GPS Visualizer will take a Google Map route (url) and convert to .gpx
"You can ignore most the options, just select Gpx and paste the
Google Maps URL into the box labelled “provide the URL of a file on
the Web” and then press the Convert button"
Often it is good to address the need that is stated rather than answering the question that was asked. I would like only to point out that there is a well-known parallel solution that neatly circumvents all the technical computing issues: Santa has helpers. These agents work asynchronously and independently to identify the houses that need visits and carry ...
I haven't been up to speed on the research or best practice on this so forgive me if I miss anything and it's been 3 years since I worked with a Travel Demand Model. And when I did travel demand models, I didn't spend a whole lot of time and effort into building turning penalty/restriction models.
Turning restrictions and penalty settings (TR/TP)...
To export a route to KML you'll have to use Google MyMaps.
add a route to new or existing layer
drag and drop the route to suit your needs
Open the maps options menue (3 dots above the layers)
Export to KML
You can then use any service to convert the KML to GPX. I prefer GPSies.
Contraction Hierarchy is a very fast algorithm:
This algorithm is RAM friendly while executing a query (to hold a contracted graph some more RAM is necessary as well as massive preprocessing)
There are some other algorithms - including the ones that solve public transit routing:
I have not used ArcGIS Schematics for more than some quick demos quite a few years ago, but there is a blog posting on Create route maps with the ArcGIS Schematics extension that may provide a solution.
Just to close this loose end, since I asked the question a new package was released called osmar which contains a vignette of how to implement shortest path algorithms in R using Open Street Map data: http://osmar.r-forge.r-project.org/ . It uses the function get.shortest.paths from the igraph package.
Excellent article on this can be found here:
I'm currently exploring the same problem as you, for the purpose of research paper. Before I started to test these two databases, I had the same presumption as you. That Neo4j graph database would be perfect solution for this kind of problem. And partially it is, but with lot of problems.
First problem is that A-Star is only implemented if you are using ...
I think that the type of software you are looking for is called a chart plotter software. There are several solutions used in navigation, where using a laptop is more and more common. I will not list here the solutions using dedicated hardware (AIS/navigation systems for example).
Amongst paying options you have the "best seller" called MaxSea (http://www....
The link given by MappaGnosis is the first attempt to implement Graph theory algorithms in Python (by Guido van Rossum, the creator of Python).
Since, many modules were developed:
One of the most comprehensive is NetworkX, mentioned before in GS
it can read or write shapefiles natively (thanks to bwreilly in ...
the grass algorithm v.net.alloc can produce the subnets - you can call it from the Processing toolbox (tested in QGIS 2.16)
You'll need a point layer (for facilities) and a lines layer with costs (either time/length). It'll create a new line layer with a field called cat added, which will be the id of the nearest facility.
Here's an example based on ...
You can use GraphHopper for that task, which also supports different mode like walking or biking and uses OpenStreetMap per default. You'll need some Java coding which explores the road network from the starting point similar to how the Dijkstra algorithms works but then you can get something like the following even in real time (<0.5s):
The code will ...
It may seem like laziness on the part of Watershed tool developers to stick with the simplest and oldest flow algorithm, D8, but there is a very sound reason for doing so. The difference between the D8/Rho8 flow algorithm and the more advanced algorithms that you mention (e.g. D-infinity) is mainly in their inability to represent the dispersion of overland ...
You could have a look at the Targomo API (formerly Route360˚), a pretty simple but powerful JS library which you can use with Leaflet (or even Google maps if you like).
It adds travel time polygons to your map for the travel times you require (e.g. 10, 20, 60 minutes) and for the following travel modes: walk, bike, car, transit.
There are quite a few ...
Okay, I looked further into the idea of Steve above. I'll try to demontrate his idea in a QGIS/PostGIS/pgrouting environment. You will get results such as this:
First, let's assume you have a geodata table with your shelves/obstacles looking like this (I made them up for this purpose):
Make sure your shelf data is in a projected coordinate system with ...
OSM has a page dedicated to routing, which is worth going over:
There is a special tool for importing OSM data into a PGRouting system and generating the required structure:
Lastly, there is a workshop tutorial on getting routing working with OSM data here:
The only practical way is to add the 'missing' routes the data yourself. OSM probably shouldn't be putting parking lots into its walking routes. There are liability issues with adding routes that aren't real, properly maintained pedestrian paths. A parking lot, though walkable, could be dangerous and could be private property. You'll have similar issues with ...
This is something you can probably solve by using the Warshal's or Dijkstra's algorithm
Although the number of houses in the world is way too big it would take a long time to compute that, I think this is a good initial point. Now I don't have the time to explain them but i give you an initial point. I'll go out with my family now and maybe I'll go back to ...
We are working on a multimodal routing for Austria (also for pedestrians). What I can say till now:
You need the data: It took at least 4 years and even longer to collect all the necessary walkways, barriers, steps, opening times, streets, railways, bikeways, ferrys, and, and and...and its still going on
You need a router which can interprete theses graphs ...
I think you need to build another table that defines all the routes in is as combinations of other routes. Then you query this table and join to the actual routes to get the geometry.
If the query is for 'from station' to 'to station' and each section has a 'from station
and 'to station'. But you want to include routes that take in multiple sections, you ...
As far as I know it's not possible to solve for alternate routes without some additional input or change to the analysis. In a network, given a particular impedance, there is only one shortest route between two points. As soon as you start looking for alternates without any additional input you've essentially removed the 'shortest' constraint and are back to ...
As an answer to both Uffe Kousgaard comments about "what the 18GB file contains" compared to a routable shapefile, and a possible answer to this question:
You don't explicitly state it, but I guess you used the ArcGIS Editor for OpenStreetMap to convert your data. If not, I really recommend to have a look at it, as it contains a dedicated option to create ...
The following is what I am using. Some of it is specific to our deployment environment since we are using docker and some bash scripts to deploy and set up the server. You could easily get rid of all the argeparse/os.getenv and hardcode the connection if you wanted.
from os import getenv
parser = argparse.ArgumentParser()
It is free and open source and coverage is pretty good. You can find a lot of different tutorials on how to use it on the website.
I solved this problem by indeed adding a temporary node on the clicked edge and adding 2 temporary edges to this temporary node. By manually joining these temporary node and edges before calling shortest_path_astar they are being used for calculating the correct path but they dont clutter your database with extra records that are only interesting for 1 ...