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  • Open Access

    ARTICLE

    Predicting Traffic Flow Using Dynamic Spatial-Temporal Graph Convolution Networks

    Yunchang Liu1,*, Fei Wan1, Chengwu Liang2

    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 4343-4361, 2024, DOI:10.32604/cmc.2024.047211 - 26 March 2024

    Abstract Traffic flow prediction plays a key role in the construction of intelligent transportation system. However, due to its complex spatio-temporal dependence and its uncertainty, the research becomes very challenging. Most of the existing studies are based on graph neural networks that model traffic flow graphs and try to use fixed graph structure to deal with the relationship between nodes. However, due to the time-varying spatial correlation of the traffic network, there is no fixed node relationship, and these methods cannot effectively integrate the temporal and spatial features. This paper proposes a novel temporal-spatial dynamic graph More >

  • Open Access

    ARTICLE

    Deep Learning Based Vehicle Detection and Counting System for Intelligent Transportation

    A. Vikram1, J. Akshya2, Sultan Ahmad3,4, L. Jerlin Rubini5, Seifedine Kadry6,7,8, Jungeun Kim9,*

    Computer Systems Science and Engineering, Vol.48, No.1, pp. 115-130, 2024, DOI:10.32604/csse.2023.037928 - 26 January 2024

    Abstract Traffic monitoring through remote sensing images (RSI) is considered an important research area in Intelligent Transportation Systems (ITSs). Vehicle counting systems must be simple enough to be implemented in real-time. With the fast expansion of road traffic, real-time vehicle counting becomes essential in constructing ITS. Compared with conventional technologies, the remote sensing-related technique for vehicle counting exhibits greater significance and considerable advantages in its flexibility, low cost, and high efficiency. But several techniques need help in balancing complexity and accuracy technique. Therefore, this article presents a deep learning-based vehicle detection and counting system for ITS More >

  • Open Access

    ARTICLE

    Traffic Control Based on Integrated Kalman Filtering and Adaptive Quantized Q-Learning Framework for Internet of Vehicles

    Othman S. Al-Heety1,*, Zahriladha Zakaria1,*, Ahmed Abu-Khadrah2, Mahamod Ismail3, Sarmad Nozad Mahmood4, Mohammed Mudhafar Shakir5, Sameer Alani6, Hussein Alsariera1

    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2103-2127, 2024, DOI:10.32604/cmes.2023.029509 - 15 December 2023

    Abstract Intelligent traffic control requires accurate estimation of the road states and incorporation of adaptive or dynamically adjusted intelligent algorithms for making the decision. In this article, these issues are handled by proposing a novel framework for traffic control using vehicular communications and Internet of Things data. The framework integrates Kalman filtering and Q-learning. Unlike smoothing Kalman filtering, our data fusion Kalman filter incorporates a process-aware model which makes it superior in terms of the prediction error. Unlike traditional Q-learning, our Q-learning algorithm enables adaptive state quantization by changing the threshold of separating low traffic from… More >

  • Open Access

    ARTICLE

    CNN-Based Multi-Object Tracking Networks with Position Correction and IMM in Intelligent Transportation System

    Chengjuan Ren1, Guangnan Zhang2,*, Dongwon Jeong3, Lele Hao4

    Revista Internacional de Métodos Numéricos para Cálculo y Diseño en Ingeniería, Vol.39, No.4, pp. 1-12, 2023, DOI:10.23967/j.rimni.2023.11.001

    Abstract In multi-object tracking of Intelligent Transportation System, there are objects of different sizes in images or videos, especially pedestrian and traffic lights with low resolution in the image. Meantime, objects are subject to occlusion or loss in object tracking. All of the above-mentioned situations may lead to unsatisfactory multi-object tracking results. Attracted by the effect of deep convolution neural networks, the paper proposes a multi-object tracking network, CNN-Based Multi-Object Tracking Networks with Position Correction and IMM (CNN_PC_IMM) to solve those problems. Our proposed method consists of object detection module and object tracking module. Compared to… More >

  • Open Access

    ARTICLE

    Analyzing the Impact of Blockchain Models for Securing Intelligent Logistics through Unified Computational Techniques

    Mohammed S. Alsaqer1, Majid H. Alsulami2,*, Rami N. Alkhawaji3, Abdulellah A. Alaboudi2

    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3943-3968, 2023, DOI:10.32604/cmc.2023.042379 - 26 December 2023

    Abstract Blockchain technology has revolutionized conventional trade. The success of blockchain can be attributed to its distributed ledger characteristic, which secures every record inside the ledger using cryptography rules, making it more reliable, secure, and tamper-proof. This is evident by the significant impact that the use of this technology has had on people connected to digital spaces in the present-day context. Furthermore, it has been proven that blockchain technology is evolving from new perspectives and that it provides an effective mechanism for the intelligent transportation system infrastructure. To realize the full potential of the accurate and… More >

  • Open Access

    ARTICLE

    YOLO and Blockchain Technology Applied to Intelligent Transportation License Plate Character Recognition for Security

    Fares Alharbi1, Reem Alshahrani2, Mohammed Zakariah3,*, Amjad Aldweesh1, Abdulrahman Abdullah Alghamdi1

    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3697-3722, 2023, DOI:10.32604/cmc.2023.040086 - 26 December 2023

    Abstract Privacy and trust are significant issues in intelligent transportation systems (ITS). Data security is critical in ITS systems since sensitive user data is communicated to another user over the internet through wireless devices and routes such as radio channels, optical fiber, and blockchain technology. The Internet of Things (IoT) is a network of connected, interconnected gadgets. Privacy issues occasionally arise due to the amount of data generated. However, they have been primarily addressed by blockchain and smart contract technology. While there are still security issues with smart contracts, primarily due to the complexity of writing… More >

  • Open Access

    ARTICLE

    A Nonlinear Spatiotemporal Optimization Method of Hypergraph Convolution Networks for Traffic Prediction

    Difeng Zhu1, Zhimou Zhu2, Xuan Gong1, Demao Ye1, Chao Li3,*, Jingjing Chen4,*

    Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 3083-3100, 2023, DOI:10.32604/iasc.2023.040517 - 11 September 2023

    Abstract Traffic prediction is a necessary function in intelligent transportation systems to alleviate traffic congestion. Graph learning methods mainly focus on the spatiotemporal dimension, but ignore the nonlinear movement of traffic prediction and the high-order relationships among various kinds of road segments. There exist two issues: 1) deep integration of the spatiotemporal information and 2) global spatial dependencies for structural properties. To address these issues, we propose a nonlinear spatiotemporal optimization method, which introduces hypergraph convolution networks (HGCN). The method utilizes the higher-order spatial features of the road network captured by HGCN, and dynamically integrates them More >

  • Open Access

    ARTICLE

    Research on Parking Path Planing Based on A-Star Algorithm

    Zhiliang Deng, Dong Wang*

    Journal of New Media, Vol.5, No.1, pp. 55-64, 2023, DOI:10.32604/jnm.2023.040252 - 14 June 2023

    Abstract The issue of finding available parking spaces and mitigating congestion during parking is a persistent challenge for numerous car owners in urban areas. In this paper, we propose a novel method based on the A-star algorithm to calculate the optimal parking path to address this issue. We integrate a road impedance function into the conventional A-star algorithm to compute path duration and adopt a fusion function composed of path length and duration as the weight matrix for the A-star algorithm to achieve optimal path planning. Furthermore, we conduct simulations using parking lot modeling to validate More >

  • Open Access

    ARTICLE

    Parameter Tuned Deep Learning Based Traffic Critical Prediction Model on Remote Sensing Imaging

    Sarkar Hasan Ahmed1, Adel Al-Zebari2, Rizgar R. Zebari3, Subhi R. M. Zeebaree4,*

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3993-4008, 2023, DOI:10.32604/cmc.2023.037464 - 31 March 2023

    Abstract Remote sensing (RS) presents laser scanning measurements, aerial photos, and high-resolution satellite images, which are utilized for extracting a range of traffic-related and road-related features. RS has a weakness, such as traffic fluctuations on small time scales that could distort the accuracy of predicted road and traffic features. This article introduces an Optimal Deep Learning for Traffic Critical Prediction Model on High-Resolution Remote Sensing Images (ODLTCP-HRRSI) to resolve these issues. The presented ODLTCP-HRRSI technique majorly aims to forecast the critical traffic in smart cities. To attain this, the presented ODLTCP-HRRSI model performs two major processes. More >

  • Open Access

    ARTICLE

    Short Term Traffic Flow Prediction Using Hybrid Deep Learning

    Mohandu Anjaneyulu, Mohan Kubendiran*

    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1641-1656, 2023, DOI:10.32604/cmc.2023.035056 - 06 February 2023

    Abstract Traffic flow prediction in urban areas is essential in the Intelligent Transportation System (ITS). Short Term Traffic Flow (STTF) prediction impacts traffic flow series, where an estimation of the number of vehicles will appear during the next instance of time per hour. Precise STTF is critical in Intelligent Transportation System. Various extinct systems aim for short-term traffic forecasts, ensuring a good precision outcome which was a significant task over the past few years. The main objective of this paper is to propose a new model to predict STTF for every hour of a day. In… More >

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