A Conceptual Framework for Modeling Smart Parking

Lejdel, Brahim

doi:10.5772/intechopen.92559

Cited by 2 publications

(2 citation statements)

References 14 publications

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“…Lejdel, in [ 27 ], proposed a smart parking approach based on the multi-agent system and genetic algorithm. It assists agents in fining the optimal parking spot, where an agent sends a parking request based on his location on the street and the parking and waiting time.…”

Section: Related Workmentioning

confidence: 99%

“…Previous research solved the parking problem as an optimization problem [ 24 , 26 , 27 , 33 ], game theoretic problem [ 28 , 30 , 31 , 34 , 35 ], queueing problem [ 39 ], and machine learning problem [ 38 ]. Different preferences were considered in this research, such as driving distance to the parking area, parking cost, physical positions, availability of parking resources, traffic congestion on the streets to the parking area, and driving time to the parking area.…”

Section: Related Workmentioning

confidence: 99%

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A Balanced Algorithm for In-City Parking Allocation: A Case Study of Al Madinah City

Abdeen

Nemer

Sheltami

2021

Sensors

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Parking in heavily populated areas has been considered one of the main challenges in the transportation systems for the past two decades given the limited parking resources, especially in city centres. Drivers often waste long periods of time hunting for an empty parking spot, which causes congestion and consumes energy during the process. Thus, finding an optimal parking spot depends on several factors such as street traffic congestion, trip distance/time, the availability of a parking spot, the waiting time on the lot gate, and the parking fees. Designing a parking spot allocation algorithm that takes those factors into account is crucial for an efficient and high-availability parking service. We propose a smart routing and parking algorithm to allocate an optimal parking space given the aforementioned limiting factors. This algorithm supports choosing the appropriate travel route and parking lot while considering the real-time street traffic and candidate parking lots. A multi-objective function is introduced, with varying weights of the five factors to produce the optimal parking spot with the least congested route while achieving a balanced utilization for candidate parking lots and a balanced traffic distribution. A queueing model is also developed to investigate the availability rate in candidate parking lots while considering the arrival rate, departure rate, and the lot capacity. To evaluate the performance of the proposed algorithm, simulation scenarios have been performed for different cases of high and low traffic intensity rates. We have tested the algorithm on in-city parking facility in the city of Al Madinah as a case study. The results show that the proposed algorithm is effective in achieving a balanced utilization of the parking lots, reducing traffic congestion rates on all routes to candidate parking lots, and minimizing the driving time to the assigned parking spot. Additionally, the proposed algorithm outperforms the MADM algorithm in terms of the selected three metrics for the five periods.

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Section: Related Workmentioning

confidence: 99%