Alright, so this is traffic works. I'm Sean. This is Tom, and we're interested in Climate change so we see everyone just got these flash flood warnings, right?

So I'm not going to say one flash flood does not mean climate change But we are getting a lot of weird rather recently go the next slide, please so this is where I used to live Austin Texas a lot of bad flooding flooding in Houston as well

I have a friend that just had supposed to pictures on Facebook that his whole house is just down And there's like hundreds of people are out and this This again this one anomaly does not show climate change, but we're seeing all these Flooding on the west on the East Coast and we're seeing drought in the West Coast and so what can we do about this?

thing well We know that greenhouse gases are somewhat to do with climate changing and One one major contributor of greenhouse classic gases is transportation now not a lot We know that it's probably about 25% of in New York all the transportation

all the greenhouse gases, but anything that we can do to decrease the How carbon is going into the atmosphere is something that we can be we can be proud of so what we wanted to do is take transportation information from the NYC dot and Bring it into new pic and see can we predict what's going on in the traffic?

And if we can predict and see anomalies in the traffic system then maybe we can reroute traffic to go around workplaces where there's stops if Traffic is blocked up then that makes cars are idling and having more greenhouse gas emissions So Google Maps does a pretty decent job of this right now if you go to Google maps

You can see right now that there's red lines place and that means things are blocked up, but which is great except that Rush hour always blocked up And so it doesn't really give you a lot of information to know that things are blocked up right now or rather

It's better to know what is really different from normal, and that's what we think new pic can do We can see over the long period of time when prep when traffic is worse than normal And then and only then give notifications and say hey, you should go somewhere else, so that's the idea We didn't get that far, but we do we did get some information

So we went to the NYC dot Went to their stream of data And you had nice it'd be nice if they had a nice API that we could pull data from well what they actually have is a CSV file Which would be nice if this was the actually queryable, but this is real-time data, so

And he has no historical data, so what we've been doing to get data is for the past When when did you say hey just start grabbing cache data right now? Yesterday morning, so we've been caching data yesterday morning every minute pulling all this data stashing the system and feeding it into new pic

Let's see anything else about that data, so what we've got here is speed data, and we've got Around different paths so these black lines represent the paths that the traffic on speed cameras in New York City show all right, so all those black lines, so we pull all that data into new pic and

Just that little that little section right there, that's lit up and Thankfully and and I don't want to seem mean But there seems to have been an anomaly so we did get to see that

And we did get to see how well new pic learns about that want to rerun that Okay, so yeah, it's not gonna take that long so But we do have a fair we we don't have so the data comes in per minute So we have a fair number of data points. We have just over we have about five hundred sixty

Data points so when Tom runs this thing we're going to see that And this is really just for one of those lines. We actually got data for about 200 different routes, but this is just one of them We kind of cherry-picked one that had an anomaly so we see here going up and down

and again, this is really just for a very small period of time and Just during the weekend so what we would expect is we would get this data for weeks at a time and Then we would see traffic going up and down Right now we see we're at what two o'clock, so then our our cashier kind of crashed And so that's it this whole line is it ran out of memory

So we came in in the morning the next morning and said oh, that's that's not right So to be coming up what's okay, so okay, so there we go so now we got now we go and Uh

Down at the bottom you have to take our word for it is the the prediction. What is it the? Anomaly So right about there we got to about 500 data points And then it said hey we could see anomaly and right here you see this huge Jump right here, which is it might be just around when the weather started getting bad

Right and here we see this flat Just until we get about 500 data points, and now we can actually start predicting things So what we'd like to do Given that the data is still kind of weird we can continue to run this thing

Put up a Heroku instance and just start keep keep going going going most of the code is there In fact the code to light up a little line on Google Maps is there mostly and I think that it would be interesting to see this sort of thing after about a month and see how well it can predict

Just as an as an interesting aside this this sort of prediction didn't actually take all that much Processing power so we were actually able to run 120 instances side-by-side Before we started slowing down and screwing around with the actual Speed of it where we couldn't access it so instead of graphing 220 lines we chose one

Because it would be a lot easier to detect the anomaly Right and that's it. Thank you. Yeah, how frequently do you get the data?

Every minute so the the data file is every minute, and we're grabbing it every minute Is it changed dramatically every minute or is it a very slow changing over that? Yeah, you can see from the from the blue line that it's actually pretty consistent

Where it and and sometimes like during rush hour It'll change drastically that it'll it'll go from like we we we grabbed travel time instead of speed because We wanted something that would go up instead of something that would go down with traffic

So keep in mind that this is just one of the paths um which is fairly flat the first one we did Was more of a gradual up and down so it really depends on the path Yeah, well and and again. There's always the risk of oversampling if the data changes trailers slowly compared to your sampling rate then

It's not gonna work as well, but you there's some sort of happy medium What we would expect if there was a really bad like crash that it would go up and up and up And then it would go from maybe 20 to boom and those are the things we really want to predict

Other types of data sets In the prediction algorithm like taking into account the weather Or if there's a concert or something else, but we didn't quite have the time to implement those But in the long run, that's the kind of thing that we're gonna want to try to implement into the algorithm

I think the basic idea of not modeling traffic, but the anomalies in traffic flow. That's really cool Thanks All right, thanks again you