Big Data Processing and Mining for the future ICT-based Smart Transportation Management System

Future Internet (FI) technologies can considerably enhance the effectiveness and user friendliness of present smart transportation management systems (STMS), providing considerable economic and social impact. Real-world application scenarios are needed to derive requirements for software architecture and smart functionalities of future-generation STMS in the context of the Internet of Things (IoT) and cloud technologies. The deployment of IoT technologies can provide future STMS with huge volumes of realtime data (Big Data) that need to be aggregated, communicated, analysed, and interpreted. In this study, we contend that future service- and cloud-based STMS can largely benefit from sophisticated data processing capabilities. Therefore, new distributed data mining and optimization techniques need to be developed and applied to support decision-making capabilities of future STMS. This study presents realworld scenarios of future STMS applications, and demonstrates the need for next-generation Big Data analysis and optimization strategies based on FI capabilities.

[1]  R Zito,et al.  A review of travel-time prediction in transport and logistica , 2005 .

[2]  W. Härdle.  Nonparametric and Semiparametric Models , 2004 .

[3]  Yong Zhao,et al.  Cloud Computing and Grid Computing 360-Degree Compared , 2008, GCE 2008.

[4]  Jan Fabian Ehmke,et al.  Decision Support for Dynamic City Traffic Management using Vehicular Communication , 2011, SIMULTECH.

[5]  Chengqi Zhang,et al.  Ubiquitous Intelligence in Agent Mining , 2009, ADMI.

[6]  Domenico Talia,et al.  Cloud Computing and Software Agents: Towards Cloud Intelligent Services , 2011, WOA.

[7]  Giovanni Malnati,et al.  Gossip: Estimating Actual Travelling Time Using Vehicle to Vehicle Communication , 2007 .

[8]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[9]  Jörg P. Müller,et al.  Mining the Traffic Cloud: Data Analysis and Optimization Strategies for Cloud-Based Cooperative Mobility Management , 2013 .

[10]  Maksims Fiosins,et al.  Density-Based Clustering in Cloud-Oriented Collaborative Multi-Agent Systems , 2013, HAIS.

[11]  Jörg P. Müller,et al.  Decentralised Cooperative Agent-Based Clustering in Intelligent Traffic Clouds , 2013, MATES.

[12]  Pericles A. Mitkas,et al.  Agent intelligence through data mining , 2006, Multiagent systems, artificial societies, and simulated organizations.

[13]  Jae-Gil Lee,et al.  Trajectory clustering: a partition-and-group framework , 2007, SIGMOD '07.

[14]  Ana L. C. Bazzan,et al.  A review on agent-based technology for traffic and transportation , 2013, The Knowledge Engineering Review.

[15]  Maksims Fiosins,et al.  Cooperative Kernel-Based Forecasting in Decentralized Multi-Agent Systems for Urban Traffic Networks , 2012 .

[16]  Jörg P. Müller,et al.  Reconciling strategic and tactical decision making in agent-oriented simulation of vehicles in urban traffic , 2011, SimuTools.

[17]  Jörg P. Müller,et al.  Agent-Based Integrated Decision Making for Autonomous Vehicles in Urban Traffic , 2011, PAAMS.

[18]  Samuel Madden,et al.  Distributed regression: an efficient framework for modeling sensor network data , 2004, Third International Symposium on Information Processing in Sensor Networks, 2004. IPSN 2004.

[19]  J. Manyika.  Big data: The next frontier for innovation, competition, and productivity , 2011 .

[20]  Dr. Alex A. Freitas.  Data Mining and Knowledge Discovery with Evolutionary Algorithms , 2002, Natural Computing Series.

[21]  Frances M. T. Brazier,et al.  A method for decentralized clustering in large multi-agent systems , 2003, AAMAS '03.

[22]  Zili Zhang,et al.  Agents and Data Mining: Mutual Enhancement by Integration , 2005, AIS-ADM.

[23]  N. Draper,et al.  Applied Regression Analysis. , 1967 .

[24]  Matthias Klusch,et al.  Agent-Based Distributed Data Mining: The KDEC Scheme , 2003, AgentLink.

[25]  Zhengxia Wang,et al.  Internet of Things: a New Application for Intelligent Traffic Monitoring System , 2011, J. Networks.

[26]  N. Draper,et al.  Applied Regression Analysis , 1966 .

[27]  Tom Holvoet,et al.  Ad hoc link traversal time prediction , 2011, 2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC).

[28]  Isabelle Guyon,et al.  A Stability Based Method for Discovering Structure in Clustered Data , 2001, Pacific Symposium on Biocomputing.

[29]  Benyoucef Othmane,et al.  Cloud computing & multi-agent systems: A new promising approach for distributed data mining , 2012, Proceedings of the ITI 2012 34th International Conference on Information Technology Interfaces.

[30]  Kai Wang,et al.  Cloud Computing for Agent-Based Urban Transportation Systems , 2011, IEEE Intelligent Systems.

[31]  Maksims Fiosins,et al.  Resampling-Based Change Point Estimation , 2011, IDA.

[32]  W. Weijermars,et al.  Analyzing highway flow patterns using cluster analysis , 2005, Proceedings. 2005 IEEE Intelligent Transportation Systems, 2005..

[33]  Billy M. Williams,et al.  Comparison of parametric and nonparametric models for traffic flow forecasting , 2002 .

[34]  Tinghuai Ma,et al.  Real time services for future cloud computing enabled vehicle networks , 2011, 2011 International Conference on Wireless Communications and Signal Processing (WCSP).

[35]  Matthias Klusch,et al.  Distributed data mining and agents , 2005, Eng. Appl. Artif. Intell..

[36]  Alexander Hinneburg,et al.  DENCLUE 2.0: Fast Clustering Based on Kernel Density Estimation , 2007, IDA.

[37]  Jelena Fiosina.  Decentralised Regression Model for Intelligent Forecasting in Multi-agent Traffic Networks , 2012, DCAI.

[38]  Maksims Fiosins,et al.  Selecting the Shortest Itinerary in a Cloud-Based Distributed Mobility Network , 2013, DCAI.

[39]  Fei-Yue Wang,et al.  Toward a Revolution in Transportation Operations: AI for Complex Systems , 2008, IEEE Intelligent Systems.

[40]  Rosaldo J. F. Rossetti,et al.  Ambient-centred intelligent traffic control and management , 2010, 13th International IEEE Conference on Intelligent Transportation Systems.

[41]  Jörg P. Müller,et al.  Change Point Analysis for Intelligent Agents in City Traffic , 2011, ADMI.

[42]  Philip K. Chan,et al.  Advances in Distributed and Parallel Knowledge Discovery , 2000 .

[43]  Shian-Shyong Tseng,et al.  Collaborative real-time traffic information generation and sharing framework for the intelligent transportation system , 2010, Inf. Sci..

[44]  Milos S. Stankovic,et al.  Decentralized Parameter Estimation by Consensus Based Stochastic Approximation , 2011, IEEE Trans. Autom. Control..

[45]  Pericles A. Mitkas,et al.  Agent Intelligence Through Data Mining (Multiagent Systems, Artificial Societies, and Simulated Organizations) , 2005 .