Advanced Machine Learning and AI Techniques for Winter Weather Traffic Modelling in Cold Region Highways

Dear Colleagues,

This Special Issue aims to explore innovative applications of machine learning and artificial intelligence in modeling and predicting traffic patterns, safety, and operations under winter weather conditions in cold region highways. We invite contributions that address challenges and advancements in this area, including, but not limited to, the following:

• The development and implementation of predictive models for traffic flow and congestion under winter weather conditions.
• The use of AI for real-time traffic management and decision making during snowstorms and icy conditions.
• The integration of weather data and traffic data for the enhanced prediction and management of highway systems.
• Case studies and practical applications of machine learning in winter weather traffic scenarios.
• Evaluations of AI-based solutions for improving road safety and reducing accidents in cold regions.
• Comparative analysis of different machine learning approaches in modeling winter weather traffic.
• Policy implications and recommendations for the adoption of AI and machine learning in winter traffic management.

We welcome original research articles, review papers, case studies, and technical notes that contribute to the body of knowledge in this critical and evolving field.

A. Focus

The primary focus of this Special Issue is on leveraging advanced machine learning and artificial intelligence techniques to model and manage traffic on highways in cold regions during winter weather conditions. The issue seeks to highlight innovative approaches and practical applications that address the unique challenges posed by snow, ice, temperature, and other winter weather phenomena on traffic flow, safety, and highway operations.

B. Scope

This Special Issue encompasses a broad range of topics related to winter weather traffic modeling in cold regions. The scope includes, but is not limited to, the following:

1. Predictive Models: Development and evaluation of machine learning models for predicting traffic flow, congestion, and travel times under winter weather conditions.
2. Real-time Traffic Management: AI application for real-time traffic monitoring, incident detection, and decision making during adverse weather events.
3. Weather-Integrated Traffic Data: Methods for integrating meteorological data with traffic data to enhance the accuracy and reliability of traffic models.
4. Safety Enhancements: AI-driven solutions for improving road safety, reducing accidents, and mitigating the impact of winter weather on highway users.
5. Case Studies: Practical examples and success stories of machine learning and AI applications in winter weather traffic management and modeling.
6. Comparative Analysis: Comparative studies of different machine learning techniques and their effectiveness in modeling winter weather traffic scenarios.
7. Policy and Recommendations: Discussions on policy implications and recommendations for the adoption of AI and machine learning technologies in traffic management practices.

C. Purpose

The purpose of this Special Issue is threefold:

1. Knowledge Advancement: To advance the understanding of how machine learning and AI can be utilized to address the challenges of winter weather traffic management in cold regions. This Special Issue aims to compile state-of-the-art research and innovative solutions that can enhance traffic modeling, prediction, and safety during winter conditions.
2. Practical Application: To provide a platform for sharing practical applications and case studies that demonstrate the successful implementation of AI and machine learning in real-world scenarios. This can serve as a valuable resource for practitioners, researchers, and policymakers in the field of traffic management.
3. Collaboration and Networking: To foster collaboration among researchers, practitioners, and policymakers by providing a comprehensive collection of cutting-edge research and insights. This Special Issue aims to stimulate discussion and encourage the exchange of ideas and best practices for improving winter weather traffic management using advanced technologies.

By addressing these aims, this Special Issue seeks to contribute significantly to the field of traffic management and ensure safer, more efficient highway operations in cold regions during winter weather conditions.

Dr. Hyuk Jae Roh
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• winter weather traffic modeling
• cold region highways
• machine learning
• artificial intelligence
• predictive modeling
• traffic flow prediction
• real-time traffic management
• snowstorm traffic management
• icy road conditions
• weather-integrated traffic data
• traffic safety
• accident reduction
• AI-based traffic solutions
• comparative analysis of machine learning techniques
• winter traffic management policy
• smart transportation systems
• intelligent transportation systems (ITS)
• road safety in winter conditions
• big data in traffic management
• climate impact on traffic systems
• winter highway closure and reopen
• vulnerability index development with traffic reduction
• traffic reduction contour map development in network scale
• weather data augmentation under scarcity of snowfall and severe cold temperature