osm_ch

Path optimizer

A tool that uses graph contraction hierarchies to speed up shortest path calculations on OpenStreetMap data

contraction hierarchies

GitHub

17 stars
4 watching
5 forks
Language: Rust
last commit: 8 months ago
Linked from 1 awesome list

contraction-hierarchiesdijkstra-shortest-pathopenstreetmap

Backlinks from these awesome lists:

Related projects:

Repository Description Stars
udst/pandana Fast network analysis library using contraction hierarchies for accessibility metrics and shortest paths 387
easbar/fast_paths Efficient shortest path calculation algorithm 271
cosmwasm/optimizer Automated compiler for generating optimized Wasm binaries from Rust contracts 123
ezheidtmann/polargraph-optimizer An optimization algorithm for arranging drawing paths to reduce travel distance in a graphical plotter 57
mandrean/cw-optimizoor Optimization tool for compiling and optimizing CosmWasm smart contracts. 50
jycouet/kitql A collection of standalone tools to speedrun web applications built with GraphQL and Svelte 407
ndreckshage/cssvacuum A Chrome extension tool to optimize critical rendering path by analyzing CSS usage and providing inlined and cached versions of critical styles. 45
mengrao/str An optimized string class with an adaptive hash table for fast searching and comparisons 125
max22-/uxnbruteforce Automates the construction of optimized UXN code by searching through all possible combinations to find the most efficient solution. 8
wkhere/eastar An algorithm for finding the shortest path in a graph 32
hyperopt/hyperopt-sklearn Automates search for optimal parameters in machine learning algorithms. 1,588
addyosmani/critical-path-css-tools Tools to help prioritize and inline critical CSS for better page performance 1,153
datacanvasio/hypergbm An AutoML toolkit designed to automate the entire machine learning process pipeline for tabular data 337
sigkill79/sts A header-only library for optimizing vertex cache sizes of triangles. 62
benedekrozemberczki/gam An implementation of a graph classification model using structural attention and PyTorch 268