About this Research Topic
Transportation and security are two domains that face many challenges and opportunities in the modern world. The increasing population, urbanization, globalization and digitalization have created complex and dynamic problems that require innovative and effective solutions. Data science, which encompasses a wide range of techniques such as statistics, machine learning, deep learning, computer vision, natural language processing, etc., can provide powerful tools to analyze, understand and utilize large and complex data sets that are generated in these domains. Data science can help optimize the performance and efficiency of transportation and security systems, as well as enhance the safety and well-being of the users and society.
This Research Topic aims to showcase the latest research and innovations in data science that can address real-world problems in transportation, security and beyond. Some examples of such problems are:
Road traffic mobility: how to optimize traffic flow, reduce congestion, improve safety, predict travel demand, etc.
Fraud and anomaly detection: how to identify and prevent fraudulent or malicious activities, such as credit card fraud, cyberattacks, money laundering, etc.
General statistics and machine learning: how to develop and evaluate novel methods and algorithms for data analysis, inference, prediction, classification, clustering, etc.
The Research Topic welcomes papers that present novel methods, models, frameworks, systems or applications that use data science techniques for solving real-world problems in transportation, security and beyond. The papers should also discuss the benefits, limitations and challenges of the proposed approaches. The Research Topic encourages interdisciplinary and cross-domain collaborations among researchers from different fields and backgrounds.
This Research Topic aims to showcase the latest research and innovations in data science that can address real-world problems in transportation, security and beyond. Some examples of such problems are:
Road traffic mobility: how to optimize traffic flow, reduce congestion, improve safety, predict travel demand, etc.
Fraud and anomaly detection: how to identify and prevent fraudulent or malicious activities, such as credit card fraud, cyberattacks, money laundering, etc.
General statistics and machine learning: how to develop and evaluate novel methods and algorithms for data analysis, inference, prediction, classification, clustering, etc.
The Research Topic welcomes papers that present novel methods, models, frameworks, systems or applications that use data science techniques for solving real-world problems in transportation, security and beyond. The papers should also discuss the benefits, limitations and challenges of the proposed approaches. The Research Topic encourages interdisciplinary and cross-domain collaborations among researchers from different fields and backgrounds.
Keywords: Transportation, Security, Optimization, Fraud Detection, Anomaly Detection, Data Science, Machine Learning, Deep Learning, Efficiency, Safety
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.
