How is Big Data rethinking parking management?
Between the emergence of intelligent car parks and new solutions to improve parking experience, the management of parking is being digitized. This digitisation functions in particular by using extensive data to optimize the management of parking.
This involves a variety of issues. First of all, facing the extent of existing data concerning a car park (occupancy rate, time traffic, average speeds, etc.), there is a need to interpret this data which is often unstructured. Then, in a large city, each space must be made profitable.
Thus, any solution to maintain a satisfactory occupancy rate is well seen the parking managers. Among these solutions, many of them use Big Data. We will see how Big Data is in the process of making major changes to parking management.
- Parking date centric: the key to success despite the challenges
- The art of predictability
- A key safety link
- Bid Data: the essential fuel for Machine Learning
Parking data centric: the key to success despite the challenges
As we often say "We can only improve what we can measure". And that's a good thing. Because the purpose of Big Data is to collect and manage high-volume data in a context where it is often unstructured.
Once this data is stored, managed and interpreted, the possibilities are endless. It will now be possible to measure just about anything. However, this comes with a certain challenge: choosing what to measure and identifying what data is relevant and what is not.
Whether it is to measure variables related to parking time, the number of employees using parking spaces according to their profession, or anything else, Big Data will allow you to have quantified previews, allowing a better overall view.
The art of predictability
The advantage of Big Data in general and unstructured data in particular is that it does not have a precise format. This gives a great deal of flexibility to anyone who is responsible for interpreting these data.
One of the applications of Big Data is to extract models from all this data. Once these models have been extracted, it is possible to identify general laws in a forward-looking approach.
For example, depending on the time or date, it may be possible to predict the number of people coming to a car park in order to accept or refuse new bookings accordingly. This will avoid saturation, or on the contrary under-utilisation of the car park.
A key safety link
One of the cornerstones of security is Data. By data, we naturally mean the data, but above all its ability to process it correctly.
In the case of an act of hacking, for example, it is often required for the attack to take place in order to act, whereas very often it is too late. Some software solutions using Big Data allow attacks to be correlated with different events in order to better anticipate them.
In the case of car parks managed via software platforms, Big Data could be an excellent ally in the fight against malicious acts.
Big Data: the essential fuel for Machine Learning
Big Data is widely used in automatic learning algorithms, which have multiple applications. Machine Learning can be used, for example, in facial recognition or license plate reading. With a multitude of possible applications for Machine Learning, taking a large amount of data can be an excellent initiative.