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, then brew 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/:

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), which the pre-built wheels bundle under its BSD-2-Clause license.

Project links

Data verified by PyPI on Aug 10, 2026
Data provided by the project maintainers, verified at the time the release was uploaded to PyPI.

Key dates

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  • Released:
Newer release available (0.1.2)

1 maintainer

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Avatar for andyyaor from gravatar.com andyyaor

Credits

Author: Rui Yao

GitHub Statistics

Data verified by PyPI on Aug 10, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License expression

MIT
View SPDX License List

Requires

Python >=3.10

Provides Extra

dev docs

Tags

route choice discrete choice transportation econometrics perturbed utility network

Classifiers

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