Analysis of Bike Availability in Paris Office Zones

1. INTRODUCTION

   In recent years, the use of bicycles for daily commuting in Paris has significantly increased, particularly with the rise of public bike-sharing systems like Vélib'. With many Parisians now using bicycles as a primary mode of transportation to work, understanding the dynamics of bike availability across the city’s major office zones is critical. This project aims to analyze the availability of Vélib' bikes and docking stations in key business districts like La Défense, Opéra, Montparnasse, and others.

   The objective of this analysis is to uncover trends in bike usage and availability, helping city planners, businesses, and residents optimize their commuting strategies and enhance urban mobility. This document outlines the full scope of the project, detailing the data, methodology, results, and future directions.

2. PROJECT OBJECTIVES

   The primary goals of the project are:

   • To analyze the availability of Vélib' bikes (both mechanical and electric) across major office zones in Paris.

   • To understand the distribution of docking stations and available bike slots in these areas.

   • To track fluctuations in bike availability during peak office commuting hours and identify areas with potential shortages.

   • To create a foundation for future visualizations (e.g., animated changes in bike availability over a week).

3. DATA CONTENTS

   The analysis utilizes real-time GeoJSON data on Vélib' bike availability across Paris. The dataset contains information on:

   • Station Code: Unique identifier for each bike station.

   • Station Name: The name of the station.

   • Is Installed/Is Renting/Is Returning: Status flags indicating whether the station is operational and allowing bike rentals or returns.

   • Number of Docks Available: The number of available slots for docking bikes.

   • Number of Bikes Available: The number of bikes currently available at the station.

   • Bike Type: Breakdown of available bikes into mechanical and electric categories.

   • Geospatial Data: Latitude and longitude coordinates for mapping the stations.

   • Timestamp: The date and time at which the data was captured.

4. METHODOLOGY

   4.1 Data Collection

   The primary data source is the Vélib' bike-sharing system’s real-time data provided by Open Data Paris, which provides up-to-date information on bike and dock availability across the city. The data was loaded in GeoJSON format and processed using Python and libraries such as GeoPandas, Pandas, and Folium for geospatial analysis and visualization.

4.2 Data Preprocessing

Cleaning the Data: The dataset was filtered to remove any stations that were not installed or operational (i.e., stations not renting or returning bikes).

Clipping and Filtering: We focused on the major office zones in Paris and filtered stations based on their proximity to these zones (using coordinates and defining radii of influence around key districts).

4.3 Geospatial Mapping

Using Folium and GeoPandas, we plotted the locations of each station on a map, marking their proximity to office zones with 5km radius circles. Additionally, the stations were color-coded to reflect bike availability, with distinct colors for mechanical bikes and e-bikes.

4.4 Descriptive Analysis

Bike Availability: We calculated the total number of available bikes across all stations in the dataset, breaking it down into mechanical and electric bikes.

Dock Availability: We summed the available docks in these zones, providing insight into bike parking capacity.

Peak Usage Patterns: By identifying key office zones and their corresponding bike stations, we analyzed which stations experience high demand during peak hours (morning and evening rush hours).

Folium Mapping of the bike availability Data.

Condensing data and creating a layer for projecting the data onto a real map

5. Analysis and Results

5.1 Total Availability of Bikes and Docks

• Total Bikes Available: The dataset showed that there are 17,701 bikes available across Paris at the time of analysis, with a split of 11,033 mechanical bikes and 6,668 e-bikes.

• Total Docks Available: There are 26,599 docks available for parking bikes across all stations.

5.2 Distribution Across Office Zones

The highest concentrations of bikes were found near major office zones such as La Défense and Saint-Lazare, where commuters frequently rely on bike rentals for the "last mile" of their journey. These zones also showed fluctuations in availability during peak hours, indicating a potential need for better dock management.

5.3 Insights on Mechanical vs. Electric Bikes

Electric bikes, though fewer in number, are increasingly popular due to the ease of use on long commutes. In office zones like Montparnasse and Opéra, e-bike usage saw a marked increase, especially in the morning.

6. Visualizations

Several visualizations were created to map and highlight the findings:

• Geospatial Heatmaps: Using Folium, we generated heatmaps showing the density of bike stations and availability of bikes in real-time, color-coded by the type of bike (mechanical or electric). A condensed data was produced to plot it onto a real map and finally software's like QGIS and photoshop was used to finalize the visualization.

8. Future Work

Building on this foundational analysis, the next steps will include:

• Time-Series Animation: An animated visualization that tracks how bike availability fluctuates in office zones throughout the week. This can help identify peak hours and areas with chronic shortages.

• Predictive Modeling: Use machine learning models to predict bike demand in different zones, allowing better station and dock planning.

• Policy Recommendations: Based on the analysis, recommendations will be made to city planners regarding optimal locations for new bike stations and better redistribution of bikes during peak hours.

9. Conclusion

This project offers a comprehensive look at bike availability across major office zones in Paris, highlighting key trends in urban mobility. The data-driven insights can be valuable for improving infrastructure, planning for better bike-sharing services, and ensuring that commuters have access to efficient, sustainable transport options.

With future plans to expand this project into animated time-series visualizations and predictive modeling, this analysis lays the groundwork for smarter urban mobility solutions in Paris.