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  • Open Access


    Multi-Objective Optimization of Traffic Signal Timing at Typical Junctions Based on Genetic Algorithms

    Zeyu Zhang1, Han Zhu1, Wei Zhang1, Zhiming Cai2,*, Linkai Zhu2, Zefeng Li2

    Computer Systems Science and Engineering, Vol.47, No.2, pp. 1901-1917, 2023, DOI:10.32604/csse.2023.039395

    Abstract With the rapid development of urban road traffic and the increasing number of vehicles, how to alleviate traffic congestion is one of the hot issues that need to be urgently addressed in building smart cities. Therefore, in this paper, a nonlinear multi-objective optimization model of urban intersection signal timing based on a Genetic Algorithm was constructed. Specifically, a typical urban intersection was selected as the research object, and drivers’ acceleration habits were taken into account. What’s more, the shortest average delay time, the least average number of stops, and the maximum capacity of the intersection were regarded as the optimization… More >

  • Open Access


    Pre-Locator Incorporating Swin-Transformer Refined Classifier for Traffic Sign Recognition

    Qiang Luo1, Wenbin Zheng1,2,*

    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 2227-2246, 2023, DOI:10.32604/iasc.2023.040195

    Abstract In the field of traffic sign recognition, traffic signs usually occupy very small areas in the input image. Most object detection algorithms directly reduce the original image to a specific size for the input model during the detection process, which leads to the loss of small object information. Additionally, classification tasks are more sensitive to information loss than localization tasks. This paper proposes a novel traffic sign recognition approach, in which a lightweight pre-locator network and a refined classification network are incorporated. The pre-locator network locates the sub-regions of the traffic signs from the original image, and the refined classification… More >

  • Open Access


    Traffic Sign Detection with Low Complexity for Intelligent Vehicles Based on Hybrid Features

    Sara Khalid1, Jamal Hussain Shah1,*, Muhammad Sharif1, Muhammad Rafiq2, Gyu Sang Choi3,*

    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 861-879, 2023, DOI:10.32604/cmc.2023.035595

    Abstract Globally traffic signs are used by all countries for healthier traffic flow and to protect drivers and pedestrians. Consequently, traffic signs have been of great importance for every civilized country, which makes researchers give more focus on the automatic detection of traffic signs. Detecting these traffic signs is challenging due to being in the dark, far away, partially occluded, and affected by the lighting or the presence of similar objects. An innovative traffic sign detection method for red and blue signs in color images is proposed to resolve these issues. This technique aimed to devise an efficient, robust and accurate… More >

  • Open Access


    A Deep Learning Model of Traffic Signs in Panoramic Images Detection

    Kha Tu Huynh1, Thi Phuong Linh Le1, Muhammad Arif2, Thien Khai Tran3,*

    Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 401-418, 2023, DOI:10.32604/iasc.2023.036981

    Abstract To pursue the ideal of a safe high-tech society in a time when traffic accidents are frequent, the traffic signs detection system has become one of the necessary topics in recent years and in the future. The ultimate goal of this research is to identify and classify the types of traffic signs in a panoramic image. To accomplish this goal, the paper proposes a new model for traffic sign detection based on the Convolutional Neural Network for comprehensive traffic sign classification and Mask Region-based Convolutional Neural Networks (R-CNN) implementation for identifying and extracting signs in panoramic images. Data augmentation and… More >

  • Open Access


    Numerical Simulation Study of Vibration Characteristics of Cantilever Traffic Signal Support Structure under Wind Environment

    Meng Zhang1, Zhichao Zhou1, Guifeng Zhao1,*, Fangfang Wang2,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.1, pp. 673-698, 2023, DOI:10.32604/cmes.2022.021463

    Abstract Computational fluid dynamics (CFD) and the finite element method (FEM) are used to investigate the wind-driven dynamic response of cantilever traffic signal support structures as a whole. By building a finite element model with the same scale as the actual structure and performing modal analysis, a preliminary understanding of the dynamic properties of the structure is obtained. Based on the two-way fluid-structure coupling calculation method, the wind vibration response of the structure under different incoming flow conditions is calculated, and the vibration characteristics of the structure are analyzed through the displacement time course data of the structure in the cross-wind… More >

  • Open Access


    Identification and Acknowledgment of Programmed Traffic Sign Utilizing Profound Convolutional Neural Organization

    P. Vigneshwaran1,*, N. Prasath1, M. Islabudeen2, A. Arun1, A. K. Sampath2

    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 1527-1543, 2023, DOI:10.32604/iasc.2023.028444

    Abstract Traffic signs are basic security workplaces making the rounds, which expects a huge part in coordinating busy time gridlock direct, ensuring the prosperity of the road and dealing with the smooth segment of vehicles and individuals by walking, etc. As a segment of the clever transportation structure, the acknowledgment of traffic signs is basic for the driving assistance system, traffic sign upkeep, self-administering driving, and various spaces. There are different assessments turns out achieved for traffic sign acknowledgment in the world. However, most of the works are only for explicit arrangements of traffic signs, for example, beyond what many would… More >

  • Open Access


    Optimizing Traffic Signals in Smart Cities Based on Genetic Algorithm

    Nagham A. Al-Madi*, Adnan A. Hnaif

    Computer Systems Science and Engineering, Vol.40, No.1, pp. 65-74, 2022, DOI:10.32604/csse.2022.016730

    Abstract Current traffic signals in Jordan suffer from severe congestion due to many factors, such as the considerable increase in the number of vehicles and the use of fixed timers, which still control existing traffic signals. This condition affects travel demand on the streets of Jordan. This study aims to improve an intelligent road traffic management system (IRTMS) derived from the human community-based genetic algorithm (HCBGA) to mitigate traffic signal congestion in Amman, Jordan’s capital city. The parameters considered for IRTMS are total time and waiting time, and fixed timers are still used for control. By contrast, the enhanced system, called… More >

  • Open Access


    Traffic Sign Recognition Method Integrating Multi-Layer Features and Kernel Extreme Learning Machine Classifier

    Wei Sun1,3,*, Hongji Du1, Shoubai Nie2,3, Xiaozheng He4

    CMC-Computers, Materials & Continua, Vol.60, No.1, pp. 147-161, 2019, DOI:10.32604/cmc.2019.03581

    Abstract Traffic sign recognition (TSR), as a critical task to automated driving and driver assistance systems, is challenging due to the color fading, motion blur, and occlusion. Traditional methods based on convolutional neural network (CNN) only use an end-layer feature as the input to TSR that requires massive data for network training. The computation-intensive network training process results in an inaccurate or delayed classification. Thereby, the current state-of-the-art methods have limited applications. This paper proposes a new TSR method integrating multi-layer feature and kernel extreme learning machine (ELM) classifier. The proposed method applies CNN to extract the multi-layer features of traffic… More >

  • Open Access


    Improved VGG Model for Road Traffic Sign Recognition

    Shuren Zhou1,2,*, Wenlong Liang1,2, Junguo Li1,2, Jeong-Uk Kim3

    CMC-Computers, Materials & Continua, Vol.57, No.1, pp. 11-24, 2018, DOI:10.32604/cmc.2018.02617

    Abstract Road traffic sign recognition is an important task in intelligent transportation system. Convolutional neural networks (CNNs) have achieved a breakthrough in computer vision tasks and made great success in traffic sign classification. In this paper, it presents a road traffic sign recognition algorithm based on a convolutional neural network. In natural scenes, traffic signs are disturbed by factors such as illumination, occlusion, missing and deformation, and the accuracy of recognition decreases, this paper proposes a model called Improved VGG (IVGG) inspired by VGG model. The IVGG model includes 9 layers, compared with the original VGG model, it is added max-pooling… More >

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