It has been estimated that close to 30% of the cars circling a city at any given time are doing so as drivers look for parking (Donald Shoup, Professor of Urban Planning, UCLA, (2011)). In addition to the drivers’ frustration, those cars are creating traffic congestion. In 2014, the World Bank estimated that urban drivers waste 111 hours a year on congestion, which translates into $2 trillion a year in lost productivity. From an environmental perspective, this congestion translates into large amounts of wasted fuel and carbon emissions. Congestion and parking are closely correlated, since cruising for a parking space creates additional delays and harms local traffic circulation. In central areas of large cities, cruising may account for more than 10% of the local circulation, as drivers can spend 20 minutes looking for a parking spot (Dr. Jean-Paul Rodrigue of Hofstra University). In an effort to make urban parking much easier, EasyPark developed a smartphone application that makes it quicker and easier to find, pay, administer, operate and plan parking through the use of mathematical optimization.
The key inputs of the parking optimization capabilities are:
The first step to deploy EasyPark in any city is to create a map of all parking spots. EasyPark collects data related to street parking by sending their vehicles to crawl the streets and take photos. Data scientists at the company use machine-learning models to read and interpret the photos taken. In addition, EasyPark collects data related to off-street paid lots. The resulting database is typically more accurate than the data managed by the city itself. The data science team estimates the probability of finding a spot by time and by city block, using a Bayesian statistical model. There are five main data sources for the predictions:
EasyPark uses a mixed-integer programming (MIP) model to compute the best route to search for a spot. The key inputs of the MIP model are:
The objective function is to minimize the time to reach the destination, which is equal to the time it takes to find a spot, plus the time to walk to the actual destination. The output of the MIP model is the route the user needs to follow. This route may need to traverse low-probability blocks to reach high-probability blocks.
The benefits that EasyPark has identified from the MIP solution are:
The MIP model was built by Yossi Amgar. Yossi has a bachelor’s degree in computer science and does not have formal training in mathematical optimization. EasyPark chose Gurobi for three reasons:
EasyPark was founded in 1997 in Stockholm, Sweden. EasyPark operates in more than 700 cities across 13 countries, primarily in Europe and Australia. The company’s vision is to make “urban life easier – one parking spot at a time.” EasyPark is a smartphone application that helps you pay for parking, as well as find an open parking spot with the help of optimization. For more information visit: easyparkgroup.com.
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