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 >

Kuldashbay Avazov¹, Kudratjon Zohirov², Alpamis Kutlimuratov³, Charos Khidirova⁴, Jasur Sevinov^5,6, Urishev Omadjon⁷, Adilbek Dauletov⁸, Akmalbek Abdusalomov^4,5,9, Young Im Cho^1,*

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.083828 - 23 July 2026

Abstract Unmanned aerial vehicles (UAVs) are becoming a common solution to urban mobility, and traffic monitoring as well, owing to their ability to be deployed flexibly, ability to see a broader area and real-time sensing. However, the reliability of UAV-assisted traffic systems can be compromised through identity spoofing, Sybil attacks, false data injection, and trajectory manipulation. Current authentication techniques primarily verify cryptographic identities but often cannot detect when a claimed identity is inconsistent with physical movement patterns and settings. To overcome this drawback, this paper presents a context-aware identity validation system, CIV-UAV, for UAV-based urban traffic… More >

Olzhas Olzhayev¹, Bakhytzhan Kulambayev^2,*, Azizah Suliman³

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.082618 - 23 July 2026

Abstract Automated road damage detection is a critical component of intelligent transportation systems, enabling efficient infrastructure maintenance and improved traffic safety. However, existing approaches often suffer from limited contextual understanding, insufficient segmentation accuracy, and suboptimal real-time performance. This study presents TCR-RoadNet, a transformer-enhanced multi-task deep learning architecture designed for simultaneous road damage detection and segmentation in real-world driving environments. The proposed framework integrates a multi-scale convolutional backbone with a Transformer Context Refinement (TCR) module to capture both fine-grained structural details and long-range spatial dependencies across feature scales. To further enhance performance, a Decoupled Detection Head (DDH)… More >

Jian Su^1,2,*, Jiaqi Wang²

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.082531 - 15 June 2026

Abstract Despite significant advances in object detection technology, vulnerable pedestrian detection in intelligent transportation systems remains highly challenging under complex weather conditions. Environmental factors such as fog, rain, and snow often lead to occlusion, motion blur, and low-contrast images, making small-scale or weak-featured vulnerable pedestrians difficult to accurately identify. Therefore, improving the detection accuracy and robustness of vulnerable pedestrians in complex weather scenarios has become an urgent research problem. To address this issue, this paper proposes an improved YOLOv8-based vulnerable pedestrian complex weather detection algorithm, termed VPCW-YOLO. The proposed method enhances detection performance through multiple structural… More >

Akmalbek Abdusalomov¹, Kudratjon Zohirov², Sojida Ochilova², Jakhongir Oramov³, Zafar Ruziyev³, Malika Rustamova⁴, Gulrukh Sherboboyeva⁵, Komil Tashev^6,7, Young Im Cho^1,*

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.081292 - 15 June 2026

Abstract Unmanned aerial vehicles (UAVs) are also increasingly becoming more often in the transportation infrastructure of smart cities, so that they can successfully achieve real-time observation of traffic, emergency coordination, and two-way communication relaying. However, the security and privacy risks arising in open, highly mobile intelligent transportation systems (ITS) enabled by UAVs are critical, as they pose threats of impersonation, replay, Sybil, and tracking attacks. Secondly, standard static authentication mechanisms are unable to support dynamic risk environments and excessive resource consumption on UAV platforms with limited capacity. To address these challenges, this study introduces a Generative-AI-assisted… More >

Pangwei Wang^1,*, Jie Wang¹, Zipeng Wang¹, Hangrui Dong², Li Wang¹

CMC-Computers, Materials & Continua, Vol.88, No.2, 2026, DOI:10.32604/cmc.2026.080815 - 15 June 2026

Abstract Traffic holographic perception refers to the real-time, high-fidelity, and multi-dimensional sensing of traffic states through the fusion of heterogeneous sensors, including cameras, radars, and connected vehicle data. The multi-source perception data obtained thereby can provide a complete digital representation of the road network for the Intelligent Transportation System (ITS). However, sensors are vulnerable to environmental interference, which can result in data loss at specific points or along arterial highways for certain periods, potentially undermining system safety and decision-making reliability. To address these challenges, a deep learning method based on Graph Convolutional Networks (GCN) and Gated… More >