GPS Data and Bike Trip Analysis

This repository aims to provide tools for visualizing and analyzing GPS data, particularly focusing on bike trip data and trajectories. Description of below Python scripts:

1. bike_trip.py

This script contains a class BikeTrip for solving problems related to bike trip data. Here are some of the functionalities provided by this class:

• Processing bike trip data
• Calculating statistics such as maximum trip duration, minimum trip duration, count of minimum trip duration, and percentage of circular trips
• Filtering data based on time range
• Finding feasible trips and unique depots
• Generating graphs and shortest routes on maps

How to use bike_trip.py

To use the functionalities provided by BikeTrip class, follow these steps:

1. Ensure you have the necessary dependencies installed (pandas, numpy, matplotlib, geopy, networkx, osmnx, PIL).
2. Import BikeTrip class from bike_trip.py.
3. Create an instance of BikeTrip by passing the path to your bike trip data CSV file.
4. You can then call various methods of BikeTrip class to analyze and visualize the data.

2. gps.py

This script contains two classes: GPSVis for visualizing GPS data on a map and GPSdata for analyzing GPS trajectories. Here's what each class does:

GPSVis

• GPSVis class provides methods for plotting GPS data on a map image.
• It allows customization of the map output, including saving the map to a file.
• The class takes GPS records and a pre-downloaded OSM map image as inputs.

GPSdata

• GPSdata class offers functionalities for processing GPS trajectory data.
• It calculates the distance traveled by each user in the provided dataset.
• It filters GPS data points for a specific geographical area (e.g., Beijing) and exports them to a CSV file.

How to use gps.py

To utilize the functionalities provided by GPSVis and GPSdata classes:

1. Ensure you have the required dependencies installed (pandas, numpy, matplotlib, geopy, PIL).
2. Import the necessary classes from gps.py.
3. Create instances of these classes, providing the required input parameters.
4. Utilize the methods provided by these classes to visualize and analyze GPS data.

Usage Examples

• Example usage of both scripts is demonstrated in the if __name__=="__main__" blocks within each file.
• You can refer to these examples to understand how to use the functionalities provided by each script.

Results

• Shortest Path ( origin_node, destination_node, method = dijakstra)

• Beijing OSM map

• Bike trip traffic on Beijing map

Note

Ensure that you have the required data files (due to large size I am not pushing here) and map images in the specified paths before running the scripts.

Feel free to explore and customize these scripts according to your specific requirements!