Hong Zhang^1,2,*, 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 >

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 >

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 >

Basim Aljabhan¹, Mahmoud Ragab^2,3,4,*, Sultanah M. Alshammari^4,5, Abdullah S. Al-Malaise Al-Ghamdi^4,6,7

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 >

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 >

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 >

K. Lakshmi¹, Srinivas Nagineni², E. Laxmi Lydia³, A. Francis Saviour Devaraj⁴, Sachi Nandan Mohanty⁵, Irina V. Pustokhina^6,*, 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 >

Mesfer Al Duhayyim¹, Amani Abdulrahman Albraikan², Fahd N. Al-Wesabi^3,4, Hiba M. Burbur⁵, Mohammad Alamgeer⁶, Anwer Mustafa Hilal⁷, Manar Ahmed Hamza^7,*, 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 >

P. Thamizhazhagan^1,*, M. Sujatha², S. Umadevi³, K. Priyadarshini⁴, Velmurugan Subbiah Parvathy⁵, Irina V. Pustokhina⁶, Denis A. Pustokhin⁷

CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3333-3347, 2022, DOI:10.32604/cmc.2022.020197

Abstract There is a paradigm shift happening in automotive industry towards electric vehicles as environment and sustainability issues gained momentum in the recent years among potential users. Connected and Autonomous Electric Vehicle (CAEV) technologies are fascinating the automakers and inducing them to manufacture connected autonomous vehicles with self-driving features such as autopilot and self-parking. Therefore, Traffic Flow Prediction (TFP) is identified as a major issue in CAEV technologies which needs to be addressed with the help of Deep Learning (DL) techniques. In this view, the current research paper presents an artificial intelligence-based parallel autoencoder for TFP, abbreviated as AIPAE-TFP model in… More >

Xijun Zhang^*, Qirui Zhang

CMES-Computer Modeling in Engineering & Sciences, Vol.125, No.1, pp. 95-109, 2020, DOI:10.32604/cmes.2020.011013

Abstract According to the time series characteristics of the trajectory history data, we predicted and analyzed the traffic flow. This paper proposed a LSTMXGBoost model based urban road short-term traffic flow prediction in order to analyze and solve the problems of periodicity, stationary and abnormality of time series. It can improve the traffic flow prediction effect, achieve efficient traffic guidance and traffic control. The model combined the characteristics of LSTM (Long Short-Term Memory) network and XGBoost (Extreme Gradient Boosting) algorithms. First, we used the LSTM model that increases dropout layer to train the data set after preprocessing. Second, we replaced the… More >