ITSBD 2016 - 2016 International workshop on Intelligent Transportation Systems and Big Data

All modes of transportation are now generating unprecedented amounts of data. While cargo and people are being transported across air, sea and land, a multitude of sensors are reporting on their constantly changing state. These firehoses of data, hold key knowledge for deciphering the complexity of transport, which amongst others includes capturing methods of optimizing supply chains, understanding fluctuations in demand, reducing emissions and improving safety and efficiency of operations. Unfortunately, current state of the art techniques and technologies are incapable of dealing with these growing volumes of high-speed, loosely structured, spatiotemporal data streams that require real-time analysis in order to produce actionable intelligence. It is a general belief that we currently lack infrastructures capable of storing, analyzing and correlating big data in a holistic way and under (even soft) real-time constraints. Extracting knowledge from diverse data sources requires the development of innovative algorithms, services and architectures capable of fusing and ingesting data at such volume, velocity and variety.
Intelligent solutions are in demand which exhibit the characteristics of autonomic and intelligent big data mining, capable of reducing data dimensionality and resolving the complexity of the problem state in an automatic or semi automatic way. A new dimension of possible services is revealing based on the innovative dynamics and perspectives of machine learning and automation of knowledge generation and exploitation. Collaborative research is necessary at the intersection of transport and the emerging Information and Communication Technology. This workshop invites research communities from a diverse set of scientific areas such as artificial intelligence, evolving and intelligent systems, big data, cloud computing, information fusion and distributed systems to publish their work and share opinions regarding real world applications, challenges and viable solutions to the potential new generation services emerging from the wealth of transportation data available today.
Topics
Today more than ever, collaborative research is necessary at the intersection of the transportation domain and Information and Communication Technology. This workshop invites research communities from a diverse set of scientific areas such as artificial intelligence, evolving and intelligent systems, big data, cloud computing, information fusion and distributed systems to publish their work and share opinions regarding real world applications, challenges and viable solutions to the potential new generation services emerging from the wealth of transportation data available today.
The topics of this workshop revolve around two interrelated themes,
Real world applications and case studies of data driven intelligent transportation systems
Algorithms and Architectures for data driven intellegent transportation systems
Real world Data Driven transportation applications and architectures
I. Real world applications and systems deployed to solve intelligently big data issues in the transportation domain. This workshops invites papers describing case studies from all areas of transportation which benefit from big data processing including,
Smart Ports and Shipping
Smart Rail
Smart Freight Transportation
Smart Aviation
Including,
Data driven implementations of Autonomous transportation
Implementations of Intelligent Supply chains
Applications and deployments of cloud computing and distributed platforms in transport
Sensor networks and IOT implementations in transport
II. Algorithms & Methods
Intelligent algorithms for fusing, ingesting, learning and reducing the dimensionality of data in the transportation domain including
Deep learning architectures
Compression and dimensionality reduction
Efficient learning and clustering at scale
Time series prediction algorithms
Statistical models
Real-time forecasting
Approaches of traffic simulation
Prediction of chaotic time series
Evolutionary algorithms for time series prediction