Using Time Machine to Find Patterns
Time Machine is the Hapn feature that allows you to view past activity in the map, with all the alerts and routes on screen. The scrollable timeline gives you easy access to the individual position reports that come in, so you can dig into the details. Using time machine to find patterns in your trackers is an ideal use of the feature. Recently, we extended the timeframe that you can run a time machine session. This gives you the chance to look at multiple days at once, increasing the chances of seeing patterns in tracker behavior.
Looking at the image below, we can see that this tracker has a defined pattern of movement, I can see repeated trips and the same basic shape of movement.
Not only can we easily see the East - West pattern that seems to be very common for this vehicle over this two-week period. We can also see less frequent trips headed South and one outlier heading way to the North.
What this tells us can be broken down into three major categories :
- Usual movements, behaviors that are expected.
- Atypical movements, not expected but not abnormal.
- Abnormal movements. Wholly unexpected behavior.
Usual Movements
These patterns help us make sure or tracked vehicles or assets are doing what they should. Seeing expected patterns is the first step in investigating any issues that come up. You can see from the above that there is variation in the most used pattern, but it all stays mainly on the same track.
By seeing this grouping of activity over a longer period of time now allowed in the Time Machine, you get to establish the baseline behavior for a tracker. It is also an excellent tool for making sure these daily behaviors are going as planned. Looking to make sure your equipment dropoff is happening when it should be, where it should be, is vital and these usual movements are where to find that behavior.
Atypical Movements
These are the movements that are not everyday patterns, but occur in a predictable pattern. Maybe it is a weekly trip to a specific location to pick something up. It could be an infrequent working location that only needs periodic check-ins. In the above example it is the southward trips, there are still a few of them, but not nearly as often as the East-West examples.
If you see these types of patterns, you should typically check with the drivers to see what the trip was about. If it is an asset, make sure that the movement was needed and necessary. Figuring out the atypical movements will also help with figuring out what to expect when looking at tracking data.
Atypical movements can also be problematic. If a vehicle is being used for off hour work, for example, it may be happening repeatedly in the same locations. Talking with drivers when these instances happen is key in getting to the bottom of what's going on with the unexpected but semi-consistent movements.
Abnormal Movements
These are the tracked events that cannot be explained easily. They are random and not a part of the larger normal behavior. These are the incidents that you will want to investigate. There is no telling what may have occurred without taking a deeper look into the event.
In our example image above, the northern trip is the one that we would take a look at. It is very different to the others and is a one-off. To dig into the details, we can make a smaller time machine session. This can be focused only on that trip. Then, when looking at it piece by piece, identify if there were any stops. If there were, it may be worth putting a boundary around this location, so you can be aware of any repeat visits down the line.
The reasoning for these trips can truly be anything, but they are the ones typically that need to be investigated first.
Wrapping up
Time machine is first and foremost a tool to see past activity. How you leverage that past activity depends on what tracking and what the goal of the operation is. This will hopefully help clue you into what to look for when view time machine data. Patterns are usually indicative of something, so being able to see a pull them out is key.
