Mohammed Alonazi¹, Muhammad Adeel Ahmed Tahir², Adnan Ahmed Rafique², Maha Abdelhaq³, Raed Alsaqour⁴, Ahmad Jalal^5,6, Jeongmin Park^7,*

CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.086550 - 15 September 2026

Abstract Accurate vehicle detection, tracking, and counting are fundamental components of Intelligent Transportation Systems (ITS) and urban traffic surveillance. However, real-world deployment remains challenging due to domain shifts, illumination variations, occlusions, dense traffic conditions, and heterogeneous data distributions. Existing studies often address detection, tracking, and counting as independent tasks, resulting in limited cross-domain generalization and inconsistent performance in complex traffic environments. To overcome these limitations, this paper proposes a unified cross-domain framework that jointly integrates vehicle detection, tracking, and lane-aware counting within a single intelligent traffic analytics pipeline. The proposed framework begins with image enhancement using… More >

Inam Ullah¹, Zeeshan Ali Haider², Omar Almomani³, Karamath Ateeq⁴, Chang Choi^1,*

CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.086072 - 15 September 2026

Abstract The growing need for intelligent, information-based, and automated transportation systems has been brought about by the rapid progress of intelligent vehicles and Intelligent Transportation Systems (ITS). Artificial Intelligence (AI) has emerged as an indispensable asset for the challenges and opportunities of today’s transportation, improving decision-making, flexibility, and system efficiency. This survey examines breakthroughs in AI techniques applied to smart vehicles and ITS between 2019 and 2026, focusing on Machine Learning (ML), Deep Learning (DL), Reinforcement Learning (RL), Federated Learning (FL), and Computer Vision. The survey also includes the integration of AI with enabling technologies, such… More >

Jeng-Shyang Pan^1,2,3, Cuijing Cao⁴, Shu-Chuan Chu^2,*, Lingping Kong⁵, Xingsi Xue⁶, Jia Zhao⁷

CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.085803 - 15 September 2026

Abstract This study proposes a Genetic Programming-based Search Strategy Generation Framework (GP-SSGF) and a novel variant of the tumbleweed algorithm, the genetic programming-based tumbleweed algorithm (GPTA). The framework automates the evolution of search formulas within metaheuristic algorithms, reducing reliance on manually designed update rules and enhancing adaptability. The GPTA algorithm, developed within this framework, employs evolved position-update formulas to improve search efficiency and convergence. Through extensive experiments on the CEC2017 benchmark suite across multiple dimensions, GPTA demonstrates superior solution quality and stability compared with other metaheuristic algorithms. Its practical effectiveness is further validated in emergency material More >

Mahbub Hassan¹, Md Kamrul Islam^2,*, Md Shafiul Alam³, Mohammad Bin Amin^4,5,*, M. M. Hafizur Rahman⁶, Md Ehtesamul Haque⁷, Zoltán Nagy⁸

CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.085434 - 15 September 2026

Abstract The integration of the Internet of Things (IoT) into Intelligent Transportation Systems (ITS) is transforming urban mobility through widespread sensing, real-time data exchange, and Artificial Intelligence (AI)-driven adaptive control. Although research in this domain has expanded rapidly, bibliometric analyses combined with critical thematic synthesis remain limited. This study addresses this gap through a two-stage analysis of 574 peer-reviewed articles indexed in Scopus from 2011 to 2024. Using performance analysis, keyword co-occurrence mapping, and co-authorship network visualization, the study maps global publication trends, institutional productivity, and collaboration patterns. The results show an annual growth rate of… More >

Afzal Badshah^1,*, Ali Daud^2,*, Sachi Arafat³, Wafa Almukadi⁴, Riad Alharbey⁴, Hussain Dawood⁵

CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.077836 - 15 September 2026

Abstract The rapid increase in vehicle numbers and resulting traffic congestion have amplified critical challenges related to safety, environmental impact, and transportation efficiency. Road accidents account for approximately 1.19 million deaths annually, with an additional 20 to 50 million people injured. Moreover, congestion leads to the loss of nearly 50 billion hours and around 3 billion gallons of fuel each year. These pressing issues necessitate innovative and integrated solutions that can enhance the overall performance of the road. This study investigates the integration of Emerging Computing Technologies (ECT) into smart road infrastructures as a potential response… More >

Yi Pan, Mingshen Wang, Ye Xue, Huiyu Miao, Kemin Dai, Xiaodong Yuan^*, Fei Zeng

Energy Engineering, Vol.123, No.10, 2026, DOI:10.32604/ee.2025.074882 - 30 August 2026

Abstract Electric vehicles (EVs), characterized by their large-scale deployment and flexible charging–discharging scheduling, represent a growing form of transportation. However, their widespread adoption poses considerable challenges to the security and stability of the power grid during peak charging periods, highlighting the need for effective management of the coupled traffic–grid system. To address this issue, this paper proposes a blockchain-driven optimization model for charging scheduling in dynamic traffic networks. Blockchain technology is introduced to ensure data transparency and security in decentralized decision-making. First, a queuing model integrating the Bureau of Public Roads (BPR) function with the M/M/c/K… More > Graphic Abstract

Yan-Wei Li, David Chunhu Li^*

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.2, 2026, DOI:10.32604/cmes.2026.086216 - 28 August 2026

Abstract As urban transportation systems grow increasingly complex, accurate and interpretable traffic congestion forecasting is critical. While existing deep learning models utilize graph neural networks (GNNs) and attention mechanisms, they often struggle with physical consistency under extreme scenarios. To address this, we propose the Physics-Informed Explainable Spatial-Temporal Graph Attention Network (PI-X-STGAT). Our framework models road segments as graph nodes, integrating traffic, weather, and cyclical temporal features. The architecture comprises a Context-Aware Graph Attention Network (GAT) enhanced with Node Adaptive Parameter Learning (NAPL) for capturing dynamic spatial dependencies, a Gated Recurrent Unit (GRU) layer for temporal evolution,… More >

Hikmat Yar^1,2, Imran Ullah Khan³, Naqqash Dilshad⁴, Weiwei Jiang⁵, Heung Soo Kim^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.2, 2026, DOI:10.32604/cmes.2026.084202 - 28 August 2026

Abstract Transportation has become an essential component of modern daily life, with continuous advancements aimed at reducing travel time and improving mobility. However, this increased convenience has also contributed to a rise in road accidents, often caused by driver distraction, fatigue, and age-related cognitive decline. These concerns have driven growing interest in Artificial Intelligence (AI)-based real-time driver monitoring systems designed to enhance road safety. Despite recent progress, several challenges remain, including limitations in detection accuracy, inadequate temporal reasoning, and high computational complexity. To address these challenges, we utilize the EfficientNetV2-S model for backbone feature extraction due… More >

Waqas Ahmad¹, Shahzad Anwar², Abid Iqbal^3,*, Abuzar Khan⁴, Saad Arif⁵, Ali S. Alzahrani³, Mohammed Al-Naeem⁶, Fatimah Alhayan⁷, Syed Hashim Raza Bukhari³, Ghassan Husnain^4,*

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.1, 2026, DOI:10.32604/cmes.2026.083950 - 27 July 2026

Abstract Accurate vehicle localization is essential for safety-critical vehicular ad hoc networks (VANETs), including emergency response, navigation, traffic monitoring, and cooperative driving. However, conventional GPS/GNSS positioning systems have often shown degradation in tunnels, dense urban corridors, and non-line-of-sight (NLOS) environments, where satellite visibility and signal reliability are limited. This paper proposes a calibrated Link Quality Indicator (LQI)-based closed-form localization framework for partially connected Roadside Unit (RSU)-assisted VANETs. The proposed framework first calibrates the LQI-to-range relationship using numerical regression parameters and then converts accepted LQI observations into distance estimates. The distances are processed through a variance-aware weighted… More >

Mujahid Ali^*

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.084555 - 23 July 2026

Abstract Understanding the determinants of travel mode choice (TMC) in urban contexts is essential for effective transport planning and policy development. Past studies predominantly employed traditional discrete choice models because of their simplicity, diversity, and high interpretability; however, they rely on restrictive assumptions. Although machine learning (ML) techniques have shown promising predictive capabilities, comparative assessments of traditional and ML approaches, particularly considering hyperparameter optimisation, remain limited. This study addresses this gap by comparing a traditional model with four ML algorithms: decision tree (DT), random forest (RF), support vector machine (SVM), and k-nearest neighbour (KNN). In addition,… More >