Management of Urban Parking: An Agent-Based Approach

Bessghaier, Nesrine; Zargayouna, Mahdi; Balbo, Flavien

doi:10.1007/978-3-642-33185-5_31

Cited by 21 publications

(15 citation statements)

References 9 publications

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“…As previously mentioned, multi-hop can increase information popularity. Bessghaier et al [151] studied a full distributed inter-vehicular network to see if parking search time decreases, compared to current blind search. edPAS [152] stands for event-driven parking allocation system.…”

Section: A Information Disseminationmentioning

confidence: 99%

A Survey of Smart Parking Solutions

Lin

Rivano

Mouël

2017

IEEE Trans. Intell. Transport. Syst.

430

185

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Section: A Information Disseminationmentioning

confidence: 99%

A Survey of Smart Parking Solutions

Lin

Rivano

Mouël

2017

IEEE Trans. Intell. Transport. Syst.

430

185

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“…This solution uses minimal information on shared, volatile and uncontrollable resources. The multiagent system works without initial information, without infrastructure to collect information about spots availability, and without a central information system (Bessghaier et al, 2012a).…”

Section: Multiagent Solutionmentioning

confidence: 99%

Generic model for resource allocation in transportation. Application to urban parking management

Zargayouna

Balbo

Ndiaye

2016

Transportation Research Part C: Emerging Technologies

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In this paper, we define the online localized resource allocation problem, especially relevant for modeling transportation applications. The problem modeling takes into account simultaneously the geographical location of consumers and resources together with their online nondeterministic appearance. We use urban parking management as an illustration of this problem. In fact, urban parking management is an online localized resource allocation problem, where the question is how to find an efficient allocation of parking spots to drivers, while they all have dynamic geographical positions and appear nondeterministically. We define this problem and propose a multiagent system to solve it. The objective of the system is to decrease, for private vehicles drivers, the parking spots search time. The drivers are organized in communities and share information about spots availability. We have defined two cooperative models and compared them: a fully cooperative model, where agents share all the available information, and a "coopetitive" model, where drivers do not share information about the spot that they have chosen. Results show the superiority of the first model 1 .

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“…The example is inspired by [4,5], where an agent-based transport information system is considered in order to improve the parking spot problem. That is, having an agent represent each car, they can communicate and coordinate to find an available parking spot nearby.…”

Section: Motivationmentioning

confidence: 99%

Belief Revision in the GOAL Agent Programming Language

Spurkeland

Jensen²,

Villadsen³

2013

ISRN Artificial Intelligence

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Agents in a multiagent system may in many cases find themselves in situations where inconsistencies arise. In order to properly deal with these, a good belief revision procedure is required. This paper illustrates the usefulness of such a procedure: a certain belief revision algorithm is considered in order to deal with inconsistencies and, particularly, the issue of inconsistencies, and belief revision is examined in relation to the GOAL agent programming language.

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Management of Urban Parking: An Agent-Based Approach

Cited by 21 publications

References 9 publications

A Survey of Smart Parking Solutions

A Survey of Smart Parking Solutions

Generic model for resource allocation in transportation. Application to urban parking management

Belief Revision in the GOAL Agent Programming Language

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