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From Andrew Butkus <>
Subject RE: Information
Date Tue, 15 Oct 2013 12:09:24 GMT
Also to add to this you probably wouldn't want to do it by route, but
maybe break it down by road, this gives more coverage and greater

Sent from my Windows Phone From: Andrew Butkus
Sent: 15/10/2013 13:07
To: Bertrand Dechoux;
Subject: RE: Information
IM not sure, i think the last 2 can be predicted, for example in
january in the uk we get bad weather which causes delays and on average
it will take longer to run a route in this month because of that,

To consider weather as a variable is probably not scalable, recording
the time to run a route with a timestamp should be good enough.

Also consider once a year there is a festival in reading, so over this
weekend routes through reading will always take longer.

IM not sure where mahout can fit this problem, other than, but if u can
train route time and add a timestamp this would give u something
scalable. Then figure out on average how long it takes to run a route
at similar time stamp, for example, minute, hour, week, month, year.

Sent from my Windows Phone From: Bertrand Dechoux
Sent: 15/10/2013 08:33
Subject: Re: Information
The biggest point is what data do you have and what exactly is your problem.

The maximum speed of the route can be easily known and in the best case
that would be your speed. From a very broad point of view, there is three
reasons for a slowdown.
1) traffic jam
2) accident
3) bad weather

But without up to date observations, those three points are non trivial to
predict (especially the last two). Doing simple statistics (like average)
can be a good start to see the variations and understand what factors
should be taken into account.

At the end, you want to do a regression but classification and clustering
might help before that. Hard to say more without knowing why the medium
speed is important, for which area, at which time...


On Tue, Oct 15, 2013 at 9:14 AM, Pavan K Narayanan <> wrote:

> Based on the information you have provided, street routing is potentially a
> Vehicle Routing Problem which is based on TSPs. You can check out the below
> link:
> Secondly, if you want to use Mahout for Forecasting, it is not possible yet
> as the solution methodology for Forecasting (LWR) is still an open problem.
> Bottomline: IMHO, you cannot use Mahout for forecasting at the moment; good
> luck with your project.
> Also, you can explore parallel computing paradigms if you have relatively
> high volumes of data.
> On 15 October 2013 12:19, Angelo Immediata <> wrote:
> > Hi there
> >
> > I'm pretty new to learning machine and apache mahout as well so pardon me
> > if this question is not too correct :)
> >
> > I'm in a street routing project where, beside other functionalities, we
> > have to make forecasts. Precisely we should be able in forecasting the
> > medium speed in a street in a well know period season (e.g we should be
> > able in answering to this kind of question: on the american route 66 what
> > will be the medium speed in spring 2015?)
> > As far as I know in order to offer this functionality we should use some
> > learning machine; this is the reason I'm checking mahout (moreover we
> need
> > to guarantee high performance and since mahout is based on Apache hadoop
> > and since it uses Map/Reduce, it seems to me very amazing)
> > The first question I'ld love to do is: can I use Apache mahout in order
> to
> > implement the previously written funcionality?
> > If I can use it sure I'll need some data in order to "train"
> mahout....can
> > I train mahout in a different time respect to when i need the prevision?
> I
> > mean: can I make the train let's say every week at 10pm and then offer
> the
> > forecasting functionality only when a user is interested in it? Should I
> > store the training result in some way?
> > And the last, but not the least :), always if I can use mahout....which
> > algoritm should I use in order to implement my scenario?
> >
> > Thank you for the help and pardon me if i was not too much corrected
> >

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