TNR-CH

Implementation of Transit Node Routing + Contraction Hierarchies, inspired by Transit Node Routing Reconsidered. This implementation includes Contraction Hierarchies, transit node selection, and Search Space Based Locality Filter, and Graph Voronoi Label Compression.

Usage

Let the code speak.

package main

import "fmt"

func main() {
	g := Graph{}

	// g.AddVertex(<vertex name>)
	g.AddVertex(int64(0))
	g.AddVertex(int64(1))
	g.AddVertex(int64(2))
	g.AddVertex(int64(3))
	g.AddVertex(int64(4))

	// g.AddEdge(<source vertex name>, <target vertex name>, <length>)
	g.AddEdge(int64(0), int64(1), 1.0) // Note that AddEdge only add an uni-directional edge
	g.AddEdge(int64(1), int64(0), 1.0) // If one needs bi-directional edges, simply add another direction
	g.AddEdge(int64(1), int64(2), 2.0)
	g.AddEdge(int64(2), int64(3), 3.0)
	g.AddEdge(int64(4), int64(2), 4.0)
	g.AddEdge(int64(2), int64(4), 2.0)

	g.ComputeContractions() // Compute the contractions before using contraction hierarchies
	g.ComputeTNR(2)         // Compute transit nodes before using TNR algorithm, 2 stands for the amount of transit nodes

	distanceCH, pathCH := g.ShortestPathWithoutTNR(int64(1), int64(4)) // Compute shortest paths without using TNR
	distanceTNR, pathTNR := g.ShortestPath(int64(1), int64(4))         // Compute shortest path using TNR if possible, fallback to CH for local paths
	distanceDijkstra, pathDijkstra := g.Dijkstra(int64(1), int64(4))   // Naive Dijkstra

	fmt.Printf("Shortest path using CH: %v, %f\n", pathCH, distanceCH)
	fmt.Printf("Shortest path using TNR+CH: %v, %f\n", pathTNR, distanceTNR)
	fmt.Printf("Shortest path using Dijkstra: %v, %f\n", pathDijkstra, distanceDijkstra)
}

Benchmark

Query 1,000 randomly chosen path with 1,000 transit nodes.

On Taipei's roadmap:

vertexCount: 48753
edgeCount: 62157
Compute Contraction Hierarchies took 4.464321111s
Compute TNR took 13m14.049223101s
Query using Dijkstra took 12.859151479s
Query using Contraction Hierarchies took 820.613105ms
Query using TNR took 264.495739ms

Reference

The code of Contraction Hierarchies is inspired by LdDl/ch. The method is from Arz et al.

License

MIT License

TODO

1. The TNR Computation can be faster since the method now is quite naive and it does many redundant calculation.
2. Memory efficiency.
3. Change int64 to int.
4. Import and export computed graph.
5. go benchmark, currently you can call ComparePerformace to do the benchmark.