roadway with signalized intersection control. Previous research has often simplified the problem with certain assumptions to reduce computational burden, such as dividing a vehicle trajectory into several segments with constant speed or linear acceleration/deceleration, which was rather unrealistic. This study departs from the existing research in that minimum constraints on CAV trajectory control were required, as long as the basic rules such as safety considerations and vehicular performance limits were followed. Modeling efforts were made to improve the algorithm solution quality and the run time efficiency over the naïve MCTS algorithm. This was achieved by an exploration-exploitation balance calibration module, and a tree expansion determination module to expand the tree more effectively along the desired direction. Results of a case study found that the proposed algorithm was able to achieve a travel time saving of 3.5% and a fuel consumption saving of 6.5%. It was also demonstrated to run at eight times the speed of a naïve MCTS model, suggesting a promising potential for real-time or near real-time applications.

Adaptive traffic signal control algorithms based on probe vehicle data

In this paper, two new adaptive traffic signal control algorithms are proposed based on data from probe vehicles to realize the coordinated signal control of arterial roads. One is an iterative signal control algorithm, and the other is an optimized signal control algorithm. The proportion of vehicles in the nonstop group is selected as the indicator of the traffic state. The value for this indicator can be accurately estimated by data from probe vehicles. Our goal is to ease traffic congestion and enhance the capacities of traffic networks. Compared with the Webster fixed-time signal control algorithm, these two new adaptive signal control algorithms are evaluated on a microscopic simulation platform. Simulation results show that the average travel time is reduced by 32% under the iterative signal control algorithm and by 23% under the optimized signal control algorithm, and the average delay times are reduced by 36% and 35%, respectively. In the meantime, the average number of stops under the iterative signal control algorithm is reduced by 43%, and under the optimized signal control algorithm, by 67%. They indicate that the two new adaptive signal control algorithms are effective for easing traffic congestion and achieve the adaptive signal control objectives using real-time traffic information.

Multi-agent deep neural networks coupled with LQF-MWM algorithm for traffic control and emergency vehicles guidance

Authorities in modern cities are facing daily challenges related to traffic control. Due to the problem complexity caused by the urbanization growth, investing in developing traffic signal control systems (TSCS) to guarantee better mobility has taken more attention by these authorities. In the existing literature, the majority of TSCS offers only a real-time control for a detected traffic problem without considering early prediction and estimation of its occurrence. Furthermore, traffic problems related to the arrival and guidance of emergency vehicles are rarely considered. Based on these gaps, we rely on concepts and mechanisms from both, the Artificial and the convolution neural networks (ANN and CNN), coupled with the longest queue first maximal weight matching algorithm (LQF-MWM), to develop PANNAL, a predictive and reactive TSCS. PANNAL is a Multi-Agent based System, where each individual agent has ANN, CNN, and LQF-MWM to adapt signal sequences and durations and favor the crossing of emergency vehicles. Agents have a heterarchical architecture considered for coordination. We leant on V1SSIM, a state-of-the-art traffic simulation software for simulation and evaluation. We adopted algorithms, scenarios, key performance indicators, and evaluation results from the recent literature for benchmarking. These algorithms are pre-emptive and have a high performance and competitive results in traffic control of disturbed traffic condition.

Fog-based dynamic traffic light control system for improving public transport

Nowadays, traffic congestion is increasingly aggravating in almost every urban area and existing traffic light controllers cannot satisfy the rising demands efficiently to handle the traffic pressure. An intelligent transportation system (ITS) aims to provide major innovations for improving the performance of a public transport system including the traffic light control systems. In this paper, we propose a fog-based distributed architecture for a dynamic traffic light control system for ITS. In the proposed architecture, the local gateway collects real-time local traffic data using a wireless sensor network at each intersection. It also gets the neighboring traffic data from the distributed fog nodes. Using this local and global traffic data, the proposed Efficient Dynamic Traffic Light Control algorithm for Multiple (EDTLCM) intersections calculates the optimal green light sequence and duration (that maximizes the benefits), considering three objectives i) reducing the average waiting time ii) minimizing the fuel consumption and iii) maximizing the throughput. The simulation result shows that the proposed EDTLCM minimizes the waiting time, reduces fuel consumption and improves system throughput compared to the other dynamic strategies.

Performance comparison of population-based optimization algorithms for air traffic control

Purpose The purpose of this paper is to present a detailed performance comparison of recent and state-of-the-art population-based optimization algorithms for the air traffic control problem. Design/methodology/approach Landing sequence and corresponding landing times for the aircrafts were determined by using population-based optimization algorithms such as artificial bee colony, particle swarm, differential evolution, biogeography-based optimization, simulated annealing, firefly and teaching–learning-based optimization. To obtain a fair comparison, all simulations were repeated 30 times for each of the seven algorithms, two different problems and two different population sizes, and many different criteria were used. Findings Compared to conventional methods that depend on a single solution at the same time, population-based algorithms have simultaneously produced many alternate possible solutions that can be used recursively to achieve better results. Research limitations/implications In some cases, it may take slightly longer to obtain the optimum landing sequence and times compared to the methods that give a direct result; however, the processing times can be reduced using powerful computers or GPU computations. Practical implications The simulation results showed that using population-based optimization algorithms were useful to obtain optimal landing sequence and corresponding landing times. Thus, the proposed air traffic control method can also be used effectively in real airport applications. Social implications By using population-based algorithms, air traffic control can be performed more effectively. In this way, there will be more efficient planning of passengers’ travel schedules and efficient airport operations. Originality/value The study compares the performances of recent and state-of-the-art optimization algorithms in terms of effective air traffic control and provides a useful approach.

A simple crowdsourced delay-based traffic signal control.

Current transportation management systems rely on physical sensors that use traffic volume and queue-lengths. These physical sensors incur significant capital and maintenance costs. The ubiquity of mobile devices has made possible access to accurate and cheap traffic delay data. However, current traffic signal control algorithms do not accommodate the use of such data. In this paper, we propose a novel parsimonious model to utilize real-time crowdsourced delay data for traffic signal management. We demonstrate the versatility and effectiveness of the data and the proposed model on seven different intersections across three cities and two countries. This signal system provides an opportunity to leapfrog from physical sensors to low-cost, reliable crowdsourced data.

Augmenting Traffic Signal Control Systems for Urban Road Networks With Connected Vehicles

The increase in traffic volumes in urban areas makes network delay and capacity optimisation challenging. However, the introduction of connected vehicles in intelligent transport systems presents unique opportunities for improving traffic flow and reducing delays in urban areas. This paper proposes a novel traffic signal control algorithm called Multi-mode Adaptive Traffic Signals (MATS) which combines position information from connected vehicles with data obtained from existing inductive loops and signal timing plans in the network to perform decentralised traffic signal control at urban intersections. The MATS algorithm is capable of adapting to scenarios with low numbers of connected vehicles, an area where existing traffic signal control strategies for connected environments are limited. Additionally, a framework for testing connected traffic signal controllers based on a large urban road network in the city of Birmingham (UK) is presented. The MATS algorithm is compared with MOVA on a single intersection, and a calibrated TRANSYT plan on the proposed testing framework. The results show that the MATS algorithm offers reductions in mean delay up to 28% over MOVA, and reductions in mean delay and mean numbers of stops of up to 96% and 33% respectively over TRANSYT, for networks with 0-100% connected vehicle presence. The MATS algorithm is also shown to be robust under non-ideal communication channel conditions, and when heavy traffic demand prevails on the road network.