Special Issue on “Evolving Systems for Smart Transport and Big Data Analytics”

1. Aims and Scope
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.
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. Data fusion combines multi origin information to determine relationships among data; thus improving the understanding of a current complex environment but also attempting to predict its future state. 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.
At this front, collaborative research is necessary at the intersection of transport and the emerging Information and Communication Technology domains including, information fusion research, artificial intelligence, intelligent systems, big data architectures, semantics, cloud computing and Internet of Things (IOT). This special issue invites papers demonstrating architectures, applications, and experiments reporting on the adoption of intelligent big data approaches in the transportation domain.

2. A Brief Outline of the Proposed Issue and Topics of Interest
Original and unpublished high-quality research results are solicited to explore various challenging topics, which include, but are not limited to:
Data Driven Real world applications
• Real world applications and implementation architectures deployed to solve intelligently big data issues in the transportation domain including, Smart Cities, Smart Vehicles, Smart Ports etc.
• Data driven implementations of Autonomous transportation
• Implementations of Intelligent Supply chains
• Cloud computing and distributed platforms in transport
Algorithms & Methods
• Intelligent algorithms for fusing, ingesting,learning and reducing the dimensionality of data in the transportation domain including
o Deep learning architectures
o Compression and dimensionality reduction
o Efficient learning and clustering at scale
o Time series prediction algorithms
o Adaptive Neuro Fuzzy Inference System
o Statistical models
o Real-time forecasting
o Approaches of traffic simulation
o Prediction of chaotic time series
o Evolutionary algorithms for time series prediction

3. Deadline Dates
Submission of manuscripts: February 1, 2016
First Revision Notification: March 1, 2016.
Submission of revision Papers: April 1, 2016.
Final Acceptance notification: April 15, 2016.
Final Acceptance/Rejection notice: April 30, 2016

4. Guest Editors

Dimitrios Zissis,
Dept. of Product & Systems Design Engineering, University of the Aegean
dzissis@aegean.gr

Elias Xidias
Dept. of Product & Systems Design Engineering, University of the Aegean
xidias@aegean.gr

Kostas Tserpes
Dept.of Informatics and Telematics, Harokopio University of Athens
tserpes@hua.gr

Dimosthenis Anagnostopoulos
Dept.of Informatics and Telematics, Harokopio University of Athens