The strike, the A.I. and the forecast.

On the Monday December 30th, 2019 I had to go to the office. I live 40km south of Paris, France. Due to pension-strikes there were very few trains and I planned to go early by car. Poor decision global warming wise but the only one I could bear with.

Anyway, I left home at 6:30 am (which was too late according to my plan) and set my favorite navigation software to get the best way to the office. The path selected was through all the secondary roads and the planned driving time was 1h44 relative to a 0h39 without any traffic (in the middle of the night).

The software I use is a very common one and is dedicated to navigation. There are embedded software with the phone’s operating system, but I noticed that the software I use gives better results when traffic is heavy. From what I could notice (and I’ll go in some hypothesis from here) there are two factors according to me that allow to get a better travel time computation on this software:

  1. collaborative information capture from drivers.
  2. I have the feeling that the paths computed in this software are using some forecasted traffic impact, meaning that the path must be sliced in small chops and for each small leg, an adjusted speed factor is applied taking into account the time it takes to get there. I think that the other software just computes the travel time just with the traffic situation at the path request.

And this has some bias, because if you are one hour from your office and you start in the morning, you might have a flowing traffic, but you don’t take into account all the commuters starting their car and that will join you.

On top of that there must be some algorithm, I would say machine learning as huge amount of historical data is available and the huge amount of data has to be generated to provide the service.

1h44 was the travel time I expected, since the beginning of the strikes the traffic jams in the Paris area met historical records. I was then happy with my choice; I was convinced to use the correct tool.

But when leaving my city something was wrong, the traffic wasn’t a “1h44 like”, I mean when this occurs, you already have trouble at the beginning of the trip. And there was nobody around, I was almost the only fellow on the road…

When I saw little cars on the motorway, I decided to use the usual way, and it took me a total travel time of 40 minutes, there was very little traffic.

The Percent Error of this forecast was 160%, in other words terribly wrong.

I am not criticizing here, just sharing an example where technology needs improvement.

If we transpose to demand forecasting, machine learning techniques give quite quickly good results with less effort than usual time series techniques. I tried the Prophet procedure on some of testing dataset and the results were promising on average.

What is interesting from my example is that the Navigation software sensed that there was some unusual thing happening in France, you never get 1h44 travel time at 6:30 am it obviously learned from the past weeks. Unfortunately, the algorithm was misled by the holiday period (as you know in France, we are either on holidays or on strike 😊). And due to the strike context, many people just stayed at home between Christmas and New Year’s Eve. Then few took their cars on this Monday morning.

How could this be improved? I managed to take the good decision as I could detect that the “early signs” of the traffic were not the ones that indicated a “black day”. So If I extrapolate to machine learning systems, there must be some feedback loop to sense if the most probable scenario is happening. I mean the software must have triggered an alert by sensing that the connected users were driving much faster than the forecasted speeds on all the roads or even more efficient, on representative sample areas. Here the new normal defined by the machine learning algorithm had to be questioned.

Same goes for the short-term demand forecasts when there is enough volume, some customers or some Items could be sensed to validate trends on a given forecast group.

And this deviation is identified, it should fall on the desk of an NI (Natural Intelligence), on other words a human, a demand planner, a forecaster to request some creative decision based on extra knowledge that is not contained in the data used by the algorithms. Now it is still difficult to identify exceptions happening for machine learning algorithms and human check is still required.

ROADTOSEE will help you in choosing and implementing your machine learning demand planning algorithm. for more information and contact information.