BalancedRoutePlanner

BalancedRoutePlanner is a Python package designed for smart city management and other extended applications. It provides functionalities for balanced clustering and route planning to optimize intra-cluster paths efficiently.

Features

1. Supports multiple data formats: CSV, XLSX, and SHP point data.
2. Balanced clustering: Uses balanced clustering methods to balance task allocation and route planning.
3. Structured data output: Provides structured data for secondary analysis and generates visualizations of clustering results and routes for intuitive inspection.

Installation

BalancedRoutePlanner requires Python 3.6 or higher. You can install BalancedRoutePlanner from PyPI using the following command:

pip install BalancedRoutePlanner

Alternatively, you can install BalancedRoutePlanner using the following method:

# Install using Git
git clone  https://github.com/SmartCitiesTransportationHub/BalancedRoutePlanner.git
cd BalancedRoutePlanner
pip install -e .

Usage

You can run test.py to test the BalancedRoutePlanner using generated data.

Example Code

Below is a simple usage example:

import numpy as np
from BalancedRoutePlanner import balanced_cluster, nearest_neighbor_2opt, visualize_clusters, visualize_paths

# Generate test data
def generate_data(n_samples, x_range, y_range):
    x = np.random.uniform(x_range[0], x_range[1], n_samples)
    y = np.random.uniform(y_range[0], y_range[1], n_samples)
    return np.column_stack((x, y))

# Test data and parameters
coords = generate_data(50, [0, 10], [0, 10])
num_clusters = 3
center_point = np.array([5, 5])

# Balanced clustering
clusters, balanced_model, labels, cluster_sizes = balanced_cluster(coords, num_clusters)

# Path planning
paths = []
for cluster_id, cluster_coords in clusters.items():
    path = nearest_neighbor_2opt(coords[cluster_coords], center_point)
    paths.append(path)

# Visualization
visualize_clusters(coords, labels, balanced_model.cluster_centers_)
visualize_paths(paths, center_point)

Parameter Descriptions

• balanced_cluster:

  • data: Array of data point coordinates.
  • num_clusters: Number of clusters.
  • tolerance (optional): Tolerance for initial assignment.
  • max_iterations (optional): Maximum number of iterations.
  • optimize_geo (optional): Whether to perform geographical optimization.

• nearest_neighbor_2opt:

  • coords: Array of data point coordinates.
  • start_point: Starting and ending point of the path.

• visualize_clusters:

  • coords: Array of data point coordinates.
  • labels: Cluster labels for each data point.
  • centers: Coordinates of cluster centers.

• visualize_paths:

  • paths: List of optimized paths for each cluster.
  • center_point: Starting and ending point of the paths.

Support

If you have any issues or if you want to contribute to BalancedRoutePlanner, please open an issue or submit a pull request on GitHub!

License

BalancedRoutePlanner is open source and licensed under the MIT license.