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  • Open Access

    ARTICLE

    Real-Time Prediction of Urban Traffic Problems Based on Artificial Intelligence-Enhanced Mobile Ad Hoc Networks (MANETS)

    Ahmed Alhussen1, Arshiya S. Ansari2,*

    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 1903-1923, 2024, DOI:10.32604/cmc.2024.049260 - 15 May 2024

    Abstract Traffic in today’s cities is a serious problem that increases travel times, negatively affects the environment, and drains financial resources. This study presents an Artificial Intelligence (AI) augmented Mobile Ad Hoc Networks (MANETs) based real-time prediction paradigm for urban traffic challenges. MANETs are wireless networks that are based on mobile devices and may self-organize. The distributed nature of MANETs and the power of AI approaches are leveraged in this framework to provide reliable and timely traffic congestion forecasts. This study suggests a unique Chaotic Spatial Fuzzy Polynomial Neural Network (CSFPNN) technique to assess real-time data… More >

  • Open Access

    ARTICLE

    Spatiotemporal Prediction of Urban Traffics Based on Deep GNN

    Ming Luo1, Huili Dou2, Ning Zheng3,*

    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 265-282, 2024, DOI:10.32604/cmc.2023.040067 - 30 January 2024

    Abstract Traffic prediction already plays a significant role in applications like traffic planning and urban management, but it is still difficult to capture the highly non-linear and complicated spatiotemporal correlations of traffic data. As well as to fulfil both long-term and short-term prediction objectives, a better representation of the temporal dependency and global spatial correlation of traffic data is needed. In order to do this, the Spatiotemporal Graph Neural Network (S-GNN) is proposed in this research as a method for traffic prediction. The S-GNN simultaneously accepts various traffic data as inputs and investigates the non-linear correlations… More >

  • Open Access

    ARTICLE

    Precise Evaluation of Vehicles Emission in Urban Traffic Using Multi-agent-based Traffic Simulator MATES

    Hideki Fujii1, Shinobu Yoshimura1

    CMES-Computer Modeling in Engineering & Sciences, Vol.88, No.1, pp. 49-64, 2012, DOI:10.3970/cmes.2012.088.049

    Abstract Recently, global warming issues have been discussed all over the world. Of the total amount of CO2 emitted in Japan, a transportation sector is responsible for 20%. In the transportation sector, 90% of the emission is due to road traffic. This amount must be reduced drastically to realize a low-carbon society. To do so, various measures have been discussed, and the effects of the measures must be estimated quantitatively. In conventional measurement methods, the amount of vehicle emission is simply calculated by multiplying travel distance or gasoline consumption by a specified emission coefficient. Such an approach… More >

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