Using Negotiation for Parking Selection in Smart Cities

Napoli, Claudia Di; Nocera, Dario Di; Rossi, Silvia

doi:10.1007/978-3-319-07551-8_31

Cited by 11 publications

(9 citation statements)

References 5 publications

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“…The question (iv) falls into the class of VRP (pickup and delivery) with the additional constraints between a pickup and a delivery point, associated with a demand, an agent should park the customer car somewhere. The pricing problems (question (iii)) can make the service attractive or not, as well as considering forecasting mechanisms that act in the market with information of energy consumption, generation and price [94].…”

Section: Smart Parkingmentioning

confidence: 99%

“…Taking into account that an urban area is a complex system, counter-intuitive consequences may occur which cannot be viewed simply by solving some optimization problems. Reference [94] also dealt with a parking selection problem in an SC scenario, their work uses a multi-agent flexible negotiation mechanism to address the parking space by taking into account the car owner preferences in locations, parking vendors' preferences regarding car park occupancy and social city benefits.…”

Section: Smart Parkingmentioning

confidence: 99%

“…An initial attempt to develop a car parking locator service, within a university campus, was done by Ganchev et al [87], considering an info station based on MAS. In a subsequent work [94], an MAS flexible negotiation mechanism was developed, taking into account car owner parking vendors' preferences and social city benefits. In recent studies, developing adaptive traffic signal control [147] was done by reinforcement learning agents.…”

Section: Decentralization Via Multi Agent Systemsmentioning

confidence: 99%

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Mobility, Citizens, Innovation and Technology in Digital and Smart Cities

et al. 2020

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Cities are constantly transforming and, consequently, attracting efforts from researchers and opportunities to the industry. New transportation systems are being built in order to meet sustainability and efficiency criteria, as well as being adapted to the current possibilities. Moreover, citizens are becoming aware about the power and possibilities provided by the current generation of autonomous devices. In this sense, this paper presents and discusses state-of-the-art transportation technologies and systems, highlighting the advances that the concepts of Internet of Things and Value are providing. Decentralized technologies, such as blockchain, are been extensively investigated by the industry, however, its widespread adoption in cities is still desirable. Aligned with operations research opportunities, this paper identifies different points in which cities’ services could move to. This also study comments about different combinatorial optimization problems that might be useful and important for an efficient evolution of our cities. By considering different perspectives, didactic examples are presented with a main focus on motivating decision makers to balance citizens, investors and industry goals and wishes.

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Section: Smart Parkingmentioning

confidence: 99%

Section: Smart Parkingmentioning

confidence: 99%

Section: Decentralization Via Multi Agent Systemsmentioning

confidence: 99%

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Mobility, Citizens, Innovation and Technology in Digital and Smart Cities

et al. 2020

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“…Other works have attempted to take into account requirements on some parking attributes. Di Napoli et al [23] use a multi-agent system to address the parking space allocation as the result of an agreement between parking providers and parking requestors. The approach takes into account user requirements on a parking space in terms of its location and cost, and the vendor requirements in terms of income and city regulations to obtain an efficient parking allocation and traffic redirection.…”

Section: Previous Workmentioning

confidence: 99%

The “ReadyPark” Collaborative Parking Search Strategy

Collard¹,

Stattner²,

Gergos³

2021

Smart Cities

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Many of us have experienced this when driving in the city, we had to “turn for hours” to find a parking space. To attenuate this drawback, this paper proposes a collaborative parking search strategy based on the reality of a drivers network in which, depending on his/her role at a given time, each one can help the others to find public on-street parking spots. To highlight a situation of parking spot scarcity, we assume the extreme case where there is no free space and the only solution to park is to swap with a parked car that leaves its location. Under this assumption, agent-based modelling and simulations allow to compare the ReadyPark solution with the classical approach that consists, by luck, in taking the place of a nearby car that is leaving its spot.

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“…In [30] a MAS system has been deployed to calculate dynamically the Origin/Destination Matrices using V2I Messages. In [31] a negotiation framework using agent technology has been defined to improve parking in cities. The system is based on a flexible negotiation mechanism that takes into account the preferences of users (not only drivers but also parkings providers) to improve the city parking problem and thus traffic flows.…”

Section: Related Workmentioning

confidence: 99%

Forecasting Adverse Weather Situations in the Road Network

Tomás

Pla‐Castells

Martínez

et al. 2016

IEEE Trans. Intell. Transport. Syst.

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Using Negotiation for Parking Selection in Smart Cities

Cited by 11 publications

References 5 publications

Mobility, Citizens, Innovation and Technology in Digital and Smart Cities

Mobility, Citizens, Innovation and Technology in Digital and Smart Cities

The “ReadyPark” Collaborative Parking Search Strategy

Forecasting Adverse Weather Situations in the Road Network

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