Ptolemy 🗺️🐍🦀

This project exposes an API that calculates the shortest path in the road network, using data from OpenStreetMap.

Why

This a personal adventure with Rust, threads, graphs, memmap and HTTP API. Of course, there are other projects that do more or less the same thing, with different trade-offs and production-readiness, but this one, this one is mine!

Overview

TODO: show some nices examples

Project status

In development, feel free to join! Main features and future roadmap:

Usage

The process starts by downloading the raw OpenStreetMap data. A good source is the pre-packaged data from GeoFabrik. You will need the *.osm.pbf format

Execute the generator to extract the data from the raw format and create the final graph. For example, for Brazil:

$ cargo run --release -- generate -i data/brazil-latest.osm.pbf -o data/brazil.ptolemy
[   0.0s ( +0.0s)] Will use 16 threads
[   0.0s ( +0.0s)] Loaded 17.5k blobs from 835.2MiB
[   0.2s ( +0.2s)] File has 16.1k nodes blobs, 1.4k ways blobs and 26 relations blobs
[   3.0s ( +2.8s)] Found 6.8M junctions and 23.5M internal nodes from 3.7M ways
[   4.4s ( +1.4s)] Loaded info about 30.3M nodes, of which 16.0k are barriers
[   6.8s ( +2.4s)] Create graph with 30.3M nodes and 16.8M edges
[   9.1s ( +2.3s)] Pruned unreachable nodes
[   9.1s ( +0.0s)] Graph now has 6.5M nodes (-23.7M) and 16.4M edges (-355.5k)
[  10.5s ( +1.5s)] Weakly-connected components were strongly connected
[  10.5s ( +0.0s)] Graph now has 16.5M edges (+70.9k)
[  14.9s ( +4.4s)] All smaller components were strongly connected with the main one
[  14.9s ( +0.0s)] Graph now has 16.5M edges (+172)
[  25.5s (+10.5s)] Wrote results to data/brazil.ptolemy, size = 76.4MiB
[  25.5s ( +0.0s)] Done! #DFTBA

Execute the api to serve the resquests with cargo run --release -- api -i data/brazil.ptolemy

API

The API is a small and compatible subset of the OSRM API, offering the following endpoints:

/route

Example:

Request: http://localhost:8000/route/v1/driving/-47.015856,-22.938538;-46.555678,-23.110895

{
    "waypoints": [{
        "location": [-47.016013, -22.938557],
        "distance": 16.21533725273027
    }, {
        "location": [-46.555669, -23.110821],
        "distance": 8.279745312178644
    }],
    "routes": [{
        "distance": 65118,
        "geometry": "~d_kC`y}}GxHk@ePlA]Zs@r@g@d@kA@iC@gC\\qCCq@xAiDlIe@hAQn@On@}CzJe@dRFfGX`HkAZ|A`HwAnIp@DCnDeD|G`@h@oA|Fm@fCmANoAmEs@iBsAkDg@sAs@oBSDeCaAwC_JoAy@yHuGsBuCa@g@kDg\\uAmEiCu@{@w@yDuDeI_Is@uA_@@[@m@@uFg@}@MuCc@wBoGUo@{CeI{@eCCiE_AoFb@iDiM@}FYgCYo]mHcASwLiEs[}T|@mNvK_}@`m@itBzVyf@fGel@Ko@WaBeBqNMiAaBmRhzAwbApS}OPe@dCeGjLiy@oAgUG{@_D}YmMaoAdf@idBi@oOCy@O_PxhAq|ApT{_@jMaVnF{IRa@jDaInBmDvHmNJSjKoRtDkHbAoBjTw[va@g\\h\\yWzF}InAiChMcYzf@{fAlTkkAhCeNrHk[bDaH`AgB`BmD~D_Iv@yArEoI~pAy_Dl@WnJ}Cz~@a`ARUvo@s]jLmZnLkcA`GeNd@gAXo@rT}oChByVF_A`A}Thb@_zCbXo`@jKmOz@oAza@el@nE}G`f@kt@dMwVzMgRzf@_Yx_@_Sn_@{Rt|@mf@bD{D^a@~F}J~DqNpD_TLs@zFm\\|C}RzA{LZ_Dd@oELqAtCiaA?qB?i@?wAOoM_AmmAy@ac@y@kSEw@KeCIoBYsIScFAQoCoq@OkEhHkDxAAYtC~M{Bf@BzKpCNHjAbAhBl@tC|@`@JfB`@tC\\?Q?q@vEmBhCa@RiE"
    }]
}

Data format at rest

The cartography data is stored in a binary and compressed format in a single .ptolemy file

Its contents, once decompressed with ZLIB would yield a binary sequence formatted like:

{
    num_nodes: u32,
    num_edges: u32,
    node_latitudes: [i32; num_nodes],
    node_longitudes: [i32; num_nodes],
    edge_sources: [i32; num_edges],
    edge_targets: [i32; num_edges],
    edge_distances: [i32; num_edges],
    edge_road_levels: [i32; num_edges],
}

All the list fields are delta-encoded and once decoded will be strictly non-negative. That is, the i32 is used only to encode possibly decreasing values.

The nodes are sorted by (latitude, longitude) and the edges by (source, target).

Both latitude and longitude are stored as 1 / 1 000 000 of a degree. The distance is stored in meters and the road level is a value from 0 (main roads) to 5 (smaller roads).

Development

Install Rust 🦀. As of the time of this writing, you'll need the nightly version.
Install miniconda
Prepare the Python environment with conda env create then conda activate view-graph
Compile and install the Python native module with VIRTUAL_ENV="$CONDA_PREFIX" maturin develop -m py_ptolemy/Cargo.toml --release
Start the notebook server with jupyter notebook

curtisy1 / ptolemy