Maybe you could do this with Predict[] given day of week and time of day as input, assuming trains run on some kind of schedule? Otherwise maybe intervals (time since last train) for night/day/weekend?

BTW your detector is cool. Could you share how you connected a microphone to the RPI?

George, this is a very nice project you got going. For those who would like to take a stab at modeling. I extracted the main info I think - the time series of differences in seconds between the trains. Data are below - simply copy and paste in the notebook for modeling.

data = Import["train.csv", "CSV"];
times = data[[3 ;;, 3]];
diff = Differences[times]

{1259,780,660,720,1680,240,301,780,1679,1080,360,480,1080,4800,2160,360,660,420,2460,3780,3060,
840,840,600,720,360,1080,780,4861,239,420,5581,6959,5220,1080,3060,3120,660,10680,2701,5220,1559,
2400,1141,6119,7920,1800,540,1020,2161,779,1680,1141,899,1080,2160,840,3841,1439,3841,959,3000,
6720,720,7561,3960,2279,720,480,4200,3120,541,839,2760,721,839,420,1741,1439,240,360,900,960,300,
960,2040,360,541,479,360,1320,1500,601,599,7501,419,2880,361,5519,2040,300,1620,1741,359,2040,
1560,300,1380,1920,780,960,4920,360,1441,3839,360,540,3060,9780,960,1440,2160,1620,3961,3119,780,
900,2700,5040,1440,1080,360,960,3120,1200,3600,1561,2579,1020,1500,1200,240,1140,1080,2340,2461,
599,780,240,6240,540,5340,900,1261,1979,360,4440,1020,841,2459,1920,1201,1499,420,4320,1201,1079,
300,3000,1561,1139,1200,480,3780,600,721,840,2280,2280,959,420,1380,1441,2159,9840,2520,900,2461,
1799,1860,1140,2820,300,1260,1381,1619,780,240,1920,2341,2159,481,4679,961,4799,4920,2280,2160,960,
660,6960,3720,240,900,360,181,300,239,1620,1740,480,1800,480,1500,1621,1079,300,1140,3600,4381,
1800,1199,1320,1080,1740,1800,1501,899,1920,840,1440,600,6541,1379,2461,5279,4680,780,6481,479,240,
1140,1860,660,240,1380,3121,1499,1860,1981,1919,2640,780,540,541,1559,3721,3599,720,1440,7441,5699,
600,2161,359,360,181,1079,1021,2339,240,960,1140,2400,1380,241,419,480,181,480,1020,359,421,779,
600,1021,3239,1860,1380,420,241,3839,901,660,1379,7440,1260,1741,6719,1140,2101,1019,540,1380,960,
5640,900,2760,5160,840,1801,1679,6540,541,4620,2879,1080,780,3180,721,3240,5339,3241,4559,2340,
2580,14400,2400,240,5280,7201,1620,4799,1020,240,2460,3661,1319,1380,4560,3060,3180,2400,2521,2159,
1261,1680,3419,3960,300,1021,4259,480,7740,5280,3060,2040,2160,600,2760,2700,5940,600,4440,1141,599,
5280,1140,2580,8940,1500,780,3060,960,2041,240,1019,2820,1020,2161,1619,361,2400,3240,5820,2639,3360,
480,1080,5280,7740,1560,420,1020,1260,240,1681,1439,780,1680,3780,5940,2100,240,600,420,660,2340,
4080,300,1021,2999,3180,720,1200,1261,719,1140,961,360,1259,1440,240,1020,240,2160,361,1980,1440,779,
420,3840,720,480,1381,1019,3420,420,2520,3780,1140,4561,1379,540,2280,5161,4919,6060,1080,1320,1680,
1980,1920,240,1440,601,2100,839,541,360,8999,840,781,1800,1319,2700,660,2460,3360,2280,780,360,780,
1920,840,241,359,1561,1980,299,3300,1200,3780,4561,7979,2700,961,659,1380,6060,1201,1919,2340,541,8699,
1800,480,1860,481,1019,1860,2160,3360,3360,1860,540,4860,1140,1380,1200,1380,1140,3720,480,841,360,1260,
899,4920,1920,7261,4440,780,1679,1020,1980,660,1081,3719,2760,900,660,4200,961,3479,1741,5759,1861,539,
1080,4140,540,361,239,1620,780,240,1140,960,780,481,1260,359,780,1561,1499,4260,2461,359,181,1020,420,
1019,3960,600,2160,780,7560,1200,1260,720,720,300,540,8700,1380,1561,900,1019,3660,6540,2101,2579,1740,
1200,300,780,960,300,2760,661,239,780,3960,3240,3660,1440,1681,599,720,1800,1020,4801,1559,1200,1680,
1800,600,540,2160,781,1319,3540,4381,1020,1199,180,541,540,1079,2220,420,1861,179,6660,1741,900,240,5520,
179,240,3660,1980,1440,2520,1320,1201,599,1440,300,420,3901,1440,179,1140,8160,240,2580,480,241,1560,599,
660,720,1140,420,841,1079}

ListLinePlot[diff, PlotTheme -> "Scientific", AspectRatio -> 1/5, 
 ImageSize -> 900, PlotRange -> All, Filling -> Bottom]

Hi everyone - thanks for the interest.

This is a freight line, so the trains do not run on a schedule. Instead, demand, crew availability and line capacity all play a complex role. And yet the distribution of minutes between trains seems to fall into a Poisson Curve shape (I do remember my sophomore stats a little bit).

Raw data: https://dl.dropboxusercontent.com/u/8515698/train/train.csv

These are timestamps representing a minute wherein a train horn was heard (what if 2 trains come at the same time? Counts as one so far as the rPi can tell - but doesn't happen often). Since data and time are unwieldy, I included the posix time column, which is the seconds since 1/1/1970. It is useful for calculating differences.

Moderators' note: Data also attached to this post below and to the top original post.

Attachments:

Sounds like a fun project, it's definitely worth downloading Mathematica! You can fit your histogram to various distributions (and find the best fit), calculate the expected time between trains, probability of a train coming the next x minutes (based on time from last train) etc. If the noise of axles going thru gaps in the tracks is loud enough, you might even be able to count the # of axles of each train and estimate tonnage of cargo going by your house - could be a good economic indicator, or you could get the speed of the train if you know axle separation (by finding the auto correlation) :)

A first interesting observation. We have an interesting and for now experimental function FindDistribution When it ran on various parts of data it suggests that data are distributed according to WeibullDistribution or LogNormalDistribution distributions. And guess what? This are the usual suspects for wind speed distributions, for example see:

Mixture probability distribution functions to model wind speed distributions

Please do not post of issues with FindDistribution here - it is marked EXPERIMENTAL in documentation and under development

Still FindDistribution works very nicely. Some insights are below. The data diff are taken from the post above.

head = FindDistribution[diff[[;; 400]]]

WeibullDistribution[0.952215, 1868.28, 180.648]

tail = FindDistribution[diff[[400 ;;]]]

LogNormalDistribution[7.10678, 0.923829]

his = Histogram[diff, 60, "ProbabilityDensity", 
   ChartElementFunction -> "GradientScaleRectangle", PlotTheme -> "Detailed"];

plo = Plot[{PDF[head, x], PDF[tail, x]}, {x, 0, 10000}, 
   PlotStyle -> {Directive[Thick, Red], Directive[Thick, Black]}, 
   PlotRange -> All, PlotTheme -> "Detailed"];

Show[his, plo, ImageSize -> 700, PlotRange -> {{1, 10000}, All}]

We live close enough to several public crossings on a busy line to hear the warning signals (Morse Code 'W' - long, long, short, long) as the trains approach and enter the level-crossings.

I observe each engineer has a 'style' involving the cadence and sound level. It's not as pronounced as with steam whistles (where tones change with the amount of steam flowing through the whistles), but I'm sure you could determine which engineer is driving each train.

I'd be interested if this could be done on a Pi by learning the various "fists" (name from Morse Code world).

GlennP

The Pi is sampling at 44 kHz, so it has the resolution to detect the length and spacing of each individual horn blast. Pattern matching would then be a matter of software.