Performance Analysis of Decentralized VS Centralized Control for the Traffic Signal Synchronization Problem

Adacher, Ludovica; Tiriolo, Marco

doi:10.1155/2020/8873962

Cited by 11 publications

(9 citation statements)

References 39 publications

(73 reference statements)

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“…The use of traffic signal synchronization is used to control traffic signals in clusters wherein all signals in one cluster follow the same phase pattern. This is a popular approach in the literature, as traffic signal synchronization may be used to improve the flow of traffic along heavily traveled routes [42]. For networks composed of more than 4 intersections, a signal synchronization scheme of k clusters is fixed, as shown in Figure 6 for a 12-node network.…”

Section: G Model Assumptionsmentioning

confidence: 99%

Deep Reinforcement Learning for the Co-Optimization of Vehicular Flow Direction Design and Signal Control Policy for a Road Network

et al. 2023

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Reinforcement Learning (RL) is a popular approach for deciding on an optimum traffic signal control policy to alleviate congestion in a road network. However, the traffic signal control policy can also be optimized in conjunction with the design of vehicular flow directions to further improve traffic performance. The design of vehicular flow directions refers to the right of way or directional restriction imposed in a road network. Here, a new RL-based technique is presented for co-optimization of the design of vehicular flow directions and control policy for traffic signals. This technique consists of a two-step iterative process, wherein a set of vehicular flow directions for a road network is generated, then a RL-based approach is used to train the traffic signal control policy over the given set of vehicular flow directions. Following the proposed technique, the vehicular flow directions with poor traffic performance are iteratively eliminated, while new vehicular flow directions are generated to achieve better traffic performance and realize convergence to a maximum possible expected traffic performance. The proposed RL-based technique is evaluated by using two examples under rush hour and non-rush hour traffic conditions. It is found that, compared to a RL-based approach in which only traffic signal control policy is considered, the proposed approach can be used to obtain a better traffic performance in terms of vehicular queue length and throughput.INDEX TERMS Co-optimization, reinforcement learning, vehicular flow direction design, traffic signal control, deep neural networks.

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Section: G Model Assumptionsmentioning

confidence: 99%

Deep Reinforcement Learning for the Co-Optimization of Vehicular Flow Direction Design and Signal Control Policy for a Road Network

et al. 2023

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“…Network decomposition is highlighted in recent DSC research [2] as an important factor for optimizing DSC system. In this work, a grid network is decomposed into grid subnetworks.…”

Section: Network Decompositionmentioning

confidence: 99%

“…us, computational time (CT) is the point of comparison for the performances of the centralized signal control (CSC) and decentralized signal control (DSC) systems. In addition, studies have shown network decomposition to have a significant impact on system performance [2,3]. erefore, we investigate the performance of CSC and DSC systems under different decomposition setups, using test scenarios constructed with varying demand profiles and grid networks.…”

Section: Introductionmentioning

confidence: 99%

“…Adacher and Tiriolo [33] addressed this by working with subnetworks containing more than one intersection. eir recent research using a clustering algorithm (CA) to solve the DSC problem based on a Cell Transmission (CTM) model highlighted the importance of spatial decompositions [2]. However, they tested one network that includes a specific group of subnetworks with a simplified signal phase, thereby offering limited results.…”

Section: Introductionmentioning

confidence: 99%

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Centralized and Decentralized Signal Control with Short-Term Origin-Destination Demand for Network Traffic

Zhang

Qiu

Kim

2022

Journal of Advanced Transportation

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We develop and assess centralized and decentralized signal control systems with short-term origin-destination (OD) demands as inputs. Considering each intersection turning movement as a virtual link, we assign traffic demand to paths based on minimal instantaneous travel time. Then, the optimal control is formulated using a G/G/n/FIFO open queueing network model (QNM). We also solve the issue of optimal control using a three-step naïve method for the centralized system with the new inputs. Because the optimization of large-scale network traffic signals can involve sizeable numbers of decision variables and nonlinear constraints, making it a nondeterministic polynomial time (NP) complete problem, we further decompose the centralized system into a decentralized system where the network is divided into subnetworks. Each subnetwork has a dedicated agent that optimizes signals within it. Furthermore, traffic demand for the entire network is decomposed into demands for subnetworks via path decomposition index (PDI). The proposed control systems are applied to test scenarios constructed using different demand profiles in grid networks. We also investigate the impact of network decomposition strategy on signal control system performance. Results show that network decomposition with smaller subnetworks results in less computational time (CT) but increased average travel time (ATT) and total travel delay (TTD).

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“…In this regard, traffic monitoring is one of the most valuable functions of traffic management systems (TMSs). Particularly in advanced TMSs (ATMSs), real-time collection of precise information through traffic monitoring plays a crucial role for traffic managers when they develop various control strategies [1][2][3]. Furthermore, the detailed numerical status of realtime traffic such as lane-by-lane travel volume and queue length can be used as supplementary information for cooperative intelligent transportation system (C-ITS) operations based on autonomous vehicles [4,5].…”

Section: Introductionmentioning

confidence: 99%

Development of AI-Based Vehicle Detection and Tracking System for C-ITS Application

Tak

Lee

Song³

et al. 2021

Journal of Advanced Transportation

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There are various means of monitoring traffic situations on roads. Due to the rise of artificial intelligence (AI) based image processing technology, there is a growing interest in developing traffic monitoring systems using camera vision data. This study provides a method for deriving traffic information using a camera installed at an intersection to improve the monitoring system for roads. The method uses a deep-learning-based approach (YOLOv4) for image processing for vehicle detection and vehicle type classification. Lane-by-lane vehicle trajectories are estimated by matching the detected vehicle locations with the high-definition map (HD map). Based on the estimated vehicle trajectories, the traffic volumes of each lane-by-lane traveling direction and queue lengths of each lane are estimated. The performance of the proposed method was tested with thousands of samples according to five different evaluation criteria: vehicle detection rate, vehicle type classification, trajectory prediction, traffic volume estimation, and queue length estimation. The results show a 99% vehicle detection performance with less than 20% errors in classifying vehicle types and estimating the lane-by-lane travel volume, which is reasonable. Hence, the method proposed in this study shows the feasibility of collecting detailed traffic information using a camera installed at an intersection. The approach of combining AI and HD map techniques is the main contribution of this study, which shows a high chance of improving current traffic monitoring systems.

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Performance Analysis of Decentralized VS Centralized Control for the Traffic Signal Synchronization Problem

Cited by 11 publications

References 39 publications

Deep Reinforcement Learning for the Co-Optimization of Vehicular Flow Direction Design and Signal Control Policy for a Road Network

Deep Reinforcement Learning for the Co-Optimization of Vehicular Flow Direction Design and Signal Control Policy for a Road Network

Centralized and Decentralized Signal Control with Short-Term Origin-Destination Demand for Network Traffic

Development of AI-Based Vehicle Detection and Tracking System for C-ITS Application

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