PyTDLM 0.1.0

Systematic comparison of trip distribution laws and models

PyTDLM: Systematic comparison of trip distribution laws and models

A Python port of the TDLM R package, with numpy-based implementations and parallel processing support for multiple exponent values.

Overview

PyTDLM provides implementations of several trip distribution models commonly used in transportation planning and spatial analysis:

Available Laws

• GravExp: Gravity model with exponential distance decay
• NGravExp: Normalized gravity model with exponential decay
• GravPow: Gravity model with power distance decay
• NGravPow: Normalized gravity model with power decay
• Schneider: Schneider's intervening opportunities model
• Rad: Radiation model
• RadExt: Extended radiation model
• Rand: Random model (baseline)

Available Models

• UM: Unconstrained Model
• PCM: Production Constrained Model
• ACM: Attraction Constrained Model
• DCM: Doubly Constrained Model

Installation

Using conda

conda install -c conda-forge pytdlm

Using pip

pip install PyTDLM

From source

git clone https://github.com/PyTDLM/TDLM.git
cd TDLM
pip install -e .

Quick Start

import numpy as np
from TDLM import tdlm

# Prepare your data
mi = np.array([100, 200, 150])  # Origin masses
mj = np.array([80, 180, 120])   # Destination masses  
dij = np.array([[0, 10, 15],    # Distance matrix
                [10, 0, 8], 
                [15, 8, 0]])
Oi = np.array([50, 80, 60])     # Out-trips
Dj = np.array([40, 90, 50])     # In-trips
Tij_observed = np.array([[0, 25, 25],  # Observed trip matrix
                         [30, 0, 50],
                         [35, 35, 0]])

# Run simulation
exponent = np.arange(0.1, 1.01, 0.01)
results = tdlm.run_law_model(
    law='NGravExp',
    mass_origin=mi,
    mass_destination=mj, 
    distance=dij,
    exponent=exponent,
    model='DCM',
    out_trips=Oi,
    in_trips=Dj,
    repli=100
)

# Calculate goodness-of-fit
gof_results = tdlm.gof(sim=results, obs=Tij_observed, distance=dij)

# Print results for a given exponent
print(gof_results[0.1].to_markdown(index=False))

Documentation

For detailed documentation and examples, visit: https://rtdlm.github.io/PyTDLM/

License

This project is licensed under the GNU General Public License v3.0 - see the LICENSE file for details.

Citation

If you use this library in your research, please cite:

@software{lenormand2024tdlm,
  title={TDLM: Trip Distribution Law Models},
  author={Lenormand, Maxime},
  year={2024},
  url={https://github.com/RTDLM/PyTDLM}
}