Improving the Performance of Single-Intersection Urban Traffic Networks Based on a Model Predictive Controller

Jafari, Sadiqa; Shahbazi, Zeinab; Byun, Yung-Cheol

doi:10.3390/su13105630

Cited by 15 publications

(13 citation statements)

References 45 publications

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“…Still, the dynamic model is not stable on a single intersection. Here, [53] has been considered at only one intersection for reducing queue length of the vehicle. Algorithms derived from RL techniques operate based on biological principles.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…The optimizer calculates this by considering the cost function and constraints. Specific models are easy to understand and implement; more information is explained in [53].…”

Section: Multi-agent and Model Predictive Controlmentioning

confidence: 99%

“…In this section, mathematical equations and several connecting intersections have been mentioned. The stability of an intersection has been mentioned in [53]. For more explanation, there are two phases at an intersection, Leg 1, Leg 3, and Leg 2, Leg 4.…”

Section: Mathematical Models In Intersectionsmentioning

confidence: 99%

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Designing the Controller-Based Urban Traffic Evaluation and Prediction Using Model Predictive Approach

2022

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As society grows, the urbanized population proliferates, and urbanization accelerates. Increasing traffic problems affect the normal process of the city. The urban transportation system is vital to the effective functioning of any city. Science and technology are critical elements in improving traffic performance in urban areas. In this paper, a novel control strategy based on selecting the type of traffic light and the duration of the green phase to achieve an optimal balance at intersections is proposed. This balance should be adaptable to fixed behavior of time and randomness in a traffic situation; the goal of the proposed method is to reduce traffic volume in transportation, the average delay for each vehicle, and control the crashing of cars. Due to the distribution of urban traffic and the urban transportation network among intelligent methods for traffic control, the multi-factor system has been designed as a suitable, intelligent, emerging, and successful model. Intersection traffic control is checked through proper traffic light timing modeled on multi-factor systems. Its ability to solve complex real-world problems has made multiagent systems a field of distributed artificial intelligence that is rapidly gaining popularity. The proposed method was investigated explicitly at the intersection through an appropriate traffic light timing by sampling a multiagent system. It consists of many intersections, and each of them is considered an independent agent that shares information with each other. The stability of each agent is proved separately. One of the salient features of the proposed method for traffic light scheduling is that there is no limit to the number of intersections and the distance between intersections. In this paper, we proposed method model predictive control for each intersection’s stability; the simulation results show that the predictive model controller in this multi-factor model predictive system is more valuable than scheduling in the fixed-time method. It reduces the length of vehicle queues.

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Section: Simulation Resultsmentioning

confidence: 99%

“…The optimizer calculates this by considering the cost function and constraints. Specific models are easy to understand and implement; more information is explained in [53].…”

Section: Multi-agent and Model Predictive Controlmentioning

confidence: 99%

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Designing the Controller-Based Urban Traffic Evaluation and Prediction Using Model Predictive Approach

2022

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“…This section presents a detailed explanation of the existing studies in smart home security based on IoT [25][26][27][28][29][30][31], blockchain, and machine learning [10,[32][33][34][35][36][37][38][39][40]. Due to the flexibility of the blockchain framework, a smart home ecosystem can shape easily.…”

Section: Related Workmentioning

confidence: 99%

Smart Home Gateway Based on Integration of Deep Reinforcement Learning and Blockchain Framework

2021

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The development of information and communication technology in terms of sensor technologies cause the Internet of Things (IoT) step toward smart homes for prevalent sensing and management of resources. The gateway connections contain various IoT devices in smart homes representing the security based on the centralized structure. To address the security purposes in this system, the blockchain framework is considered a smart home gateway to overcome the possible attacks and apply Deep Reinforcement Learning (DRL). The proposed blockchain-based smart home approach carefully evaluated the reliability and security in terms of accessibility, privacy, and integrity. To overcome traditional centralized architecture, blockchain is employed in the data store and exchange blocks. The data integrity inside and outside of the smart home cause the ability of network members to authenticate. The presented network implemented in the Ethereum blockchain, and the measurements are in terms of security, response time, and accuracy. The experimental results show that the proposed solution contains a better outperform than recent existing works. DRL is a learning-based algorithm which has the most effective aspects of the proposed approach to improve the performance of system based on the right values and combining with blockchain in terms of security of smart home based on the smart devices to overcome sharing and hacking the privacy. We have compared our proposed system with the other state-of-the-art and test this system in two types of datasets as NSL-KDD and KDD-CUP-99. DRL with an accuracy of 96.9% performs higher and has a stronger output compared with Artificial Neural Networks with an accuracy of 80.05% in the second stage, which contains 16% differences in terms of improving the accuracy of smart homes.

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“…Communities are quickly expanding and changing as a result of population expansion, economy, social, and cultural changes [1], [2]. The number of cars has increased as a result of urbanization, particularly in major cities, and issues concerning these vehicles have developed [3].…”

Section: Introductionmentioning

confidence: 99%

Improving model predictive control's optimization for urban traffic

Khelafa

Baghdad

Hachimi

2022

IJEECS

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<p><span>When it comes to decreasing traffic congestion and enhancing mobility, traffic forecasting is critical. However, due to the complicated spatio-temporal dynamics of urban transportation networks, which are difficult to describe, this task is tough. Using a model predictive controller (MPC) provides the control of a traffic network's architecture as well as errors in its operations. Based on a real-time simulation, a novel, accurate prediction controller for urban traffic was presented in this study to estimate the number of cars at junctions and their waiting duration. Different optimization approaches were employed and evaluated to improve the MPC's performance. Simulation results demonstrated that the fmincon was very robust and could effectively reduce the number of vehicles in the link, in comparison with other algorithms This study also includes an in-depth analysis of the characteristics of various prediction horizon sets in an MPC. By increasing the prediction horizon, the amplitude of fluctuation became more important, but when Np=4, the fluctuations reduced.</span></p>

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Improving the Performance of Single-Intersection Urban Traffic Networks Based on a Model Predictive Controller

Cited by 15 publications

References 45 publications

Designing the Controller-Based Urban Traffic Evaluation and Prediction Using Model Predictive Approach

Designing the Controller-Based Urban Traffic Evaluation and Prediction Using Model Predictive Approach

Smart Home Gateway Based on Integration of Deep Reinforcement Learning and Blockchain Framework

Improving model predictive control's optimization for urban traffic

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