microPURC
A Python package for estimating the perturbed utility route choice (microPURC) model from trip-level data. It accompanies the paper:
M. Fosgerau, N. Nielsen, T. Rasmussen, and R. Yao. Estimating the perturbed utility route choice model with trip-level data.
📖 Documentation: https://andyyaor.github.io/micropurc/
Installation
pip install micropurc
Pre-built wheels cover Linux (x86_64, aarch64), macOS (x86_64, arm64), and Windows (AMD64) for Python 3.10 and newer; new stable Python releases are picked up automatically.
Building from source
A source build compiles a small C++ extension and needs a C++ compiler and CMake (Eigen is fetched automatically when it is not found on the system):
- macOS —
xcode-select --install, thenbrew install cmake - Linux —
sudo apt install build-essential cmake(or the equivalent) - Windows — Visual Studio Build Tools (the "Desktop development with C++" workload) and CMake
pip install -e .
The development tools (tests, linting, docs) install via ./scripts/dev_setup.sh.
Quick start
import numpy as np
from micropurc import (
Network, PIQPFlowSolver, DGP, DGPConfig, MarkovSampler,
od_uniform_all_pairs, MicroPURCEstimator,
)
# A 5x5 grid; its links carry three synthetic attributes (nonnegative, unit variance).
net = Network.grid(rows=5, cols=5, K=3)
net.attribute_names = ["length", "time", "toll"]
scale_m = np.ones(net.num_links) # quadratic-perturbation scale
solver = PIQPFlowSolver(net, scale_m)
# Simulate trips at a known beta, then recover it.
dgp = DGP(network=net, beta_true=np.array([1.0, 0.5, 0.8]),
forward_solver=solver, route_sampler=MarkovSampler(net),
od_dist=od_uniform_all_pairs(net), rng=np.random.default_rng(1),
config=DGPConfig())
data = dgp.sample_dataset(n_trips=20000)
est = MicroPURCEstimator(net, solver)
result = est.fit(y=data["y"], b=data["b"], beta_init=np.zeros(3))
print(result["beta_hat"], result["diagnostics"]["converged"])
# -> [0.993 0.498 0.798] True (recovers beta_true; N=20000 -> ~0.01 sampling error)
Documentation and examples
Full documentation is hosted at https://andyyaor.github.io/micropurc/:
- Getting started
- Examples — end-to-end pipeline, model specification, simulation, and run reporting
- API reference
Also in this repo: scripts/paper/ holds the paper's simulation experiments
(the network data ships in the repository), pytest runs the test suite, and
make -C docs html builds the docs locally.
Citing
If you use microPURC, please cite the paper above; citation metadata is in
CITATION.cff. The forward route-choice quadratic programs are
solved with PIQP (Schwan et al., 2023),
whose C++ sources are vendored in extern/piqp and compiled into the native
extension under its BSD-2-Clause license; no separate PIQP installation is
needed.
1 maintainer
Metadata
1 maintainer
Release files for micropurc 0.1.2
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| File | Size | Uploaded | |
|---|---|---|---|
| micropurc-0.1.2.tar.gz | 348.9 kB | Details |
Built distributions (wheels)
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