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Journal of Advanced Transportation
Volume 2017 (2017), Article ID 1760842, 14 pages
Research Article

Cooperative Multiagent System for Parking Availability Prediction Based on Time Varying Dynamic Markov Chains

1Department of Mathematical Sciences, University of Zululand, Private Bag X1001, KwaDlangezwa 3886, South Africa
2Computational Science Program, College of Natural Science, Addis Ababa University, 1176 Addis Ababa, Ethiopia
3Centre Universitaire d’Informatique (CUI), University of Geneva, Battelle Batiment A, Rte de Drize 7, 1227 Carouge, Switzerland

Correspondence should be addressed to Surafel Luleseged Tilahun

Received 11 May 2017; Revised 1 August 2017; Accepted 22 August 2017; Published 28 September 2017

Academic Editor: Angel Ibeas

Copyright © 2017 Surafel Luleseged Tilahun and Giovanna Di Marzo Serugendo. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Traffic congestion is one of the main issues in the study of transportation planning and management. It creates different problems including environmental pollution and health problem and incurs a cost which is increasing through years. One-third of this congestion is created by cars searching for parking places. Drivers may be aware that parking places are fully occupied but will drive around hoping that a parking place may become vacant. Opportunistic services, involving learning, predicting, and exploiting Internet of Things scenarios, are able to adapt to dynamic unforeseen situations and have the potential to ease parking search issues. Hence, in this paper, a cooperative dynamic prediction mechanism between multiple agents for parking space availability in the neighborhood, integrating foreseen and unforeseen events and adapting for long-term changes, is proposed. An agent in each parking place will use a dynamic and time varying Markov chain to predict the parking availability and these agents will communicate to produce the parking availability prediction in the whole neighborhood. Furthermore, a learning approach is proposed where the system can adapt to different changes in the parking demand including long-term changes. Simulation results, using synthesized data based on an actual parking lot data from a shopping mall in Geneva, show that the proposed model is promising based on the learning accuracy with service adaptation and performance in different cases.