Advancements in Traffic Safety: Data-Driven Insights and Emerging Technologies

Traffic safety remains one of the most pressing challenges worldwide, necessitating innovative research to effectively reduce road accidents and conflicts while simultaneously enhancing traffic mobility. As urbanization intensifies and transportation technologies advance at an unprecedented rate, leveraging data-driven approaches becomes crucial for crafting evidence-based safety strategies.

The intersection of Artificial Intelligence (AI), big data analytics, and Intelligent Transportation Systems (ITS) presents transformative opportunities to improve road safety across varied traffic environments, especially under mixed traffic conditions. This topic addresses the critical need for advancements in traffic safety research, which is corroborated by the United Nations Sustainable Development Goals (SDGs), particularly Goal 3.6. This goal ambitiously aims to halve the number of global road traffic fatalities by 2030. Moreover, Agenda 2030 emphasizes the importance of developing sustainable and resilient infrastructure, which supports the creation of safer mobility solutions.

Achieving these goals relies on interdisciplinary contributions that harness advancements in computational technologies, insights from human factors research, and other emerging technological innovations to construct safer and smarter road networks. This research topic encourages contributions that focus on proactive safety interventions and real-time risk assessment, aiming to bring about meaningful improvements in transportation planning and policy-making.

We invite researchers and practitioners to explore a variety of themes related to this topic, including but not limited to:

o The role of AI and machine learning in predictive traffic safety analysis.
o Applications of big data analytics for real-time traffic monitoring and decision-making.
o The impact of ITS on reducing road conflicts and enhancing mobility.
o Exploration of human factors and their integration with technology-driven solutions.
o Case studies demonstrating successful implementations of data-driven safety strategies.

By prioritizing innovative, scalable solutions, this special issue endeavors to catalyze impactful changes in the realm of transportation and transit systems, ultimately contributing to safer roads worldwide.
For submission guidelines and deadlines, please refer to Frontiers in Built Environment for more details.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Editorial
• FAIR² Data
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Review

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Editorial
• FAIR² Data
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Review

Keywords: Traffic Safety, AI and Machine Learning, Big Data Analytics, Driving Behavior, Intelligent Transportation Systems (ITS), Vision Zero, Autonomous Vehicles, Mixed Traffic Conditions

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.