Enhancing Urban Mobility

Analyzing Bicycle Sharing for Effective Transportation in Lisbon

About the Project

Lisbon implemented a bike sharing system in 2019 as part of the initiative to become the green capital of Europe. The goverment project aims to promote sustainable transportation and enhance the usability of shared bicycles.

By undertaking this personal project, I aim to apply my data engineering and analytical skills to drive positive change in urban mobility and contribute to the ongoing efforts towards a greener future, analyzing demand patterns and gaining insights to optimize the bike sharing system.

My analysis provide valuable insights to the Lisbon government and GIRA operators, enabling them to make data-driven decisions regarding bike deployment, station planning, and overall system optimization. Ultimately, my goal is to contribute to the success of the GIRA bike sharing project and support Lisbon's vision of becoming a greener and more sustainable city.

Dataset

In this project, I utilize publicly available data from Lisboa Aberta which includes information about GIRA bike sharing stations in Lisbon. The data consists of the following key attributes:
  • Station position: The geographic location of each station in Lisbon.
  • Number of docks at the station: This indicates the total number of docking spaces available at each station. This value is constant for each station.
  • Number of bicycles: The count of bicycles currently available at each station. This information provides insights into the bike availability and can help analyze usage patterns.
  • Timestamp of update: The date and time at which the information about the station was last updated.

    • Media

    The data preprocessing steps, including cleaning, aggregating, and transforming the data, were implemented using Python scripts. The code for this data preparation process is readily available on my page.

    Bike Availability Visualization and Insights

    My project features a dynamic and interactive Tableau visualization that presents the mean distribution of bicycles per station, highlighting patterns based on the time of day and the day of the week.

    Through the Tableau visualization, users can explore the fluctuations in bike availability throughout the day and week, observing trends and identifying peak periods of usage according to the region.

    Insights

    Based on the number of available bicycles, we can draw some pattern insights:

  • In general, the number of bikes in use at the same time is higher during the week, with a minimum of available bicycles at 17:00.
  • Looking at all stations on weekdays, there is a reduction in the number of available bicycles from 7:00 to 9:00 and from 16:00 to 19:00. This indicates that people typically use the sharing bicycle for their daily commutes to work or school.
  • During the weekend, the distribution is more uniform, with higher usage from 9:00 to 22:00. We can conclude that the bicycles are also used for recreational and transportation purposes during weekends.
  • The information regarding the start and end points of each trip is not available, but we can observe a clear increase in the availability of bicycles at Saldanha, Picoas, and Avenida regions during the morning and a decrease by the end of the day on weekdays.
  • There is no clear pattern of distribution variability by region during the weekend.