Optimization of signal behavior through dynamic traffic control: Proposed algorithm with traffic profiling
01 Dec 2016-pp 598-602
TL;DR: The method to make traffic signals intelligent with the dynamic signal values on the basis of different parameters shall drastically reduce time, fuel and also suggest alternate routes that are congestion free.
Abstract: The current scenario of traffic signals in India is fixed time, leading to more pollution, wastage of time and precious fuel. This paper proposes the method to make traffic signals intelligent with the dynamic signal values on the basis of different parameters. This method shall drastically reduce time, fuel and also suggest alternate routes that are congestion free. There are different adaptive strategies for traffic control but with their limitations of particular region and junctions, it is required to make it more precise for a specific network of junctions. For creating the real traffic scenario, consider that all vehicles are equipped with the Bluetooth devices. Our proposed algorithm shows the dynamic traffic control and traffic profiling for the given time.
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23 Apr 2018
TL;DR: This work will focus on the flow of traffic across number of adjacent traffic intersections in a city equipped with Road Side Units (RSU) and enhance the use of this information to improve future generations of inVANET-based protocols for controlling traffic intersections.
Abstract: The dawn of wireless communications makes the delivery of real time information at hands. This includes Vehicle to Infrastructure (V2I) and the Vehicle to Vehicle (V2V) communications which opened the doors for superior collection and use of information. In this paper, we try to enhance the use of this information to improve future generations of inVANET-based protocols for controlling traffic intersections. Previous work focused on the flow of the vehicles in one intersection. In this work, we will focus on the flow of traffic across number of adjacent traffic intersections in a city equipped with Road Side Units (RSU). The RSUs cooperation by exchanging information collected from vehicles through V2I and distribute among all RDUs using Infrastructure to Infrastructure (I2I). Advanced knowledge of the moving vehicles will lead to better traffic management at intersections and will reduce waiting time.
1 citations
18 Nov 2019
TL;DR: This paper tries to enhance traffic flow at intersections by simulating an improved VANET-based control algorithm and believes that the communication between RSU at a given intersection and nearby vehicles, RSU and other surrounding RSUs (RSU2RSU) will affect the flow of the vehicles positively.
Abstract: The massive improvement in wireless communications pertains a real time and accurate delivery of information, which makes it possible to remotely control and manage a wide number of applications and services. The ability to connect to mobile and fast-moving nodes can aid in providing or obtaining information from vehicles that in turn provides a diverse picking in the development of vehicular communications and control. This includes all type of vehicular communications such as Vehicle to Infrastructure (V2I) and the Vehicle-to-Vehicle (V2V) communications. In this paper, we try to enhance traffic flow at intersections by simulating an improved VANET-based control algorithm. The study focuses on the flow of traffic across multiple adjacent intersections in a city where each intersection is equipped with a Roadside Unit (RSU). We believe that the communication between RSU at a given intersection and nearby vehicles, RSU and other surrounding RSUs (RSU2RSU) will affect the flow of the vehicles positively. We also consider the need to minimize the required time to cross an intersection particularly if a vehicle is an emergency vehicle.
1 citations
References
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TL;DR: To this knowledge, this is the first paper that employs PNs to model and design a real-time traffic emergency system for intersections facing accidents, and it can be used to improve the state of the art in real- time traffic accident management and traffic safety at intersections.
Abstract: Petri nets (PNs) are well utilized as a visual and mathematical formalism to model discrete-event systems. This paper uses deterministic and stochastic PNs to design an emergency traffic-light control system for intersections providing emergency response to deal with accidents. According to blocked crossing sections, as depicted by dynamic PN models, the corresponding emergency traffic-light strategies are designed to ensure the safety of an intersection. The cooperation among traffic lights/facilities at those affected intersections and roads is illustrated. For the upstream neighboring intersections, a traffic-signal-based emergency control policy is designed to help prevent accident-induced large-scale congestion. Deadlock recovery, livelock prevention, and conflict resolution strategies are developed. We adopt a reachability analysis method to verify the constructed model. To our knowledge, this is the first paper that employs PNs to model and design a real-time traffic emergency system for intersections facing accidents. It can be used to improve the state of the art in real-time traffic accident management and traffic safety at intersections.
88 citations
TL;DR: In this article, a novel framework for designing and implementing a coordinated wide-area controller architecture for improved power system dynamic stability is presented and tested, which is based on a hybrid reinforcement learning and temporal difference framework.
Abstract: In this paper, a novel framework for designing and implementing a coordinated wide-area controller architecture for improved power system dynamic stability is presented and tested. The algorithm is an optimal wide-area system-centric controller and observer based on a hybrid reinforcement learning and temporal difference framework. It allows the system to deal with major concerns of wide-area monitoring problem: delays in signal transmission, the uncertainty of the communication network, and data traffic. The main advantage of this design is its ability to learn from the past using eligibility traces and predict the optimal trajectory of cost function through temporal difference method. The control algorithm is evolved from adaptive critic design (ACD) and performed online at a finite horizon through backward and forward view. The ACD controller's training and testing are implemented on the Innovative Integration Picolo card integrated to TMS320C28335 processor. Results on a real experimental test bed using a real power system feeder shows that this architecture provides better stability compared with conventional schemes.
26 citations
16 Jun 2015
TL;DR: It is proved that under certain conditions, the extended back-pressure algorithm still achieves maximum throughput, i.e, the expected long-term average of total queues is bounded from above under the extendedBack- pressure algorithm for largest possible set of arrival vectors.
Abstract: The back-pressure/max-pressure traffic signal control algorithm proposed in the existing literature is distributed, maximizes network throughput, and can be implemented without knowing traffic arrival rates. In this paper, we present an extended back-pressure traffic signal control algorithm, which can further handle bounded measurement/estimation noises in queue lengths and incorporate online estimation of turning ratios and saturated flow rates. Therefore, the extended back-pressure algorithm forms an important step towards the real application of distributed traffic signal control. We prove that under certain conditions, the extended back-pressure algorithm still achieves maximum throughput, i.e., the expected long-term average of total queues is bounded from above under the extended back-pressure algorithm for largest possible set of arrival vectors.
16 citations
20 Nov 2014
TL;DR: A Biologically-Inspired Neural Network for traffic signal control that consists of a competitive neural network, which balances feedforward and feedback inhibition to synchronize the activity of the neurons, and, thus, the semaphore activation.
Abstract: Urban mobility is a central concern of large cities around the world. The growing urbanization indicates the situation can be even worse. A traffic demand higher than the urban capacity generates traffic congestions, which can be reduced through an efficient traffic signal control method. This paper presents a Biologically-Inspired Neural Network for traffic signal control. Instead of focusing on the macroscopic optimization of urban traffic, like other works, the hereby proposed control method investigates a single intersection between streets. This way, it is possible to incorporate more knowledge about the system dynamics into the control model and analyze its effects on control efficiency. The proposed method consists of a competitive neural network, which balances feedforward and feedback inhibition to synchronize the activity of the neurons, and, thus, the semaphore activation. Moreover, other proprieties of biological neurons are adopted: intrinsic plasticity, to impose system constraints; and synaptic plasticity, to prioritize traffic flows. The flexibility of the neurons and its synaptic connections regarding parameter definition constitute the capacity of easily incorporating knowledge about the system dynamics into the control model. Results of comparative simulations validate the proposed method and illustrate its efficiency and consistency.
12 citations
20 Nov 2014
TL;DR: Simulation results show that Iterative Tuning strategy for urban traffic signal control can improve the performance considerably comparing with fixed-time strategy.
Abstract: This paper introduces Iterative Tuning (IT) strat- egy for urban traffic signal control. This strategy is motivated by people's daily repetitive travel patterns between homes and working places. Statistical analysis of a real traffic network shows that traffic flows of junctions are repetitive with small variations on a weekly basis. The main idea of IT is that, daily traffic signal schedules are tuned with anticipation of traffic demands. In this paper, only phase split is tuned iteratively to balance the traffic demands from all directions in a junction. Each junction has its own controller and these controllers can work cooperatively to improve the network performance after several iterations. Therefore IT strategy is scalable for arbitrary large urban networks. Marina Bay and Clementi areas in Singapore based on real traffic data are simulated and simulation results show that IT strategy can improve the performance considerably comparing with fixed-time strategy. I. INTRODUCTION Traffic signal control strategies have gone through various development from fixed-time to adaptive strategies, from single junction to coordinated multi-junctions. The fixed-time algorithm was developed by Webster (1) to optimize splits and cycle length with delay estimated model. MAXBAND, developed by Little (2), considers the synchronization of traf- fic signals so that a car, starting at a main artery and traveling with free speed, can go through several junctions without stop for a red light. TRANSYT (TRAffic Network StudY Tool), developed by Robertson (3), is the most well-known and frequently applied fixed-time traffic signal strategy. It is the benchmark to test the performance improved by adaptive traffic-responsive strategies. SCATS and SCOOT are the most popular adaptive co- ordinated traffic strategies. SCATS (Sydney Coordinated Adaptive Traffic System) (4), is a model-free distributed strategies with predefined signal plans. SCOOT (Split Cycle Offset Optimisation Technique) (5), is almost like adaptive TRANSYT strategy with three kinds of optimizer: Split Optimizer, Offset Optimizer and Cycle length Optimizer. OPAC (Optimized Policies for Adaptive Control) (6) strategy is a real-time distributed signal optimization algorithm with three control layers to optimize cycle, split, offset and phase sequences. RHODES (Real-time Hierarchical Optimizing Distributed Effective System) is an adaptive traffic control
9 citations