Scientific article
Open access

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

Published inJournal of Advanced Transportation, vol. 2017, no. 1760842
Publication date2017

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.

  • Smart services
  • Traffic prediction
  • Dynamic markov chains
Citation (ISO format)
SURAFEL LULESEGED, Tilahun, DI MARZO SERUGENDO, Giovanna. Cooperative Multiagent System for Parking Availability Prediction Based on Time Varying Dynamic Markov Chains. In: Journal of Advanced Transportation, 2017, vol. 2017, n° 1760842.
Main files (1)
Article (Published version)
  • PID : unige:97173
ISSN of the journal0197-6729

Technical informations

Creation09/28/2017 1:50:00 PM
First validation09/28/2017 1:50:00 PM
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