Transportation News and Research

RSS

AI-Driven Framework for Real-Time Crash Risk Forecasting at Intersections

Researchers present a cutting-edge framework for real-time crash risk estimation and prediction at signalized intersections, leveraging artificial intelligence and traffic conflict data. By integrating a non-stationary generalized extreme value model and a recurrent neural network, the framework offers proactive insights for safety management and countermeasure implementation, demonstrating high accuracy and potential for real-world applications.

29 Feb 2024

Hierarchical Federated Learning for Smart City AIoT Systems

Researchers introduce a hierarchical federated learning framework tailored for large-scale AIoT systems in smart cities. By integrating cloud, edge, and fog computing layers and leveraging the MQTT protocol, the framework addresses data privacy and communication latency challenges, demonstrating enhanced scalability and efficiency. Experimental validation in Docker environments confirms the framework's feasibility and performance improvements, laying the foundation for future optimizations.

29 Feb 2024

Optimizing Content Caching in Vehicular Networks: A Gale–Shapley Approach

Researchers proposed a novel content caching scheme for fog nodes in 5G-enabled vehicular networks, utilizing a modified Gale–Shapley algorithm to optimize content placement based on the preferences of content providers and vehicles. Their approach effectively reduced content downloading time, improved quality of service, and ensured fair and efficient content placement by considering factors such as content popularity, vehicle connectivity, and channel quality.

21 Feb 2024

Optimizing Electric Vehicle Power: A Novel Energy Management Strategy

Researchers present an innovative energy management strategy (EMS) for electric vehicles (EVs), combining slap swarm optimization and differential flatness control to enhance performance and reliability. The proposed EMS efficiently coordinates power flow among battery, supercapacitor, and motor components, stabilizing voltage, reducing harmonics, and meeting power demand, promising improved EV efficiency and driving range while reducing greenhouse gas emissions.

18 Feb 2024

Low-Carbon Transformation in Resource-Based Cities by Integrating ChatGPT and ABC Algorithms

Researchers propose a novel approach utilizing ChatGPT and artificial bee colony (ABC) algorithms to advance low-carbon transformation in resource-based cities. Their study demonstrates significant improvements in energy efficiency, carbon emissions reduction, and traffic congestion alleviation, highlighting the potential of these methods in promoting green development and sustainable urban planning.

15 Feb 2024

Advancing Multi-Vehicle Dial-a-Ride Problems with Enhanced Deterministic Annealing Algorithm

This paper introduces an innovative Linearly Decreasing Deterministic Annealing (LD-DA) algorithm tailored for large-scale Multi-Vehicle Dial-a-Ride Problems (DARPs). By incorporating advanced local search techniques and a linearly decreasing annealing schedule, LD-DA ensures efficient exploration of feasible solution spaces, yielding competitive solutions at accelerated convergence rates. Extensive computational experiments demonstrate LD-DA's superiority over existing algorithms, highlighting its potential for real-world applications in transportation optimization.

15 Feb 2024

Improved Analysis of Highway Construction Safety Incidents Using AI

This study in the journal Applied Sciences utilizes large language models (LLMs) and artificial intelligence (AI) to analyze textual narratives from the Occupational Safety and Health Administration (OSHA) severe injury reports (SIR) database related to highway construction accidents. By employing LLMs such as GPT-3.5, along with natural language processing (NLP) techniques and clustering algorithms, the researchers identified major accident causes and types, providing valuable insights for improving accident prevention and intervention strategies in the industry.

14 Feb 2024

AI-Driven Virtual Rehabilitation: Revolutionizing Home-Based Care

"npj Digital Medicine" presents a scoping review on AI applications in home-based virtual rehabilitation (VRehab), showing its effectiveness in stroke, cardiac, and orthopedic rehabilitation. AI-driven VRehab offers personalized feedback, enhances patient outcomes, and overcomes barriers to traditional rehabilitation, heralding a new era in accessible and efficient healthcare delivery. Further research is needed to standardize evaluation methods and ensure privacy while maximizing the potential of AI in personalized rehabilitation programs.

12 Feb 2024

STA-LSTM: Enhancing Vehicle Trajectory Prediction in Connected Environments

Scientific Reports presents the STA-LSTM model, integrating spatial-temporal attention mechanisms for precise vehicle trajectory prediction in connected environments. Outperforming baseline models, STA-LSTM accurately captures dynamic interactions and uncertainty, offering multi-modal predictions crucial for collision avoidance and traffic optimization in intelligent transportation systems and autonomous driving scenarios. Future enhancements could address complex scenarios like intersections and integrate additional factors for comprehensive predictive capabilities.

12 Feb 2024

MaTE: Macroscopic Traffic Estimator for Enhanced Traffic Flow and Travel Time Prediction

MaTE, a novel traffic estimator, utilizes macroscopic flow theory, logit-based stochastic traffic assignment, and neural networks to accurately predict traffic flow and travel time in areas with limited sensor coverage. Demonstrating superiority over existing models, MaTE offers interpretability and scalability, bridging the gap between data-driven and model-based methodologies in transportation planning.

7 Feb 2024

Innovative Vision Transformer for Pothole and Traffic Sign Detection in Challenging Conditions

Researchers from India, Australia, and Hungary introduce a robust model employing a cascade classifier and a vision transformer to detect potholes and traffic signs in challenging conditions on Indian roads. The algorithm, showcasing impressive accuracy and outperforming existing methods, holds promise for improving road safety, infrastructure maintenance, and integration with intelligent transport systems and autonomous vehicles

2 Feb 2024

A Federated Learning Approach for Passenger Demand Forecasting in Smart Cities

Researchers present a groundbreaking Federated Learning (FL) model for passenger demand forecasting in Smart Cities, focusing on the context of Autonomous Taxis (ATs). The FL approach ensures data privacy by allowing ATs in different regions to collaboratively enhance their demand forecasting models without directly sharing sensitive passenger information. The proposed model outperforms traditional methods, showcasing superior accuracy while addressing privacy concerns in the era of smart and autonomous transportation systems.

1 Feb 2024

Virtual Reality-based Safety Skills Training for Individuals in the Autism Spectrum

This review explores the impactful use of Virtual Reality (VR) technology in safety skills interventions for individuals with Autism Spectrum Disorder (ASD). Highlighting the effectiveness of VR training in diverse scenarios, the study underscores the need for personalized interventions and adaptive models, advocating cautious application in younger patients and calling for further research into tailored approaches for enhanced safety skill acquisition in the ASD population.

26 Jan 2024

Intelligent Systems and Machine Learning for Traffic Prediction on Suburban Roads

Researchers employ advanced intelligent systems to analyze extensive traffic data on northern Iranian suburban roads, revolutionizing traffic state prediction. By integrating principal component analysis, genetic algorithms, and cyclic features, coupled with machine learning models like LSTM and SVM, the study achieves a significant boost in prediction accuracy and efficiency, offering valuable insights for optimizing transportation management and paving the way for advancements in traffic prediction methodologies.

24 Jan 2024

Enhancing Railroad Safety: RailFOD23 Dataset for Foreign Object Detection

Scientists present a pioneering approach to address the scarcity of datasets for foreign object detection on railroad power transmission lines. The article introduces the RailFOD23 dataset, comprising 14,615 images synthesized through a combination of manual and AI-based methods, providing a valuable resource for developing and benchmarking artificial intelligence models in the critical domain of railway safety.

23 Jan 2024

Revolutionizing Traffic Safety: MTGAE Framework for Anomaly Detection

A groundbreaking study unveils the Mirror Temporal Graph Autoencoder (MTGAE), a novel framework for traffic anomaly detection in intelligent transportation. Through advanced modules like the Mirror Temporal Convolutional Module (MTCM) and Graph Convolutional Gate Recurrent Unit (GCGRU), MTGAE outshines existing models, offering superior adaptability and performance in real-world traffic scenarios, marking a significant leap in intelligent transportation system technology.

22 Jan 2024

AVOCA Algorithm Enhances Efficiency in Internet of Vehicles

Researchers introduce the African Vulture Optimization-based Clustering Algorithm (AVOCA) to revolutionize the integration of the automotive industry with the Internet of Things (IoT) through the Internet of Vehicles (IoV). Inspired by African vultures' migratory behavior, AVOCA outperforms existing algorithms in optimizing vehicular ad-hoc networks (VANETs), showcasing superior efficiency and scalability across diverse network scenarios. This innovative approach holds the potential to address challenges in intelligent transportation systems, contributing to the evolution of IoV and shaping the future of vehicular communication.

19 Jan 2024

LLMs Illuminate Social Determinants of Health in Clinical Narratives

Researchers from the USA leverage Large Language Models (LLMs) to automatically extract social determinants of health (SDoH) from clinical narratives, addressing challenges in healthcare data. Their innovative approach, combining Flan-T5 models and synthetic data augmentation, showcases remarkable efficiency, emphasizing the potential to bridge gaps in understanding and addressing crucial factors influencing patients' well-being.

15 Jan 2024

GNN-Based Edge Ranking for Rapid Post-Disaster Recovery

This work presents a novel Graph Neural Network (GNN) method for swiftly identifying critical road segments post-disaster, aiding efficient recovery and resilience planning. Overcoming computational challenges, the GNN-based edge ranking framework proves effective in large-scale networks, offering accuracy and adaptability. This approach showcases versatility, enabling real-time analysis and facilitating proactive measures for reinforcing critical infrastructure against future disruptions.

4 Jan 2024

Machine Learning-Based Pedestrian Crossing Decision Models to Increase Pedestrian Safety

Researchers focus on improving pedestrian safety within intelligent cities using AI, specifically support vector machine (SVM). Leveraging machine learning and authentic pedestrian behavior data, the SVM model outperforms others in predicting crossing probabilities and speeds, demonstrating its potential for enhancing road traffic safety and integrating with intelligent traffic simulations. The study emphasizes the significance of SVM in accurately predicting real-time pedestrian behaviors, contributing to refined decision models for safer road designs.

3 Jan 2024