How Google is Using Machine Learning to Predict Parking Difficulty3 min read
Applications like Google Maps have long since established themselves in the market as a large-scale navigation solution and provided millions of commuters around the world an easily accessible platform that helps them get from one place to another. However, there is one problem that continues to plague commuters around the world, and that is the issue of parking. In recent times it has been a constant challenge for commuters to predict the chances of finding parking spaces.
With this in mind, Google began to look for solutions to gain real-time information about free parking spaces. The constant flux in the demand and supply of parking meant that Google looked to implement machine learning to predict parking difficulty as far back as January 2017.
The Difficulty in Finding Parking
High variability in the availability of parking
A number of factors such as time of day, season, special events, holidays, etc. influence the availability of parking in any given area at any given point in time. The lack of real-time information regarding these variables makes it even harder to predict parking availability.
Inadequacy of inter-connected parking meters
Interconnected parking meters are a better alternative to independently stationed parking meters, but do not provide real-time information on parking availability. This highlights the rising need for smart parking meters and smart parking systems in our societies to perform more efficiently.
The complexity of parking structures
Although roads are planar making it relatively simple to estimate traffic flows, parking structures are often complex with multiple levels and a variety of customized layouts. This makes it near impossible to create a common platform to track parking flows.
The Role of crowdsourcing and Machine Learning in Helping Solve the Problem
Google has made an attempt to use a combination of crowdsourcing and machine learning to estimate parking difficulty levels. This has been relatively successful and also paves the way for developing better measures in the future. The method involves the gathering of high-quality ground data, which serves as an important criterion for being able to accurately predict parking difficulty. This is retrieved with the help of objective questions posed to commuters through Google services.
Modifications were later made to the machine learning model to incorporate estimates of live traffic, popular times and visit durations to better predict parking availability. Yet, despite several modifications, there is still much progress to be made in order to accurately estimate parking difficulty. The closest estimates were based on the differences in time between the expected time of arrival at a given location and the actual time taken to arrive at the location.
The problem with this measure is that delays or differences in the expected and actual time of arrivals may be caused due to factors other than parking. This can lead to several errors and gross miscalculations in estimating parking difficulty.
This demonstrates the importance of adopting smart parking systems and smart parking meters. We must consider smart parking systems as an alternative means to achieve efficient parking without having to run complex and currently unreliable machine learning models.
While machine learning techniques may well help predict parking accurately in the future and provide real-time parking availability information on a large scale, as of present smart parking systems provide the most viable means to optimize the use of parking facilities. They work irrespective of traffic estimates and gather high-quality real-time data by efficiently regulating and monitoring the inflow and outflow of vehicles in a given location.
Further improvements to machine learning models in the future can result in the two workings well in tandem rather than one replacing the other.