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 >

R. B. Sarooraj^*, S. Prayla Shyry

Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 2071-2084, 2023, DOI:10.32604/iasc.2023.034716 - 05 January 2023

Abstract In Intelligent Transportation Systems (ITS), controlling the traffic flow of a region in a city is the major challenge. Particularly, allocation of the traffic-free route to the taxi drivers during peak hours is one of the challenges to control the traffic flow. So, in this paper, the route between the taxi driver and pickup location or hotspot with the spatial-temporal dependencies is optimized. Initially, the hotspots in a region are clustered using the density-based spatial clustering of applications with noise (DBSCAN) algorithm to find the hot spots at the peak hours in an urban area.… 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 >

Xun Li¹, Wenzhe Ma^1,*, Zhengfan Zhao², Muhammad Bashir¹, Wenjie Wang¹, Xiaohua Wang¹

CMES-Computer Modeling in Engineering & Sciences, Vol.133, No.1, pp. 67-92, 2022, DOI:10.32604/cmes.2022.020019 - 18 July 2022

Abstract In a multi-lane area, the increasing randomness of lane changes contributes to traffic insecurity and local traffic flow instability. A study on safe lane shifting activity that focuses on threat assessment under real-time knowledge is necessary to enhance smooth vehicle flow. This paper proposed a more comprehensive lane changing guidance rule to investigate the status of surrounding vehicles to accommodate future vehicle-on-road collaborative environments based on these parameters 1) lane change demand and 2) treat assessment function. The collaborative relationships between vehicles are analyzed using a cellular automata model based on their location, velocity, and… More >

Li Lei¹, Zihao Wang^2,*, Yong Wu³

CMES-Computer Modeling in Engineering & Sciences, Vol.131, No.3, pp. 1815-1830, 2022, DOI:10.32604/cmes.2022.019855 - 19 April 2022

Abstract It is essential to fully understand master the traffic characteristics of the self-stabilizing control effect and road characteristics to ensure the regular operation of transportation. Traffic flow on curved roads and slopes is irregular and more complicated than that on the straight road. However, most of the research only considers the effect of self-stabilizing in the straight road. This study attempts to bridge this deficiency from the following three aspects. First, we review the potential influencing factors of traffic flow stability, which are related to the vehicle's steady velocity, history velocity, and the turn radius… 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 >

V. Rajalakshmi^1,*, S. Ganesh Vaidyanathan²

Intelligent Automation & Soft Computing, Vol.33, No.3, pp. 1335-1348, 2022, DOI:10.32604/iasc.2022.024310 - 24 March 2022

Abstract Traffic flow forecasting is the need of the hour requirement in Intelligent Transportation Systems (ITS). Various Artificial Intelligence Frameworks and Machine Learning Models are incorporated in today’s ITS to enhance forecasting. Tuning the model parameters play a vital role in designing an efficient model to improve the reliability of forecasting. Hence, the primary objective of this research is to propose a novel hybrid framework to tune the parameters of Multilayer Perceptron (MLP) using the Swarm Intelligence technique called Particle Swarm Optimization (PSO). The proposed MLP-PSO framework is designed to adjust the weights and bias parameters… 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 >