mammoth transit app

But in order to stay faithful to the look of diagrammatic transit maps, bezier curves weren’t quite right. Integer-linear-programming was what allowed us to explore all the possibilities and find a solution that globally minimized the penalty function. All the lines were drawn on top of each other.

We had just finished our aforementioned super compression algorithm and felt ready to tackle a new problem. That made it risky to use, even in ‘offline’ pre-processing inside the backend. We had this up and running within a couple of weeks. The minimum radius depends on the total width of all the parallel lines. But when the next stop isn’t shared, the lines ‘unsnap’: But that’s not how trains and buses really travel! Before publishing our maps we needed to get rid of the ugly, unnecessary criss-crossing of lines, which was turning them into a horrendous spaghetti-mess. With so many transit lines in certain areas, this allows users to create fully customized maps. First, we tried a heuristic solution — sorting lines based on where they terminate — but this often failed, working in some places, but not others.

(So much for having the whole thing done in three!). Real transit maps have lines drawn in parallel.

As you might expect, avoiding a cross-over in one place on the map could create another one somewhere else. If we could sort the lines to minimize the visual clutter near intersections, we would have a publishable map. Good app guys even though it does turn itself off after a while, but would be good to add some bright colours or even be able to add background photos, Only works for 30mins then it can’t even detect “scan” device.

A small loop in the route would throw off the centerline to infinity, and we also needed to deal with multiple lines, multiple branches, different stop patterns…, After two months of bashing our brains against our keyboards, we finally threw in the towel. But while they were pretty… they still looked strangely naked. It’s also the problem with Google’s Transit Maps. Want to play with our maps?You can get Transit for free on the App Store and Google Play. We then created an algorithm to draw transit shapes on a giant black-and-white canvas representing the whole world, where every pixel is equivalent to one square-meter.

Public transit riders are helping one another avoid crowds. The first step was to show the source data on a map. This system enables us to manage 350 transit feeds in 125 cities with a small team. We will continue to work under the hood, not just toward having the prettiest app with the best UI, but the most functional, powerful and accurate transit app out there. To do this, we had to decide which lines would go left and which would go right, in order to minimize their crossings. And they’re also contextual: unlike a PDF of an agency map, our maps incorporate your location giving you a sense of where you are relative to nearby lines and adjusting the look depending on your zoom level. And thanks to their community of volunteer map-geeks, OSM has virtually all the tracks for the public transit rail lines that we use in our app. Below the surface, there’s a lot of ‘hard’ technology quietly driving it. That’s because they were missing stops. Or learn more about the company on our website. What we needed to do was identify common segments, where they travel on the same street, and then ‘snap’ those lines together. What are the main differences? Altogether, the drawing, skeletonization, network building and glitch removal took somewhere around six months to develop.

1 day old fridge & thinking about getting refund already. There were huge lags between the release of new cities, and there was something strangely off about the way the maps looked — as though they were drawn by humans, not computers. But it turns out it’s quite the problem to solve, and Google (even after all these years) still can’t generate very pretty, or even very useful transit maps.

As long as the radius is big enough, we were guaranteed to have no degenerative cases. THE ALL NEW ADVANCED NATIONAL PARK MAPS ARE FOR HIKERS, CAMPERS, ADVENTURE SEEKERS, NATURE LOVERS COMBINED FOR ALL RECREATIONAL ACTIVITIES. This turned out to be surprisingly complicated, however, as we kept running into simple examples that would break our algorithm. After closer scrutiny, we hypothesized that Apple was drawing their maps manually. We also added a white overlay to reduce the contrast of the map layer below.

To our great joy (and relief), we finally launched it this summer: automatically-generated transit maps. When lines traced around corners, we wanted them to stay perfectly parallel, even in potentially degenerative cases, like in Chicago. Here’s what we did. While Google might not think it’s important, we certainly do. So that’s how we did it. So we used arc segments for rounding. Their lines are nicely rounded with smooth transitions at intersections.
You can easily get to the slopes or around town with our free public transit system that offers routes to The Village and all 3 base lodges. There, a large number of lines travel together around sharp curves, so drawing them in parallel could result in lines bunching on the inside of the bend. We came up with a custom algorithm that, given a shape, would remove and add points to round it off using circular arc segments. Now, all we had to do was make it look good. We also encountered many cases where multiple stops in the data actually corresponded to one single physical station, so we needed to collapse them into one stop. Here’s an example. So what did we do? So we created a dynamic programming algorithm which follows roads or tracks likely to be used by transit lines.

We launched with 55 cities. Once all of the lines were drawn, we used a ‘skeletonization’ process to successively thin the lines until each was only one pixel thick.

In the summer of 2015, after having worked on our maps for the better part of a year, we were finally ready to release our first version of Transit Maps. Fly, drive or ride. Usually rounding is done using bezier curves, which look like they’re easing into the curves. Many of the trips in the underlying transit data already contained shapes representing the routes that transit vehicles took. As long as two lines share the same stops, they are ‘snapped’ together. For stops with multiple lines, we drew a white bar with a black outline (for contrast) across all of the lines. Google had (and still has) a similar problem — except their lines criss-cross each other even where there are only stops and no intersections. But UI isn’t the only thing we’re about: our team extends well beyond expert designers, and our app is much more than just pretty. Sure, implementing automatically-generated transit maps took a lot of work, but it was worth it. Please see this News Post for information about Eastern Sierra Transit’s certification in Inyo and Mono Counties indicating state guidelines for safe operations have been met. So how does Google do it? This approach gave us something like this: The rounding only happens along shared segments. We had figured out a network that would allow us to draw lines in parallel.

Lines stay together for some distance before diverging. So how did we do it? Building our maps with shapes posed other problems: what should we do when an agency doesn’t provide any shape data, or if an agency provides very poor shapes?

We then processed the resulting network in order to get rid of the glitches introduced by the skeletonization. Find your best parking options at all base lodges, including general and preferred parking areas. Opening day for the 20/21 winter season is scheduled for November 14, conditions permitting.

Download Mammoth Cave National Park, KY App 3.5 for iPad & iPhone free online at AppPure. We’re hiring! You might also notice that we removed all the intersections. So while the lines were no longer merged, they stayed connected, maintaining the same topology. With sweat, tears, and creative thinking.

At the time, Google was the only player in the industry, and to be frank, their transit maps were kind of crappy. Then there’s our compression algorithm. Controls the functions of the fridge such as Temperature & Battery Monitor Settings, etc. So how did we manage it? What we had now, were a bunch of pixels denoting the skeleton, which meant we knew every line must be travelling along this skeleton, but we still had to figure out which lines were travelling where. This is the final result: To allow users to turn lines on-and-off selectively from our apps’ settings page, we decided that the rounding, as well as some stop-processing and rendering should be done on the device.

This allows Transit to download the schedules of the user’s entire region, store the data on the user’s device, and return search results… all in the amount of time it takes other apps to request and load a single schedule.

Here’s the final result: Pretty great, right? Then Apple rolled out their transit maps, and they were really pretty.

We extract the set of tracks the trip uses from OSM (grey lines). Feel like tackling challenges like this one for a living? Instead of processing the lines in vector space, we decided to to try something crazy.

We tried to compute the route separations in vector space, which seemed simple at first: take two lines that travel closely together and then find the centerline of the shared segment.


Directions to Courts Mammoth (Seremban) with public transportation. What’s funny is that when you zoom into the maps, you’ll see that Google often has data for the underlying railroad tracks, which begs the question: why don’t they combine the tracks with the shape data?

No matter your means of transportation, getting to Mammoth is easy from nearly anywhere. The resulting shape is smooth. Easy. You're welcome! Dealing with the intersections was a major problem because we had to ensure that each line continued from one segment to the next and linked up properly. Mammoth Mountain Ski Area 10001 Minaret Road, Mammoth Lakes, CA 93546 800.MAMMOTH (800.626.6684) Processing Chicago’s data took us hours.

They’re customizable, so users can turn on/off networks and modes to create their very own personalized transit maps.

And then… Apple Maps happened. That’s fast.

If we simply drew all of the shapes defined by all of the trips, we’d get a simplistic sort of transit map: Doing this was relatively simple: we set up a processing pipeline to extract the shapes from the source data; stored the shapes in a data-interchange format; ensured that the data was available to the device; and used the device’s mapping libraries to draw a new layer with the data. Although we now had the center lines of the network, we had destroyed more information than we’d gained. We figured, rather naïvely, that it would take about three months.

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