Why should you use the idle time metric?
Why should you use median time metric instead of Number of bikes metric on Locale?
Any console that a user creates should be able to tell us where things are going wrong. To do so, we must identify the suitable metric that will help us in identifying the root cause of the problem and how to troubleshoot bottlenecks.
However, one of the most common errors we noticed in this process was that users used such as "Number of bikes", "Number of drivers", "Number of trucks", and so on to understand important factors such as utilisation, fleet performance, demand, and so on.
This blog will help demonstrate why this is a problem and to present a new way of thinking about it with the help of median idle time metric.
As you know, there are two ways to examine data: in real time and historically. When you look at metrics like Number of bikes, Number of drivers, and Number of trucks for different areas historically it would be wrong.
Source: Towards Data Science
This because they are moving assets that are constantly moving from one area (hexagons/polygons) to another, with their states changing from idle to busy.
Therefore, when this data is aggregated for several locations across days, weeks, or even months, the number of bikes or drivers in any specific hexagon or polygon will not accurately reflect the distinction between these idle and busy states.
In a nutshell, the number of bikes metric cannot distinguish between idle bikes, available bikes, and busy bikes. As a result, users will be unable to derive insights about utilisation, productivity, and supply-demand gaps.
We at Locale created a metric called median idle time to tackle this interesting challenge. This metric allows you to track the median idle time for all bikes/drivers/trucks in your region definition (hexagon/polygon) before they got a booking or an order.
Time Spent Metric on Locale
The idle time or time spent metric calculates the time spent in a particular state by your supply. Through this, you can measure the idle time of the driver, time spent in delivering an order, or idle time at the restaurants or warehouses.
Since these assets are constantly on the move, you can use the median idle time as we take into account both time and area to provide an accurate representation the bike movement. You will be able to accurately understand how your areas are performing based on how much time your supply (bikes, drivers, or delivery personnel) spends:
It will help you better understand how your assets are performing and also answer questions like:
- Which is the area in which the time to get a ride is minimal, and in which area/areas it takes a lot of time to get a ride?
- At what time of the day are most of the fleets idle?
- On an average, how much time do all the fleets spend being idle and in which area?
- What is the pattern of idle time during peak hours?
- In which lap of the journey/delivery are they idle?
So the next time you want to understand the demand patterns, utilisation or want to take important decisions on your moving assets, you can use the idle time metric. To understand how to create an idle time metric on Locale, check out the link down below:
This is a very complex engineering problem on the backend as we are working with raw GPS pings in real time. If you want to know more about how idle time is calculated internally, you can check this article out.