Special issue on AI for Safety and Urban Emergency Resilience

Introduction:

Urban areas are increasingly exposed to complex and cascading risks, including fires, transport disruptions, infrastructure failures, public health emergencies, and extreme weather events. Nonethless, recent advances in artificial intelligence, such as large language models, knowledge graphs, digital twins, graph learning, and multimodal perception, are creating new opportunities for risk detection, prediction, decision support, and resilience enhancement.

This Special Issue aims to provide a focused platform for interdisciplinary studies at the intersection of AI, safety science, and urban emergency resilience, with emphasis on both methodological innovation and practical relevance.

The Special Issue will bring together high-quality original research and reviews on AI-enabled approaches for improving urban safety, emergency response, and resilience.

Topics include, but are not limited to:

• AI for hazard detection, monitoring, and early warning
• Intelligent evacuation, crowd safety, and emergency guidance
• Knowledge graphs and large language models for emergency decision support
• Digital twins for urban safety and resilience
• AI for critical infrastructure resilience and cascading risk analysis
• Multimodal sensing and data fusion for safety management
• Human-AI collaboration in emergency response
• Explainable and trustworthy AI in safety-critical systems

We expect this issue to attract:

• original research articles,
• review papers,
• perspective/discussion papers,

addressing the use of AI to enhance safety and resilience in urban contexts.

Important Deadlines:

Submission deadline: 31 March 2027

Submission Instructions:

Please read the [Guide for Authors] before submitting. All articles should be [submitted online], please select [SI: AI for Safety and Urban Emergency Resilience] on submission. If the manuscript is accepted, the article will be published in Open Access, and the costs will be paid by the author.

Guest Editors:

1. Dr.  Qing Deng, Research Institute of Macro-Safety Science, University of Science and Technology Beijing, Beijing 100083, China. 
2. Dr.  Saman Ghaffarian, Deprtment of Risk and Disaster Reduction (RDR), University College London (UCL), UK. 
3. Dr. Tiantian Zhu, Department of Hydraulic Engineering, Delft University of Technology, Delft, Netherlands. 
4. Dr. Feng Yu, School of International and Public Affairs, Shanghai Jiao Tong University, Shanghai, China. 

(Please pass this call for papers on to any of your personal contacts who might be interested in submitting a paper. All queries should be addressed to JSSR Editorial Office:  jssr@mail.sciencep.com)