Proceedings ArticleDOI
Centralized Approach towards Intelligent Traffic Signal Control
Bhushan S. Atote,Mangesh Bedekar,Suja S. Panicker +2 more
- pp 63
TLDR
This paper presents the centralized approach in three phases, first phase gives the complete information of Central System (CS), second phase shows the working of Area Controller Systems (ACS) and Area Border Controller systems (ABCS), and phase three shows the Onsite Unit (OU).Abstract:
For better transportation system it is needed to make our traffic signal well instructed with the help of Vehicle-to-Infrastructure (V2I) communication for transferring the information about vehicles to the central system. This paper presents the centralized approach in three phases. First phase gives the complete information of Central System (CS), second phase shows the working of Area Controller Systems (ACS) and Area Border Controller Systems (ABCS) and phase three shows the Onsite Unit (OU).read more
Citations
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Journal ArticleDOI
Intelligent Traffic Signal Control Based on Reinforcement Learning with State Reduction for Smart Cities
TL;DR: The results of simulation experiments show that the proposed control algorithm effectively improves the capacity of isolated intersections while reducing the time and space costs of the signal control algorithm.
Book ChapterDOI
Performance Evaluation of Citywide Intersections Traffic Control Algorithm inVANETs-Based
Sarah Hasan,Mourad Elhadef +1 more
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.
References
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Recent Development and Applications of SUMO - Simulation of Urban MObility
TL;DR: The current state of the SUMO package, its major applications, both by research topic and by example, as well as future developments and extensions are described.
Proceedings ArticleDOI
Multi-agent reinforcement learning for traffic signal control
TL;DR: This paper forms the TSC problem as a discounted cost Markov decision process (MDP) and applies multi-agent reinforcement learning (MARL) algorithms to obtain dynamic TSC policies and shows that these algorithms perform significantly better than both the standard fixed signal timing (FST) algorithm and the saturation balancing (SAT) algorithm over two real road networks.
Journal ArticleDOI
Modeling Traffic Control Agency Decision Behavior for Multimodal Manual Signal Control Under Event Occurrences
TL;DR: A pressure-based human behavior model is proposed to mimic TCA's decision-making behavior and is validated by both offline segment-based phase and duration prediction and online VISSIM-based simulation.
BookDOI
The International Conference on Health Informatics
TL;DR: The International Conference on Health Informatics :, کتابخانه دیجیتال جندی شاپور اهواز
Proceedings ArticleDOI
Throughput optimality of extended back-pressure traffic signal control algorithm
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.