Abstract
Future Internet (FI) technologies can considerably enhance the effectiveness and user friendliness of present cooperative mobility management systems (CMMS), providing considerable economical and social impact. Real-world application scenarios are needed to derive requirements for software architecture and smart functionalities of future-generation CMMS in the context of the Internet of Things (IoT) and cloud technologies. The deployment of IoT technologies can provide future CMMS with huge volumes of real-time data that need to be aggregated, communicated, analysed, and interpreted. In this study, we contend that future service- and cloud-based CMMS 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 CMMS. This study presents real-world scenarios of future CMMS applications, and demonstrates the need for next-generation data analysis and optimization strategies based on FI capabilities.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
7-th european framework programme project, instant mobility: Multimodality for people and goods in urban area, cp 284806, http://instant-mobility.com/
Fiosina, J.: Decentralised regression model for intelligent forecasting in multi-agent traffic networks. In: Omatu, S., Paz Santana, J.F., González, S.R., Molina, J.M., Bernardos, A.M., Rodríguez, J.M.C. (eds.) Distributed Computing and Artificial Intelligence. AISC, vol. 151, pp. 255–264. Springer, Heidelberg (2012)
Fiosina, J., Fiosins, M.: Distributed cooperative kernel-based forecasting in decentralized multi-agent systems for urban traffic networks. In: Proc. of Ubiquitous Data Mining (UDM) Workshop of ECAI 2012, Montpellier, France, pp. 3–7 (2012)
Fiosins, M., Fiosina, J., Müller, J.P.: Change point analysis for intelligent agents in city traffic. In: Cao, L., Bazzan, A.L.C., Symeonidis, A.L., Gorodetsky, V.I., Weiss, G., Yu, P.S. (eds.) ADMI 2011. LNCS, vol. 7103, pp. 195–210. Springer, Heidelberg (2012)
Fiosins, M., Fiosina, J., Müller, J.P., Görmer, J.: Reconciling strategic and tactical decision making in agent-oriented simulation of vehicles in urban traffic. In: Proc. of 4th International ICST Conference on Simulation Tools and Techniques, SimuTools 2011 (2011)
Foster, I.: Cloud computing and grid computing 360-degree compared. In: Proc. of the Grid Computing Environments Workshop, pp. 1–10 (2008)
Li, Z., Chen, C., Wang, K.: Cloud computing for agent-based urban transportation systems. IEEE Intelligent Systems 26(1), 73–79 (2011)
Passos, L., Rossetti, R., Oliveira, E.: Ambient-centred intelligent traffic control and management. In: Proc. of the 13th Int. IEEE Annual Conf. on ITS, pp. 224–229 (2010)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer International Publishing Switzerland
About this paper
Cite this paper
Fiosina, J., Fiosins, M., Müller, J.P. (2013). Mining the Traffic Cloud: Data Analysis and Optimization Strategies for Cloud-Based Cooperative Mobility Management. In: Casillas, J., Martínez-López, F., Vicari, R., De la Prieta, F. (eds) Management Intelligent Systems. Advances in Intelligent Systems and Computing, vol 220. Springer, Heidelberg. https://doi.org/10.1007/978-3-319-00569-0_4
Download citation
DOI: https://doi.org/10.1007/978-3-319-00569-0_4
Publisher Name: Springer, Heidelberg
Print ISBN: 978-3-319-00568-3
Online ISBN: 978-3-319-00569-0
eBook Packages: EngineeringEngineering (R0)