Saibot Airport makes a lot of money from the car parks around the airport. Close monitoring and careful management of the parking process is important for that reason and because it is an important aspect in the evaluation by travelers in airport surveys. Indicating at the right time that a car park is full for example prevents people from needlessly driving in circles to find a free spot. However, too early warnings mean that people are unnecessarily forced to park their cars on the more remote car parks – not necessarily improving their experience.
In this section, we will see how an OEP application is created to process all events signaling a car entering or leaving a car park and thus deriving the number of cars currently inside the car park. The application will publish a special event when the car park is declared full. and it will publish a similar event when spaces become available again.
Next, we will look at how we can correlate events, detect ‘missing’ events and derive business intelligence from them. Chaining OEP processors – within or across applications – is commonly done to derive varying conclusions from a certain base set of data signals at different levels of granularity; this can be done across applications or within. We will use several processors inside our application.
We determine for cars that leave how long they have stayed in the car park; we could use that to automatically calculate the parking fees. We next extend our summary with the average stay duration of cars. We look for cars that have out-stayed their welcome: our car parks are short stay only, intended for parking up to 36 hours. Cars that have been in the car park for more than 48 hours become candidate for towing.
The sources for the final situation for this section are found in folder SaibotParking – directory CarParking_step1 through CarParking_step3.