From the very start, the team is committed to a data-optimized public transportation system. Bike Share Toronto real-time data feed caught our eyes as we delve into means of shared economy in transportation: some stations are overflowed with returned bikes while others emptied from rush hour. Such discrepancy contributes to a drop to ridership availability, and requires additional load-balancing effort to fill. With its vast amount of open data, we set off to create an intelligent reservation system to deliver higher service availability, efficient load balancing and lower maintenance cost for Shared Biking industry.

What it does

Based on a tiered membership database of riders, the system identifies frequent, predictable behaviour of riders who utilize the service on certain time slots. The ability to reserve bikes from a particular station on a monthly basis will be available for year-pass holder; a one-time reservation will be available to General Members. Unregistered users who only pay one-time fee to access the bike.

This system allows Toronto Parking Authority, which oversees the bike share program, to efficiently allocate resources. For example, more bikes coule be moved overnight along popular routes to ensure that no year-pass riders lack bike to work or school. This promotes rider loyalty, and makes Toronto Parking Authority a green alternative for commuting.

How we built it

Databases: Toronto Parking Authority's rideshare usage database (containing detailed information on each individual ride, such as starting time and location) Toronto Parking Authority's rideshare station database (JSON format, contains information on all stations, such as number of bike racks available)

Python: Python was used to read and analyze the databases, and generate a chart that shows the number of bikes available at each bike station, for ten-minute windows throughout the day.

Plotly: Plotly was used to visualize the data. Owing to computational limitations, we were only able to generate a subset of graphs (for a few days, for a few stations), but this can be easily scaled to all stations in GTA.

Challenges we ran into


Accomplishments that we're proud of

We make this!!!

What we learned

Databases, Python

What's next for inteliREZ Bike Share

We're excited to have the possibility of implementing it!

Built With

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