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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):

  • macOSxcode-select --install, then brew install cmake
  • Linuxsudo 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 standardized synthetic attributes.
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 (with the benchmark network data bundled), 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.

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