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Search Results (13)
  • Open Access


    Traffic Flow Prediction with Heterogenous Data Using a Hybrid CNN-LSTM Model

    Jing-Doo Wang1, Chayadi Oktomy Noto Susanto1,2,*

    CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3097-3112, 2023, DOI:10.32604/cmc.2023.040914

    Abstract Predicting traffic flow is a crucial component of an intelligent transportation system. Precisely monitoring and predicting traffic flow remains a challenging endeavor. However, existing methods for predicting traffic flow do not incorporate various external factors or consider the spatiotemporal correlation between spatially adjacent nodes, resulting in the loss of essential information and lower forecast performance. On the other hand, the availability of spatiotemporal data is limited. This research offers alternative spatiotemporal data with three specific features as input, vehicle type (5 types), holidays (3 types), and weather (10 conditions). In this study, the proposed model combines the advantages of the… More >

  • Open Access


    Flow Direction Level Traffic Flow Prediction Based on a GCN-LSTM Combined Model

    Fulu Wei1, Xin Li1, Yongqing Guo1,*, Zhenyu Wang2, Qingyin Li1, Xueshi Ma3

    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 2001-2018, 2023, DOI:10.32604/iasc.2023.035799

    Abstract Traffic flow prediction plays an important role in intelligent transportation systems and is of great significance in the applications of traffic control and urban planning. Due to the complexity of road traffic flow data, traffic flow prediction has been one of the challenging tasks to fully exploit the spatiotemporal characteristics of roads to improve prediction accuracy. In this study, a combined flow direction level traffic flow prediction graph convolutional network (GCN) and long short-term memory (LSTM) model based on spatiotemporal characteristics is proposed. First, a GCN model is employed to capture the topological structure of the data graph and extract… More >

  • Open Access


    Kalman Filter-Based CNN-BiLSTM-ATT Model for Traffic Flow Prediction

    Hong Zhang1,2,*, Gang Yang1, Hailiang Yu1, Zan Zheng1

    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 1047-1063, 2023, DOI:10.32604/cmc.2023.039274

    Abstract To accurately predict traffic flow on the highways, this paper proposes a Convolutional Neural Network-Bi-directional Long Short-Term Memory-Attention Mechanism (CNN-BiLSTM-Attention) traffic flow prediction model based on Kalman-filtered data processing. Firstly, the original fluctuating data is processed by Kalman filtering, which can reduce the instability of short-term traffic flow prediction due to unexpected accidents. Then the local spatial features of the traffic data during different periods are extracted, dimensionality is reduced through a one-dimensional CNN, and the BiLSTM network is used to analyze the time series information. Finally, the Attention Mechanism assigns feature weights and performs Softmax regression. The experimental results… More >

  • Open Access


    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

    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 this paper, we have proposed… More >

  • Open Access


    Intelligent Slime Mould Optimization with Deep Learning Enabled Traffic Prediction in Smart Cities

    Manar Ahmed Hamza1,*, Hadeel Alsolai2, Jaber S. Alzahrani3, Mohammad Alamgeer4,5, 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 >

  • Open Access


    Optimal Logistics Activities Based Deep Learning Enabled Traffic Flow Prediction Model

    Basim Aljabhan1, Mahmoud Ragab2,3,4,*, Sultanah M. Alshammari4,5, Abdullah S. Al-Malaise Al-Ghamdi4,6,7

    CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 5269-5282, 2022, DOI:10.32604/cmc.2022.030694

    Abstract Traffic flow prediction becomes an essential process for intelligent transportation systems (ITS). Though traffic sensor devices are manually controllable, traffic flow data with distinct length, uneven sampling, and missing data finds challenging for effective exploitation. The traffic data has been considerably increased in recent times which cannot be handled by traditional mathematical models. The recent developments of statistic and deep learning (DL) models pave a way for the effectual design of traffic flow prediction (TFP) models. In this view, this study designs optimal attention-based deep learning with statistical analysis for TFP (OADLSA-TFP) model. The presented OADLSA-TFP model intends to effectually… More >

  • Open Access


    Networking Controller Based Real Time Traffic Prediction in Clustered Vehicular Adhoc Networks

    T. S. Balaji1,2, S. Srinivasan3,*

    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 >

  • Open Access


    Sustainable Energy Management with Traffic Prediction Strategy for Autonomous Vehicle Systems

    Manar Ahmed Hamza1,*, 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 >

  • Open Access


    An Optimal Deep Learning for Cooperative Intelligent Transportation System

    K. Lakshmi1, Srinivas Nagineni2, E. Laxmi Lydia3, A. Francis Saviour Devaraj4, Sachi Nandan Mohanty5, Irina V. Pustokhina6,*, Denis A. Pustokhin7

    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 19-35, 2022, DOI:10.32604/cmc.2022.020244

    Abstract Cooperative Intelligent Transport System (C-ITS) plays a vital role in the future road traffic management system. A vital element of C-ITS comprises vehicles, road side units, and traffic command centers, which produce a massive quantity of data comprising both mobility and service-related data. For the extraction of meaningful and related details out of the generated data, data science acts as an essential part of the upcoming C-ITS applications. At the same time, prediction of short-term traffic flow is highly essential to manage the traffic accurately. Due to the rapid increase in the amount of traffic data, deep learning (DL) models… More >

  • Open Access


    Modeling of Artificial Intelligence Based Traffic Flow Prediction with Weather Conditions

    Mesfer Al Duhayyim1, Amani Abdulrahman Albraikan2, Fahd N. Al-Wesabi3,4, Hiba M. Burbur5, Mohammad Alamgeer6, Anwer Mustafa Hilal7, Manar Ahmed Hamza7,*, Mohammed Rizwanullah7

    CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 3953-3968, 2022, DOI:10.32604/cmc.2022.022692

    Abstract Short-term traffic flow prediction (TFP) is an important area in intelligent transportation system (ITS), which is used to reduce traffic congestion. But the avail of traffic flow data with temporal features and periodic features are susceptible to weather conditions, making TFP a challenging issue. TFP process are significantly influenced by several factors like accident and weather. Particularly, the inclement weather conditions may have an extreme impact on travel time and traffic flow. Since most of the existing TFP techniques do not consider the impact of weather conditions on the TF, it is needed to develop effective TFP with the consideration… More >

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