Abstract-Finding a vacant parking space in a large crowded parking facility takes long time. In this paper, we propose a navigation method that minimizes the parking time based on collected real-time positional information of cars. In the proposed method, a central server in the parking facility collects the information and estimates the occupancy of each parking zone. Then, the server broadcasts the occupancy data to the cars in the parking facility. Each car then computes a parking route with the shortest expected parking waiting time and shows it to the driver. We conducted simulation-based evaluations of the proposed method using a realistic model based on trace data taken from a real parking facility. We confirmed that the proposed method reduced parking waiting time by 20%-70% even with low system penetration.
In this paper, we focus on multilevel parking facilities and propose a navigation system that minimizes the time required for cars to find vacant parking spaces. Parking zones at large parking facilities provide drivers conditions to drivers due to differences in distances from the entrance of the parking facility or to the entrances of the shopping areas. This leads to many cars concentrating at some parking zones while other zones are not occupied. It is not easy for car drivers entering a large parking facility to know which parking zones are vacant. It is fairly common that parking facilities have indicators that show occupancy information to the drivers. However, since these indicators deliver the same information to all drivers, this method tends to make a new congested zone by sending many drivers to that zone. In this paper, we propose a system that provides each driver with a recommended route in the parking facility that minimizes the expected parking time. Our method estimates the occupancy of each zone from the information sensed by the cars that implement the proposed method. This information is collected to a server installed in the facility, and then the server disseminates the processed information to the cars. The cars then calculates the recommended route from this information. We conducted a simulation-based evaluation of the proposed method using a realistic model simulating a real parking facility in Nara. As a result, we confirmed that the proposed method reduced parking waiting time by 20%-70% even with low penetration ratio.
Traffic congestion in parking lots is a common phenomenon across the world and larger commercial facilities with multiple parking areas may be particularly affected as many users struggle to gain access to sought-after parking spots close to their destinations. These popular zones often see traffic jams forming as many vehicles arrive within these regions, while less popular areas may remain free from congestion. This creates a very uneven distribution of traffic, with motorists in popular areas becoming trapped and unable to leave bottleneck regions. As a result, the car park management industry has taken an interest in research into parking guidance. Parking guidance has been developed to help improve efficiencies in car parks, guiding drivers to specific spaces using GPS technology to highlight free spaces near their location detailing the most efficient way to get to that spot. Associate Professor Akira Kawai, who is based at Shiga University in Japan, has been working on a KAKEN project that seeks to leverage real-time positional information to help guide drivers to free spaces within parking lots.
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