Disruptions in the operation of our countries’ infrastructure may put the functioning of our societies and their economies at risk. Such disruptions may result from many kinds of hazards and physical and/or cyberattacks on installations and systems. Recent events have demonstrated the increased interconnection among the impact of hazards, of the two kinds of attacks and, conversely, the usefulness for operators of combining cyber and physical security solutions to protect installations of the critical infrastructure globally. New ideas and innovation for comprehensive yet installation-specific approaches are necessary to secure the integrity of existing or future, public or private, connected and interdependent assets, installations, and infrastructure systems.
This Research Topic on “Data Science in Transportation and Transit Systems” enables transparent, fair, rapid communication of research that highlights the role of big data, data sciences, artificial intelligence, and engineering in multidisciplinary areas across materials science, physics, and engineering. Emphasis is on the impact, depth, and originality of new concepts, methods, and observations at the forefront of applied sciences and engineering technologies. This topic will help us to achieve our carbon neutrality roadmap by deploying specialist technology to create a digital twin of the infrastructure system to identify the optimal pathway to net zero, which be achieved by combining digital sensor and analytic technologies, artificial intelligence, environmental and energy footprints, and concepts that help change users’ behaviour to transform transportation and transit systems.
Among the topical areas of interest are the data science research into:
• Big Data
• Data Analytics, Visualisation
• Data modelling and assimilation
• Digital twins
• Artificial Intelligence
• Materials and Design
• Infrastructures
• Transport systems
• Railway systems
• Predictions
• Operations research
• Resilience, energy and carbon emissions
• Signal processing
• Applications of AI in practices
Disruptions in the operation of our countries’ infrastructure may put the functioning of our societies and their economies at risk. Such disruptions may result from many kinds of hazards and physical and/or cyberattacks on installations and systems. Recent events have demonstrated the increased interconnection among the impact of hazards, of the two kinds of attacks and, conversely, the usefulness for operators of combining cyber and physical security solutions to protect installations of the critical infrastructure globally. New ideas and innovation for comprehensive yet installation-specific approaches are necessary to secure the integrity of existing or future, public or private, connected and interdependent assets, installations, and infrastructure systems.
This Research Topic on “Data Science in Transportation and Transit Systems” enables transparent, fair, rapid communication of research that highlights the role of big data, data sciences, artificial intelligence, and engineering in multidisciplinary areas across materials science, physics, and engineering. Emphasis is on the impact, depth, and originality of new concepts, methods, and observations at the forefront of applied sciences and engineering technologies. This topic will help us to achieve our carbon neutrality roadmap by deploying specialist technology to create a digital twin of the infrastructure system to identify the optimal pathway to net zero, which be achieved by combining digital sensor and analytic technologies, artificial intelligence, environmental and energy footprints, and concepts that help change users’ behaviour to transform transportation and transit systems.
Among the topical areas of interest are the data science research into:
• Big Data
• Data Analytics, Visualisation
• Data modelling and assimilation
• Digital twins
• Artificial Intelligence
• Materials and Design
• Infrastructures
• Transport systems
• Railway systems
• Predictions
• Operations research
• Resilience, energy and carbon emissions
• Signal processing
• Applications of AI in practices
