ch-router 0.2.2

Fast map matching and contraction hierarchy routing for Python

ch-router

ch-router is a fast (C++) Python package for GPS map matching and road-network routing, with no Python runtime dependencies. It bundles two complementary engines:

• Map matching (fastmm) — snap noisy GPS traces onto a road network using a Hidden Markov Model with a precomputed UBODT. It can interpolate time along the match (not just position) and matches as much of the trace as possible instead of failing on a single bad point.
• Contraction-hierarchy routing (routingkit_ch) — build a contraction hierarchy from a weighted graph and answer point-to-point shortest-path queries in microseconds, via Python bindings over RoutingKit.

Both are re-exported from the top-level ch_router package, so a single import gives you the whole toolkit.

It's built for map matching a lot of vehicle trace data quickly, without the infrastructure to spin up OSRM / Valhalla (and likely faster, since there's no IPC).

Source: ankushv-003/ch-router. This is a fork of kodonnell/fastmm, itself based on https://github.com/cyang-kth/fmm, updated to:

• Add Python helper classes for automatic trajectory splitting and time interpolation.
• Remove the GDAL/OGR dependencies — networks are created programmatically from Python.
• Add RoutingKit contraction-hierarchy routing bindings.
• Focus on Python packaging with distributable wheels (Linux + macOS).
• Drop STMatch — the focus is FMM.

Installation

pip install ch-router

Wheels are published for CPython 3.10–3.13 on Linux (manylinux_2_28) and macOS (11.0+).

Map Matching

The high-level FastMapMatch class manages the UBODT for you:

from pathlib import Path
from ch_router import FastMapMatch, MatchErrorCode, Network, Trajectory, TransitionMode

# Create and populate a network. Edge/node IDs are 64-bit for OSM compatibility.
# Geometry is a list of (x, y) tuples.
network = Network()
network.add_edge(1234567890123, source=10, target=20, geom=[(0, 0), (100, 0)])
network.add_edge(1234567890124, source=20, target=30, geom=[(100, 0), (200, 0)])
network.finalize()

# SHORTEST = distance-based. UBODT is generated/cached automatically.
matcher = FastMapMatch(
    network,
    TransitionMode.SHORTEST,
    max_distance_between_candidates=300.0,
    cache_dir=Path("./ubodt_cache"),
)

# A trajectory of (x, y, t) points. Use from_xy_tuples([(x, y), ...]) if you have no time.
trajectory = Trajectory.from_xyt_tuples([(10, 0, 1), (50, 0, 2), (150, 0, 3)])
result = matcher.match(
    trajectory,
    max_candidates=8,
    candidate_search_radius=50,
    gps_error=50,
)

# Collect the matched points:
matched_xyt = []
for sub in result.subtrajectories:
    if sub.error_code == MatchErrorCode.SUCCESS:
        for segment in sub.segments:
            for edge in segment.edges:
                for p in edge.points:
                    matched_xyt.append((p.x, p.y, p.t))

For time-based routing, set a speed on every edge and use TransitionMode.FASTEST. Otherwise the API is identical.

Sharing a UBODT across matchers (advanced)

FastMapMatch's cache_dir constructor is the easy path. If you want to manage the UBODT yourself — generate it once, load it, and share it across many matchers without touching the disk cache — use UBODTGenAlgorithm and the pre-loaded constructor:

from ch_router import (
    FastMapMatch, Network, NetworkGraph, TransitionMode, UBODT, UBODTGenAlgorithm,
)

network = Network()
network.add_edge(1, source=10, target=20, geom=[(0, 0), (100, 0)])
network.add_edge(2, source=20, target=30, geom=[(100, 0), (200, 0)])
network.finalize()

graph = NetworkGraph(network, TransitionMode.SHORTEST)
gen = UBODTGenAlgorithm(network, graph, TransitionMode.SHORTEST)

# IMPORTANT: embed the network hash so the pre-loaded constructor can validate it.
gen.generate_ubodt("ubodt.bin", delta=300.0, network_hash=network.compute_hash())

ubodt = UBODT.read_ubodt("ubodt.bin")
matcher = FastMapMatch(network, TransitionMode.SHORTEST, ubodt)  # shares this UBODT

If the UBODT's network hash, mode, or vertex count doesn't match the network, the constructor raises RuntimeError — that's why you must pass network_hash=network.compute_hash() when generating it for this path.

Contraction-Hierarchy Routing

Build a contraction hierarchy from a directed, weighted graph (flat uint32 arrays — NumPy arrays or plain lists), then run point-to-point queries. Weights are unsigned integers.

import numpy as np
from ch_router import ContractionHierarchy, CHQuery, INF_WEIGHT

# Graph: 0 -3-> 1 -4-> 2, plus a direct 0 -10-> 2.
tail   = np.array([0, 1, 0], dtype=np.uint32)
head   = np.array([1, 2, 2], dtype=np.uint32)
weight = np.array([3, 4, 10], dtype=np.uint32)

ch = ContractionHierarchy.build(3, tail, head, weight)   # 3 nodes
print(ch.node_count)                                     # 3

q = CHQuery(ch)
q.reset().add_source(0).add_target(2).run()
print(q.get_distance())    # 7  (via 0 -> 1 -> 2, cheaper than the direct arc of 10)
print(q.get_node_path())   # [0, 1, 2]
print(q.get_arc_path())    # [0, 1]  (indices into the tail/head/weight arrays)

# Unreachable targets return the sentinel INF_WEIGHT (2**31 - 1).
q.reset().add_source(2).add_target(0).run()
print(q.get_distance() == INF_WEIGHT)   # True

A CHQuery is reusable — call reset() between queries. The query keeps the underlying ContractionHierarchy alive for its lifetime, so it is safe to let the ch variable go out of scope while a query is in use.

Persisting a contraction hierarchy

Building a CH on a large graph is expensive; persist it once and reload it:

ch.save("graph.ch")                       # stable, portable CHB1 binary format
ch = ContractionHierarchy.load("graph.ch")

The .ch (CHB1) format is little-endian and version-tagged, so files are portable across machines. Note it is not interchangeable with RoutingKit's own save_file/load_file format.

Automatic Trajectory Splitting

For traces with gaps or failures, match() filters out troublesome sections and matches everything it can:

• Points with no nearby road candidate (tunnels, off-network) are skipped — you get the matched sections on either side. (Merge them yourself later if you want.)
• A break caused by a very long jump between two points (data issues, teleportation) likewise yields the matched sections on either side of the gap.

Because failures are dropped rather than reported in-band, the way to detect them is that the affected section is absent from result.subtrajectories (or the list is empty when nothing matched). Returned sub-trajectories carry error_code == MatchErrorCode.SUCCESS.

Interpolating Time

If your trajectory has timestamps, the match includes time as well — i.e. at what speed/time the vehicle moved along the matched geometry. Without per-edge speed, time is apportioned linearly along the matched geometry between two GPS points. With per-edge speed, it's apportioned correctly — e.g. between a 100 km/h edge and a 50 km/h edge of equal length, less time goes on the faster edge.

Understanding Delta Parameters

The delta parameter (max_distance_between_candidates or max_time_between_candidates on FastMapMatch) controls the maximum routing cost for precomputed paths in the UBODT.

SHORTEST Mode (distance-based)

• Units: same as your network (typically meters)
• Meaning: maximum road-network distance between GPS points
• Recommendation: 2–3× your expected maximum distance between consecutive GPS points
• Example: if GPS points are ~100 m apart, use delta=300

FASTEST Mode (time-based)

• Units: seconds
• Meaning: maximum travel time between GPS points
• Recommendation: 2–3× your expected maximum travel time between GPS points
• Example: for 200 m spacing at 50 km/h: 200 ÷ (50000/3600) ≈ 14.4 s → use delta=40

Trade-offs:

• Larger delta: better matching quality (more routing options), but larger UBODT and slower generation. (Too small and generate_ubodt can produce an empty table that read_ubodt rejects.)
• Smaller delta: faster generation and smaller files, but may fail to connect distant GPS points.

Understanding Reverse Tolerance

The reverse_tolerance parameter handles GPS noise that causes slight backward movement on the same edge. We operate on directed edges, so without this a backward jiggle would route to the end of the road and back down the opposite edge — making a stationary, jittery vehicle look like it's driving up and down the street.

How It Works

Edge traversal: the graph uses directed edges. Dijkstra routing always respects edge direction (source → target). For OSM bidirectional roads, add two edges (one per direction).

Same-edge positioning: when two consecutive GPS points match the same edge with the second at a lower offset (backward movement), reverse_tolerance decides whether it's allowed:

# GPS noise causes apparent backward movement:
GPS Point 1 -> Edge 1 at offset = 80 m
GPS Point 2 -> Edge 1 at offset = 50 m

# reverse_tolerance = 0.0:  transition has infinite cost -> rejected
#   (algorithm may match Point 2 to the opposite-direction edge, a fake U-turn,
#    or skip Point 2 in split mode)
# reverse_tolerance = 40:   backward movement = 30 m < 40 m -> allowed at cost 0

The Reversed Flag

When backward movement is allowed, the reversed flag records it. Geometry is automatically corrected to always go forward (low → high offset), so you never handle backward linestrings:

for segment in result.subtrajectories[0].segments:
    for edge in segment.edges:
        if edge.reversed:
            # Geometry already corrected to go forward; flag for QC if you like.
            print(f"Edge {edge.edge_id} had backward GPS movement (corrected)")
        for point in edge.points:
            print(f"  offset {point.edge_offset}: ({point.x}, {point.y})")

• reversed=True: GPS moved backward (offset1 > offset2), geometry auto-corrected forward.
• reversed=False: GPS moved forward normally (offset1 ≤ offset2).

No special handling is needed — geometry is always correct. Use the flag for quality control, statistics, or debugging.

Recommendations

Start with reverse_tolerance=0. If stationary-ish vehicles jump around, either pre-filter or try e.g. reverse_tolerance=20.

Routing Modes: SHORTEST vs FASTEST

Both modes balance the emission probability against the transition probability. The emission probability is how likely a candidate is the right edge given its distance from the GPS point — closer is higher. Tune it with gps_error: larger keeps emission high even for distant points.

SHORTEST Mode (distance-based)

The default. Routes on distance, matching by spatial proximity. Transition probability:

tp = min(euclidean_dist, path_dist) / max(euclidean_dist, path_dist)

Higher when the network path closely follows the straight-line GPS distance. If routes stick to the nearest edge regardless of feasibility, your gps_error is probably too large (and vice versa).

FASTEST Mode (time-based)

Routes on travel time; requires speed on all edges. Transition probability:

expected_time = euclidean_dist / reference_speed
actual_time   = sum(segment_length / segment_speed)
tp = min(expected_time, actual_time) / max(expected_time, actual_time)

Higher when travel time matches or beats the expected time (euclidean distance ÷ reference speed). If routes stick to the nearest edge, decrease gps_error or the reference speed.

Developing

Run the tests:

pytest .

Build and install from source (needs Boost and a C++17 compiler; OpenMP for parallel UBODT generation):

pip install -e . --no-build-isolation

Type stubs (.pyi) for the compiled extensions are hand-maintained under python/fastmm/ and python/routingkit_ch/; update them by hand when the bindings change (each stub notes this in its header).

Roadmap / Known Limitations

• A failed GPS point (too far, etc.) is excluded from the match rather than emitted as a zero-length segment with an error code.
• If a path isn't found in the UBODT, fall back to a plain Dijkstra lookup instead of bailing.
• max_distance_between_candidates may not be a hard limit in the UBODT — needs verification and possibly an extra check.
• The contraction-hierarchy bindings currently expose point-to-point queries; many-to-many (pin_targets) and RoutingKit's native save/load format are not yet exposed.

Third-Party Licenses

This package vendors source from the following projects under third_party/:

• RoutingKit — BSD-3-Clause. Source under third_party/routingkit/ with the original LICENSE preserved and shipped inside the wheel (routingkit_ch/LICENSE.RoutingKit). Compiled into the routingkit_ch._native extension.
• spdlog — MIT. Headers under third_party/spdlog/; used for logging in the C++ core.
• FiboHeap — LGPL-3.0. Header-only, under third_party/fiboheap/ with its LICENSE preserved; used in the routing core. Note the LGPL terms when redistributing.

See each project's directory under third_party/ for the full license text.