Yali Cao¹, Weijian Hu^1,2, Lingfang Li^1,*, Minchao Li¹, Meng Xu², Ke Han²

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-23, 2026, DOI:10.32604/cmc.2025.069373 - 09 December 2025

Abstract Traffic flow prediction constitutes a fundamental component of Intelligent Transportation Systems (ITS), playing a pivotal role in mitigating congestion, enhancing route optimization, and improving the utilization efficiency of roadway infrastructure. However, existing methods struggle in complex traffic scenarios due to static spatio-temporal embedding, restricted multi-scale temporal modeling, and weak representation of local spatial interactions. This study proposes Bi-STAT+, an enhanced bidirectional spatio-temporal attention framework to address existing limitations through three principal contributions: (1) an adaptive spatio-temporal embedding module that dynamically adjusts embeddings to capture complex traffic variations; (2) frequency-domain analysis in the temporal dimension for… More >

Ahmed Alhussen¹, Arshiya S. Ansari^2,*

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 >

Ming Luo¹, Huili Dou², Ning Zheng^3,*

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 >

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 >