If you set a goal and analyze how routes have traditionally been planned in various areas of business, you can see that the main number of routes is not a banner. That’s how the planning chain traditionally looks like – the logistician distributes the order among the drivers, and the drivers themselves determine the routes. And ultimately the driver, in addition to his main function – to drive a car – appears a lot of additional ones.
This includes loading/unloading management, route planning, communication with customers, solving problems that have arisen, etc. And this means that with the increase in functions, the problems multiply, and there can be no talk of any optimization. To change the situation, there is a very convenient service – the distance matrix.
What is the distance matrix?
The distance matrix is, in layman’s terms, a mathematical model that calculates the most logical route between different locations in reality and gives timely and efficient results if there is a change in location.
When the task is to optimize a combination of translocations for several people who are in different starting locations and want to get to different planned places. Distancematrix.ai provides such services. It uses various APIs, namely: Distance Matrix API, Asynchronous DM API, and Geocoding API.
Examples of usage scenarios
For example, you work in a logistics company, and you need to plan transport carriages in such a way as not only to satisfy your client’s request, but also not to increase, and maybe even reduce your fuel costs, driver’s wages, loss of time, etc. In this case, use distance matrix API (DM API), and many of your problems will cease to be such.
At the same time, it is important to note that when offering you the best routes with the calculation of the time to overcome them, the DM API takes into account the actual nuances of the planned movement. The first is how you travel (on foot, by public transport, by bike, in a car, etc.). The second is what happens on the planned route exactly at the moment of moving (traffic jams, traffic patterns, etc.). But what is even more important is that the answer with all of the above information will “fly” to you almost instantly.
But what if you are faced with the task of optimizing routes for a very large number of start and finish positions? It could be a million locations or more. This task is also solved with the help of one of the service products – asynchronous DM API (ADM API). ADM API has great potential: processing large amounts of data, relatively low payment for it, and almost unlimited coverage of any part of the world.
And yet, with such undoubted advantages, ADM API also has some limitations. Namely, the impossibility of taking into account the current road image at the time of route planning and a quick response to your input information.
In summary
And finally, let’s say you need a service such as geocoding or reverse geocoding your data. In other words, you need to convert either a postal address to a geographic grid (perform forward geocoding) or a geographic grid to a postal address (perform reverse geocoding). Distancematrix.ai’s product can meet your needs.
Here Geocoding API (GC API) will be the most suitable product for you. Using a geocoder and generating an HTTP request, you will receive information that is useful to you in one form or another that is convenient for you.
