Find Nearest Cab Service

In my opinion, we have the use of sphere geometric service at our website https://www.quora.com/What-is-Ubers-server-architecture. . Every cab has a system that transmits its position and the availability of the operator to a centrally located remote control system. In this way, the host computer can determine the nearest rider, but can also make a choice on the basis of rider evaluation and other business considerations. Every scuba diving professional has his own state in the main data base.

In order to get the next one, they probably use an approach, so they take the degree of latitude/L longitude of the users, work out the rectangle area on the chart and select the vehicles in it. Once filtered, they can then compute the actual route using the street chart, which is much more resources intensive.

Datastructures - Algorithm for two Reality Issues

I have two real-life problems where I struggle to find the answers to : Service Restaurants : If I use my order apple application (like FoodPand, Zomato, etc.), the apple application recognizes my position when I log in and proposes the nearest restaurants accordingly (probably good enough in the area so that the chosen restaurants can supply the food).

Cabin service : If I use a taxi service (such as Uber or Ola), they also recognize my whereabouts when I try to get a taxi and suggest the taxis available at that point in the area. Q. How is the search for the nearest restaurant and taxi located? What particular algorithms do they use in practice?

Because both cases are different, because the searching information is in one case statical and in another case always change. After I brainstormed on this subject, I learned that since a restaurant is a solid unit, it is possible to represent it using the KD tree (which allows you to store space indexes).

On the basis of the customer's site, we can conduct a KD treesearch to find a number of local cafes. Creating the KD trees will take O(n) hours and finding them will take O(logn) hours, where n is the number of n Oden in the trees. The taxi service does not have static taxi locations (unlike in restaurants).

The creation of a KD treeview for every modified location of taxis seems to be an effort. What can I say to find 5 nearest taxis to me when looking at the actual location of the taxis? What algorithms are used by taxis in practice? P.S.: I also came across the K Nearest Neighbor algorithms, which in turn lead to KD beams.