Deep Q-network-based traffic signal control models

Park, Sang Min; Han, Eum; Park, Sungho; Jeong, Harim; Yun, Ilsoo

doi:10.1371/journal.pone.0256405

Cited by 11 publications

(5 citation statements)

References 14 publications

(12 reference statements)

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“…To enhance the comprehensiveness of our evaluation and establish a comprehensive benchmark, we extended our analysis by incorporating a comparative study with a relatively advanced Deep Q-network (DQN)-based signal scheduling system [46]. In this comparative study, we considered the same sets of experiments conducted for the evaluation of our proposed system.…”

Section: Experiments and Comparisonsmentioning

confidence: 99%

A dynamic traffic signal scheduling system based on improved greedy algorithm

Sun,

Qi,

Liu

et al. 2024

PLoS ONE

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Urbanization has led to accelerated traffic congestion, posing a significant obstacle to urban development. Traditional traffic signal scheduling methods are often inefficient and cumbersome, resulting in unnecessary waiting times for vehicles and pedestrians, exacerbating the traffic situation. To address this issue, this article proposes a dynamic traffic signal scheduling system based on an improved greedy algorithm. Unlike conventional approaches, we introduce a reward function and a cost model to ensure fair scheduling plans. A constraint function is also established, and the traffic signal scheduling is iterated through the feasible matrix using the greedy algorithm to simplify the decision-making process and enhance solution efficiency. Moreover, an emergency module is integrated to prioritize special emergency vehicles, reducing their response time during emergencies. To validate the effectiveness of our dynamic traffic signal scheduling system, we conducted simulation experiments using the Simulation of Urban Mobility (SUMO) traffic simulation suite and the SUMO traffic control interface Traci. The results indicate that our system significantly improves intersection throughput and adapts well to various traffic conditions, effectively resolving urban traffic congestion while ensuring fair scheduling plans.

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Section: Experiments and Comparisonsmentioning

confidence: 99%

A dynamic traffic signal scheduling system based on improved greedy algorithm

Sun,

Qi,

Liu

et al. 2024

PLoS ONE

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“…Normal techniques failed to give a good result using image processing. So, in this search gaussian mixture algorithm were used to solve the problem and gives a high accuracy to isolate and count the cars from the camera frames [12], [13].…”

Section: Comparative Analysismentioning

confidence: 99%

A Controlling Traffic Light system using Fuzzy logic

Gdeeb

2023

JQCM

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Automatic control systems are a new advanced system uses computers and hardware controllers to act as a driving system. Increasing in cars numbers while we have the same infrastructure caused a large traffic problem so we need automatic adaptive controllers to control the open and close tasks of traffic light system. Fuzzy Logic is a conversation from classical logic with values of 0 for false and 1 for true to a range of values between 0 and 1. This study aims to create an adaptive traffic control system using fuzzy logic and image processing, we have a camera on each traffic light to calculate the number of cars there and then we feed the number of cars to a fuzzy logic system to decide which traffic light must become on

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“…Artificial intelligence has been widely applied to develop adaptive controllers. In the literature, a considerable variety of solutions mainly based on reinforcement learning (RL) [13][14][15]59,60], neural networks (NN) [16][17][18], deep reinforcement learning (DRL) [19][20][21][22], and fuzzy logic (FL) can be found. However, as has been surveyed by Araghi et al [35], RL has the worst performance in terms of accuracy, speed, and capacity to manage a huge amount of data compared to the other approaches.…”

Section: Adaptive Controllersmentioning

confidence: 99%

“…Artificial intelligence methods have gained popularity to design adaptive controllers capable of addressing unpredictable traffic conditions. Numerous approaches have been proposed based on reinforcement learning (RL) [13][14][15], neural networks (NN) [16][17][18], deep reinforcement learning (DRL) [19][20][21][22], and fuzzy logic (FL) [12,[23][24][25][26][27][28][29][30][31][32][33][34]. Due to their accuracy, computational requirements, as well as the supported amounts of states and actions, NN, DRL, and FL methods have demonstrated the best performances [35][36][37].…”

Section: Introductionmentioning

confidence: 99%

Efficient Intersection Management Based on an Adaptive Fuzzy-Logic Traffic Signal

et al. 2022

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Traffic signals may generate bottlenecks due to an unfair timing balance. Facing this problem, adaptive traffic signal controllers have been proposed to compute the phase durations according to conditions monitored from on-road sensors. However, high hardware requirements, as well as complex setups, make the majority of these approaches infeasible for most cities. This paper proposes an adaptive traffic signal fuzzy-logic controller which uses the flow rate, retrieved from simple traffic counters, as a unique input requirement. The controller dynamically computes the cycle duration according to the arrival flow rates, executing a fuzzy inference system guided by the reasoning: the higher the traffic flow, the longer the cycle length. The computed cycle is split into different phases proportionally to the arrival flow rates according to Webster’s method for signalization. Consequently, the controller only requires determining minimum/maximum flow rates and cycle lengths to establish if–then mappings, allowing the reduction of technical requirements and computational overhead. The controller was tested through a microsimulation model of a real isolated intersection, which was calibrated with data collected from a six-month traffic study. Results revealed that the proposed controller with fewer input requirements and lower computational costs has a competitive performance compared to the best and most used approaches, being a feasible solution for many cities.

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Deep Q-network-based traffic signal control models

Cited by 11 publications

References 14 publications

A dynamic traffic signal scheduling system based on improved greedy algorithm

A dynamic traffic signal scheduling system based on improved greedy algorithm

A Controlling Traffic Light system using Fuzzy logic

Efficient Intersection Management Based on an Adaptive Fuzzy-Logic Traffic Signal

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