Difeng Zhu¹, Zhimou Zhu², Xuan Gong¹, Demao Ye¹, Chao Li^3,*, Jingjing Chen^4,*

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

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 with the historical data to… More >

Sarkar Hasan Ahmed¹, Adel Al-Zebari², Rizgar R. Zebari³, Subhi R. M. Zeebaree^4,*

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

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. At the initial stage, the… More >

Guoqing Xu¹, Changsen Xia¹, Jun Qian¹, Guo Ran³, Zilong Jin^1,2,*

Journal on Internet of Things, Vol.4, No.2, pp. 113-125, 2022, DOI:10.32604/jiot.2022.036066

Abstract Huge networks and increasing network traffic will consume more and more resources. It is critical to predict network traffic accurately and timely for network planning, and resource allocation, etc. In this paper, a combined network traffic prediction model is proposed, which is based on Prophet, evolutionary attention-based LSTM (EALSTM) network, and Gaussian process regression (GPR). According to the non-smooth, sudden, periodic, and long correlation characteristics of network traffic, the prediction procedure is divided into three steps to predict network traffic accurately. In the first step, the Prophet model decomposes network traffic data into periodic and non-periodic parts. The periodic term… More >

Manar Ahmed Hamza^1,*, Hadeel Alsolai², Jaber S. Alzahrani³, Mohammad Alamgeer^4,5, Mohamed Mahmoud Sayed⁶, Abu Sarwar Zamani¹, Ishfaq Yaseen¹, Abdelwahed Motwakel¹

CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 6563-6577, 2022, DOI:10.32604/cmc.2022.031541

Abstract Intelligent Transportation System (ITS) is one of the revolutionary technologies in smart cities that helps in reducing traffic congestion and enhancing traffic quality. With the help of big data and communication technologies, ITS offers real-time investigation and highly-effective traffic management. Traffic Flow Prediction (TFP) is a vital element in smart city management and is used to forecast the upcoming traffic conditions on transportation network based on past data. Neural Network (NN) and Machine Learning (ML) models are widely utilized in resolving real-time issues since these methods are capable of dealing with adaptive data over a period of time. Deep Learning… More >

T. S. Balaji^1,2, S. Srinivasan^3,*

Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2189-2203, 2023, DOI:10.32604/iasc.2023.028785

Abstract The vehicular ad hoc network (VANET) is an emerging network technology that has gained popularity because to its low cost, flexibility, and seamless services. Software defined networking (SDN) technology plays a critical role in network administration in the future generation of VANET with fifth generation (5G) networks. Regardless of the benefits of VANET, energy economy and traffic control are significant architectural challenges. Accurate and real-time traffic flow prediction (TFP) becomes critical for managing traffic effectively in the VANET. SDN controllers are a critical issue in VANET, which has garnered much interest in recent years. With this objective, this study develops… More >

Ashit Kumar Dutta¹, S. Srinivasan², S. N. Kumar³, T. S. Balaji^4,5, Won Il Lee⁶, Gyanendra Prasad Joshi⁷, Sung Won Kim^8,*

CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5563-5576, 2022, DOI:10.32604/cmc.2022.027707

Abstract Due to the advances of intelligent transportation system (ITSs), traffic forecasting has gained significant interest as robust traffic prediction acts as an important part in different ITSs namely traffic signal control, navigation, route mapping, etc. The traffic prediction model aims to predict the traffic conditions based on the past traffic data. For more accurate traffic prediction, this study proposes an optimal deep learning-enabled statistical analysis model. This study offers the design of optimal convolutional neural network with attention long short term memory (OCNN-ALSTM) model for traffic prediction. The proposed OCNN-ALSTM technique primarily pre-processes the traffic data by the use of… More >

Manar Ahmed Hamza^1,*, Masoud Alajmi², Jaber S. Alzahrani³, Siwar Ben Haj Hassine⁴, Abdelwahed Motwakel¹, Ishfaq Yaseen¹

CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 3465-3479, 2022, DOI:10.32604/cmc.2022.026066

Abstract Recent advancements of the intelligent transportation system (ITS) provide an effective way of improving the overall efficiency of the energy management strategy (EMSs) for autonomous vehicles (AVs). The use of AVs possesses many advantages such as congestion control, accident prevention, and etc. However, energy management and traffic flow prediction (TFP) still remains a challenging problem in AVs. The complexity and uncertainties of driving situations adequately affect the outcome of the designed EMSs. In this view, this paper presents novel sustainable energy management with traffic flow prediction strategy (SEM-TPS) for AVs. The SEM-TPS technique applies type II fuzzy logic system (T2FLS)… More >

M. Govindarajan^1,*, V. Chandrasekaran², S. Anitha³

Computer Systems Science and Engineering, Vol.40, No.3, pp. 851-863, 2022, DOI:10.32604/csse.2022.019298

Abstract Accurate cellular network traffic prediction is a crucial task to access Internet services for various devices at any time. With the use of mobile devices, communication services generate numerous data for every moment. Given the increasing dense population of data, traffic learning and prediction are the main components to substantially enhance the effectiveness of demand-aware resource allocation. A novel deep learning technique called radial kernelized LSTM-based connectionist Tversky multilayer deep structure learning (RKLSTM-CTMDSL) model is introduced for traffic prediction with superior accuracy and minimal time consumption. The RKLSTM-CTMDSL model performs attribute selection and classification processes for cellular traffic prediction. In… More >