Artificial Intelligence and Parking Management: 6 concrete examples
Artificial intelligence has become a crucial element of our daily lives, adapting to almost every scenario of it.
In this article, we will see concrete examples of applications of Artificial Intelligence in parking management to improve parking experience. Here are 6 application ideas made possible by a 2.0 management of your parking data.
- Count the number of occupants and vehicles
- Facial recognition
- Predictions on the occupancy rate
- Detection of vehicle speed
- Deduce models using parking data
- License plate reading
Count the number of occupants and vehicles
Business Intelligence (B.I) or Business Intelligence is the set of methods and tools used to collect and format data for processing by decision-makers. Bosch has implemented an IoT tool capable of detecting the presence of a vehicle in a parking space and to transmit formal information via an analysis platform. This data can then be analyzed by parking managers, any other analyst or decision-maker to optimize the parking management strategy. This is the case with BePark's platform, which allows you, for example, to observe this data in order to analyze it later.
Artificial Intelligence is a vast field that includes Deep Learning. This Machine Learning method is inspired by the functioning of our brain, in particular through a system of neural networks. There are thus many applications, such as facial recognition. This one can have several uses.
First of all, it can help to recognize a person by name in order to facilitate access. And then, it is a further step towards a more secure car park.
Predictions on the occupancy rate
Machine Learning is a statistically based method. This is a type of algorithm that allows automatic learning by deducing general laws from past information. This method can be very useful for performing predictive models.
The latter may, for example, have the virtue of anticipating a future need according to different parameters: taking into account, in particular, the time, day of the week or the current behaviour of motorists.
Detection of vehicle speed
Always with the goal of improving safety in car parks, speed control can be a work in progress given the risk of collisions. Thanks to a simple video, a Deep Learning (D.L.) algorithm can detect the speed of a vehicle. Once this speed has been detected, several measures can be taken in the event of an excess. For example, it is possible to automatically notify an occupant by email for preventive purposes.
In addition, the speed of vehicles travelling in car parks can be an interesting factor in itself, as it makes it possible to become a performance indicator in terms of the parking experience, particularly in terms of traffic flow.
Deduce models using parking data
Another advantage of Business Intelligence is the ability to deduce models and general laws from parking data. This deduction can be made automatically or consciously.
For example, it is possible to establish correlations using graphs between time and occupancy rate. Your data has something to tell you: by interpreting it, you can implement strategies accordingly.
License plate reading
The automated recognition of license plates is done in a classic way using an optical character recognition technique. This is very effective and can also be replaced by a Deep Learning algorithm.
For example, reading the plates can identify an occupant in order to grant him automatic access to a car park, without having to ask him for any action (pressing a button, removing a ticket, etc.) and without the intervention of a third party.
This automatic reading can also be useful in the acquisition of parking data. For example, suppose that by using these parking data, we can deduce that sales representatives are making greater use of car parks, it will be possible to readjust their parking management strategy according to the number of sales representatives recruited, their schedules or their attendance within the company.
As you can see from the examples cited, artificial intelligence has an impact on parking management in the digital age. However, it is crucial to address the issues that can be barriers to the wider adoption of this technology, as it is essential to build user confidence in it.
Factors such as convenience, speed, accuracy, confidence and experience are extremely important for any technology to be better accepted, and artificial intelligence is not a unique case.
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