Traffic Control Algorithm Research Articles

ICAFE: Intelligent Congestion Avoidance and Fast Emergency services

Abstract Content Centric Network (CCN) has been envisioned as a paradigm shift from client server architecture. In smart cities, transportation plays an important role where integrated services facilitate citizens through the ease of use, safety, and convenience. In this work, we propose an integrated CCN for intelligent Congestion Avoidance and Fast Emergency (iCAFE) services delivery at road side accidents. One of the significant contributions is a novel content-centric VANET-based protocol called iCAFE, an efficient traffic control algorithm and five unique packet headers for effective communications. In case of accident, emergency packets are broadcast to RSU wherein the forwarding information based (FIB) and pending interest table (PIT) are updated accordingly. The RSU broadcasts interest packets to hospital and sends a rescue message to the ambulance. The RSU also informs nearest RSUs and vehicles to evacuate the affected lane. After the rescue process is completed, the data packet is unicast from the hospital to the RSU and the PIT and FIB are updated. iCAFE achieves a high packet delivery ratio (PDR) with minimum rescue delay (R-Delay), high throughput, minimum network load, smaller collision probability, and minimum packet drop fraction. The iCAFE results are compared with the traffic accidents reduction strategy (TARS).

A Neuroevolutionary Approach to Controlling Traffic Signals Based on Data from Sensor Network.

The paper introduces an artificial neural network ensemble for decentralized control of traffic signals based on data from sensor network. According to the decentralized approach, traffic signals at each intersection are controlled independently using real-time data obtained from sensor nodes installed along traffic lanes. In the proposed ensemble, a neural network, which reflects design of signalized intersection, is combined with fully connected neural networks to enable evaluation of signal group priorities. Based on the evaluated priorities, control decisions are taken about switching traffic signals. A neuroevolution strategy is used to optimize configuration of the introduced neural network ensemble. The proposed solution was compared against state-of-the-art decentralized traffic control algorithms during extensive simulation experiments. The experiments confirmed that the proposed solution provides better results in terms of reduced vehicle delay, shorter travel time, and increased average velocity of vehicles.

A simulation system and speed guidance algorithms for intersection traffic control using connected vehicle technology

In the connected vehicle environment, real-time vehicle-state data can be obtained through vehicle-to-infrastructure communication, and the prediction accuracy of urban traffic conditions can significantly increase. This study uses the C++/Qt programming language and framework to build a simulation platform. A two-way six-lane intersection is set up on the simulation platform. In addition, two speed guidance algorithms based on optimizing the travel time of a single vehicle or multiple vehicles are proposed. The goal of optimization is to minimize the travel time, with common indicators such as average delay of vehicles, average number of stops, and average stop time chosen as indexes of traffic efficiency. When the traffic flow is not saturated, compared with the case of no speed guidance, single-vehicle speed guidance can improve the traffic efficiency by 20%, whereas multi-vehicle speed guidance can improve the traffic efficiency by 50%. When the traffic flow is saturated, the speed guidance algorithms show outstanding performance. The effect of speed guidance gradually enhances with increasing penetration rate, and the most obvious gains are obtained when the penetration rate increases from 10% to 40%. Thus, this study has shown that speed guidance in the connected vehicle environment can significantly improve the traffic efficiency of intersections, and the multi-vehicle speed guidance strategy is more effective than the single-vehicle speed guidance strategy.

Distributed Cooperative Backpressure-Based Traffic Light Control Method

On the foundation of the original backpressure-based traffic light control algorithm, a distributed cooperative backpressure-based traffic light control method is proposed in this paper. The urban traffic network is modeled as a smart agent-controlled queuing network, in which the intersection agents exchange the queue length information and the selected activating light phase information of neighboring intersections through communications and determine the activating light phase at each time slot according to local traffic information. The improved phase pressure computation method considers the phase state of downstream intersections instead of only the queue length of the local intersections. Light phase switching coordination among adjacent intersections is achieved using the consensus-based bundle algorithm, in which the cooperative light phase switching problem is viewed as a task assignment issue among adjacent intersections. Simulation results illustrated that the proposed cooperative backpressure-based traffic light control method obtained better performance than the original backpressure-based and fixed-time traffic control methods.

An improved artificial fish swarm algorithm for traffic signal control

An improved artificial fish swarm algorithm for traffic signal control

An improved artificial fish swarm algorithm for traffic signal control

The excessive growth of car ownership has caused great pressure on urban traffic. The traffic congestion is the most acute problem. One of the main causes of traffic congestion is the unreasonable scheme of traffic signal timing at road intersections. In view of the limitation of Webster algorithm, we combine the artificial fish swarm algorithm, chaos search and feedback strategy based on the optimisation theory of the signal timing problem to solve this problem. Furthermore, we apply the algorithm to the field of the traffic signal control. We set the average of vehicle delays and parking numbers as the target and improve the target road intersection timing scheme by using the optimisation algorithm. This method enhances the capacity of the target intersection effectively. Taking the condition of the target road intersection and the basic data into consideration, we construct the simulation model of the road intersection through the VISSIM simulation modelling tool. Then we import the relevant data and obtain a new timing plan which sets a new cycle and the green light duration of each phase. Compared to the original method, the algorithm based on the artificial fish-swarm is feasible and effective.

Dynamic Local Vehicular Flow Optimization Using Real-Time Traffic Conditions at Multiple Road Intersections

Dynamic management of vehicular traffic congestion to maximize throughput in urban areas has been drawing increased attention in recent years. For that purpose, a number of adaptive control algorithms have been proposed for individual traffic lights based on the in-flow rate. However, little attention has been given to the traffic throughput maximization problem considering real-time road conditions from multiple intersections. In this paper, we formulate such a problem as maximum integer multi-commodity flow by considering incoming vehicles that have different outgoing directions. Then, we propose a novel adaptive traffic light signal control algorithm which opts to maximize traffic flow through and reduce the waiting time of vehicles at an intersection. The proposed algorithm adjusts traffic light signal phases and durations depending on real-time road condition of local and neighboring intersections. Via SUMO simulation, we demonstrate the effectiveness of the proposed algorithm in terms of traffic throughput and average travel time.

Autonomous and connected intersection crossing traffic management using discrete-time occupancies trajectory

In this paper, we address the problem of safe and efficient intersection crossing traffic management of autonomous and connected ground traffic. Toward this objective, we propose an algorithm called the discrete-time occupancies trajectory based intersection traffic coordination algorithm (DICA). We show that the basic DICA has a computational complexity of $\mathcal {O}(n^{2} {L_{m}^{3}})$ where n is the number of vehicles granted to cross an intersection and Lm is the maximum length of intersection crossing routes. To improve the overall computational efficiency of the algorithm, the basic DICA is enhanced by several computational approaches that are proposed in this paper. The enhanced algorithm has the computational complexity of $\mathcal {O}(n^{2} L_{m} \log _{2} L_{m})$ . The improved computational efficiency of the enhanced algorithm is validated through simulations using an open source traffic simulator called the simulation of urban mobility (SUMO). The overall throughput, as well as the computational efficiency of the enhanced algorithm, are also compared with those of an optimized traffic light control algorithm.

A Control-Theoretic Approach to In-Network Congestion Management

WANs are often over-provisioned to accommodate worst-case operating conditions, with many links typically running at only around 30% capacity. In this paper, we show that in-network congestion management can play an important role in increasing network utilization. To mitigate the effects of in-network congestion caused by rapid variations in traffic demand, we propose using high-frequency traffic control (HFTraC) algorithms that exchange real-time flow rate and buffer occupancy information between routers to dynamically coordinate their link-service rates. We show that the design of such dynamic link-service rate policies can be cast as a distributed optimal control problem that allows us to systematically explore an enlarged design space of in-network congestion management algorithms. This also provides a means of quantitatively comparing different controller architectures: we show, perhaps surprisingly, that centralized control is not always better. We implement and evaluate HFTraC in the face of rapidly varying UDP and TCP flows and in combination with AQM algorithms. Using a custom experimental testbed, a Mininet emulator, and a production WAN, we show that HFTraC leads to up to 66% decreases in packet loss rates at high link utilizations as compared to FIFO policies.

On Intelligent Traffic Control for Large-Scale Heterogeneous Networks: A Value Matrix-Based Deep Learning Approach

Recently, deep learning has emerged as an attractive technique to intelligently control network traffic. However, the contemporary researches only focused on small-/medium-scale networks, since the computational complexity of deep learning based traffic control algorithm significantly increases with the network size. In this paper, we address this issue and envision a reward-based deep learning structure, which jointly employs deep convolutional neural network (CNN) and a deep belief network (DBN) to predict the traffic load value matrix and construct the final action matrix, respectively. In our proposal, the deep CNN is used to construct the award prediction network, while the deep DBN constructs the action decision network. Thus, the final action space is simplified to a next destination action matrix, and the computational complexity is substantially reduced. Computer-based simulation results demonstrate that our proposal is able to achieve an improved performance in the large-scale network in terms of the packets loss rate and throughput in contrast with those in the conventional routing method.

Label-less Learning for Traffic Control in an Edge Network

With the development of intelligent applications (e.g., self-driving, real-time emotion recognition), there are higher requirements for cloud intelligence. However, cloud intelligence depends on the multi-modal data collected by user equipments (UEs). Due to the limited capacity of network bandwidth, offloading all data generated from the UEs to the remote cloud is impractical. Thus, in this article, we consider the challenging issue of achieving a certain level of cloud intelligence while reducing network traffic. In order to solve this problem, we design a traffic control algorithm based on label-less learning on the edge cloud, which is dubbed LLTC. By use of the limited computing and storage resources at the edge cloud, LLTC evaluates the value of data that will be offloaded. Specifically, we first give a statement of the problem and the system architecture. Then we design the LLTC algorithm in detail. Finally, we set up the system testbed. Experimental results show that the proposed LLTC can guarantee the required cloud intelligence while minimizing the amount of data transmission.

Introducing a Re-Sampling Methodology for the Estimation of Empirical Macroscopic Fundamental Diagrams

The uncertainty in the estimation of the macroscopic fundamental diagram (MFD) under real-world traffic conditions and urban dynamics might result in an inaccurate estimation of the MFD parameters—especially if congestion is rarely observed network-wide. For example, as data normally come from punctual observations out of the whole network, it is unclear how representative these observations might be (i.e., how much is the observed capacity affected by the network’s inhomogeneity). Similarly, if the observed data do not exhibit a distinct congested branch, it is hard to determine the network capacity and critical density. This, in turn, also leads to uncertainties and errors in the parametrization of the MFD for applications, for example traffic control. This paper introduces a novel methodology to estimate (i) the level of inhomogeneity in the network, and (ii) the critical density of the MFD, even when no congested branch is observed. The methodology is based on the idea of re-sampling the empirical data set. Using an extensive data set from Lucerne, Switzerland, and London, UK, insights are provided on the performance and the application of the proposed methodology. The proposed methodology is used to illustrate how the level of inhomogeneity is lower in Lucerne than in the three areas of the network of London that are investigated. The proposed measure of the level of inhomogeneity gives city planners the possibility to analyze and investigate how efficiently their road network is utilized. In addition, the analysis shows that, for the network of Lucerne, the proposed methodology allows accurate estimation of the critical density up to 16 times more often than would be possible otherwise. This simple and robust estimation of the critical density is crucial for the application of many traffic control algorithms.

Congestion control in wireless sensor and 6LoWPAN networks: toward the Internet of Things

The Internet of Things (IoT) is the next big challenge for the research community where the IPv6 over low power wireless personal area network (6LoWPAN) protocol stack is a key part of the IoT. Recently, the IETF ROLL and 6LoWPAN working groups have developed new IP based protocols for 6LoWPAN networks to alleviate the challenges of connecting low memory, limited processing capability, and constrained power supply sensor nodes to the Internet. In 6LoWPAN networks, heavy network traffic causes congestion which significantly degrades network performance and impacts on quality of service aspects such as throughput, latency, energy consumption, reliability, and packet delivery. In this paper, we overview the protocol stack of 6LoWPAN networks and summarize a set of its protocols and standards. Also, we review and compare a number of popular congestion control mechanisms in wireless sensor networks (WSNs) and classify them into traffic control, resource control, and hybrid algorithms based on the congestion control strategy used. We present a comparative review of all existing congestion control approaches in 6LoWPAN networks. This paper highlights and discusses the differences between congestion control mechanisms for WSNs and 6LoWPAN networks as well as explaining the suitability and validity of WSN congestion control schemes for 6LoWPAN networks. Finally, this paper gives some potential directions for designing a novel congestion control protocol, which supports the IoT application requirements, in future work.

Investigation of stochastic variation of parameters for a macroscopic traffic model

ABSTRACTThis paper proposes a novel approach to include stochastic variation in the framework of a macroscopic traffic model. Simultaneously, the effects of insufficient data quality and consequent inaccurate identification are investigated. The traffic density distribution over time and space provides a deeper understanding of the evolution of jams, which in turn is vital for the development of traffic network control algorithms. Model parameters are estimated using both Eulerian and Lagrangian sensing. Instead of using deterministic model parameters directly, this work proposes to use stochastic model parameters. Therefore, in addition to the estimated mean of model parameters, a corresponding covariance matrix is estimated as well. The utilized traffic model supports arbitrary Fundamental Diagrams defined by (a combination of) model parameters. Using an analytic approach to define the resulting variation in the Fundamental Diagram is generally impossible, due to the nonlinear combination of random variables in the parameterization of the Fundamental Diagram. A general approach to deal with complex combinations of random variables in the definition of Fundamental Diagrams and, consequently, in the traffic model, is proposed. The core idea is to utilize Monte Carlo Simulation to create a discretized lookup table for the density–speed relationship. Thereby computational effort is significantly reduced, while preserving the ability of the traffic model to reflect variation in the driving behavior. The proposed approach is validated via simulation with state-of-the-art software and Monte Carlo Simulations.

Delay-Based Traffic Signal Control for Throughput Optimality and Fairness at an Isolated Intersection

With the attractive feature of guaranteeing maximum network throughput, backpressure routing has been widely used in wireless communication networks. Motivated by the backpressure routing, in this paper, we propose a delay-based traffic signal control algorithm in transportation networks. We prove that this delay-based control achieves optimal throughput performance, same as the queue-based traffic signal control in the literature. However, a vehicle at a lane whose queue length remains very small may be excessively delayed under the queue-based signal control. Our delay-based backpressure control can deal with the excessive delays and achieve better fairness with respect to delay while still guaranteeing throughput optimality. Moreover, a general weighted control scheme combining the queue-based and delay-based schemes is also investigated, to provide a more flexible control according to the quality of service requirements. Numerical results explore their performance under both homogeneous and heterogeneous traffic scenarios.

Traffic signal fuzzy control algorithm for urban intersection based on vehicle-road cooperative environment

Traditionally, when using simple fuzzy control algorithm to control traffic signals at urban intersections, phase sequence optimization control is not carried out in view of vehicle-road coordination environment, and traffic lights cannot be reasonably arranged, resulting in a long queue of vehicles at intersections, resulting in traffic congestion. Therefore, a fuzzy control algorithm of urban intersection traffic signal is proposed under the vehicle-road cooperative environment. Based on the traffic signal model under the vehicle-road cooperative environment, the fuzzy control algorithm based on phase sequence optimization is obtained by combining the fuzzy control algorithm with the phase sequence optimization algorithm, and the fuzzy control algorithm steps are controlled reasonably according to the phase sequence optimization. The experimental results show that the average waiting time of vehicles controlled by the proposed algorithm is only 21s and 23s in the afternoon rush hours, which is an effective traffic signal control algorithm for urban intersections.

Implementing an adaptive traffic signal control algorithm in an agent-based transport simulation

This paper describes the implementation of the fully traffic adaptive signal control algorithm by Lämmer2 in the agent-based transport simulation MATSim. The implementation is tested at an illustrative, single intersection scenario and compared to the results of Lammers MATLAB simulation. Plausibility of the self-controlled signals and overall results can be confirmed. Small deviations can be explained by differences in flow simulation and resolution of simulation time steps. In the simulation of the illustrative intersection, the adaptive control is proved to be stable and, overall, superior to a fixed-time control. Constant vehicle arrivals are simulated to show the performance of the control and its underlying sub-strategies. The expected behavior of the algorithm and its implementation are validated by analyzing queue lengths over time. The adaptive control significantly outperforms the fixed-time control for stochastic demand, where its ability to dynamically react to changes in flow becomes important.

High performance adaptive traffic control for efficient response in vehicular ad hoc networks

Nowadays, with the invention of CUDA, a parallel computing platform and programming model, there is a dramatic increase in computing performance by harnessing the power of the GPU. GPU computing with CUDA can be utilised to find efficient solutions for many real world complex problems. One such problem is traffic signal control problem, which takes care of conflicting movements at the intersections to avoid accidents and ensure smooth flow of traffic for the commuters in safe and efficient manner. Adaptive traffic control (ATC) algorithm is used in literature to reduce the average queue length at the intersections. This algorithm has serial implementation on a single CPU and hence takes large computation time. In this paper, we propose a high performance adaptive traffic control for proving efficient responses and hence reducing average queue length that results in decrease in the overall waiting time at the intersections. We tested our proposed approach with varying number of vehicles for two real world networks. The performance of the proposed algorithm is compared with its serial counterpart.

Effectiveness Proving and Control of Platoon-Based Vehicular Cyber-Physical Systems

With the application of cyber-physical systems, many cargo fleets take the initiative to form a platoon during transportation. Compared with the conventional signal timing algorithms, the platoon-based algorithm can greatly reduce the traveling time. However, the mathematical proof of the platoon-based algorithm performance has not been given for vehicular cyber-physical systems. In this paper, we proposed a timed Petri nets model to delineate the behavior of platoons at an isolated intersection. According to the results of a model structure analysis, we proved that to minimize the total queue length at any time, the platoon must be kept as large as possible. Based on the results, an efficient platoon-based traffic control algorithm is presented for the vehicular cyber-physical systems at intersections. The simulation results show that the proposed algorithm performs better than many conventional signal timing strategies.

Calculation of vehicle delay at signal-controlled intersections with adaptive traffic control algorithm

Calculation of vehicle delay at signal-controlled intersections with adaptive traffic control algorithm