Jing-Doo Wang¹, Chayadi Oktomy Noto Susanto^1,2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1711-1728, 2024, DOI:10.32604/cmes.2024.048955 - 20 May 2024

Abstract A significant obstacle in intelligent transportation systems (ITS) is the capacity to predict traffic flow. Recent advancements in deep neural networks have enabled the development of models to represent traffic flow accurately. However, accurately predicting traffic flow at the individual road level is extremely difficult due to the complex interplay of spatial and temporal factors. This paper proposes a technique for predicting short-term traffic flow data using an architecture that utilizes convolutional bidirectional long short-term memory (Conv-BiLSTM) with attention mechanisms. Prior studies neglected to include data pertaining to factors such as holidays, weather conditions, and More >

Jing-Doo Wang¹, Chayadi Oktomy Noto Susanto^1,2,*

CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3097-3112, 2023, DOI:10.32604/cmc.2023.040914 - 08 October 2023

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… More >

Fulu Wei¹, Xin Li¹, Yongqing Guo^1,*, Zhenyu Wang², Qingyin Li¹, Xueshi Ma³

Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 2001-2018, 2023, DOI:10.32604/iasc.2023.035799 - 21 June 2023

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… More >

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 - 08 June 2023

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… More >

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 >

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 - 19 July 2022

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… 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 - 28 July 2022

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… 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 - 28 July 2022

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… 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 - 29 March 2022

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 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 - 24 February 2022

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… More >