The initiative aims to encourage bicycle use in São Paulo by rewarding active users
Bike SP Project arose from the need to make urban mobility more efficient, inclusive, and environmentally sustainable. Its main idea is to encourage more people to use bicycles as a means of transportation in São Paulo, choosing them for daily commutes between home and work, school, university, and other routine destinations. To strengthen this incentive, the project provides cyclists with credits that can be used in public transportation (buses, trains, and the metro).
Researcher Fabio Kon, a member of ICoNIoT from IME-USP, is part of the team coordinating the project. He explained in detail how the initiative works and the technologies involved.
The core idea is to use an application (initially developed for Android) to track users’ bicycle trips and understand their behavior, generating insights that can inform the development of public policies. The project is not limited to producing data for a specific policy area; rather, it aims to serve as a general framework demonstrating how scientific research can support public policy for improving urban mobility in large cities.
How does it work?
During each trip, the app collects the user’s location every 30 seconds. Data from the phone’s accelerometer are used to verify whether the trip is actually being made by bicycle, which is determined through AI algorithms. In addition, GPS coordinates are essential: each segment of the trip has its average speed analyzed to confirm that the user is indeed cycling.
It is crucial for the system to verify that trips are genuinely made by bicycle, as the collected data determine the user’s compensation. Each week, a reward value per kilometer is randomly defined. At the end of the week, the system calculates the total distance traveled and multiplies it by the reward value to determine the amount each user receives.
Not all bicycle trips qualify. The project focuses on collecting data from essential daily trips, such as commuting between home and work, school, university, or public transport terminals. Users register these key locations, which are converted into latitude and longitude coordinates, and the system calculates the distances between them.
One of the project’s main goals is to understand how much financial incentive is needed to effectively encourage people to adopt cycling for essential daily travel. The reward values will be proposed based on these analyses.
Generated Data
The data collected by the Bike SP system will form a large dataset, which will be analyzed by a multidisciplinary team of statisticians, economists, and computer scientists.
The application is capable of collecting detailed data for each city block traveled by users. This enables the system to generate unprecedented insights into urban mobility in São Paulo. The resulting dataset will be unique and directly useful for city planning, particularly for expanding cycling infrastructure. Currently, São Paulo has around 740 km of bike lanes and paths, and the goal is to triple this network within ten years.
By revealing where cyclists travel and the challenges they face, the dataset can support decisions about where to expand bike lanes. Today, many cycling paths in São Paulo function like isolated “islands.” Because they are disconnected, cyclists are often forced to leave the bike lane and rejoin it later, which increases their exposure to traffic and reduces safety. Due to these safety concerns, women—who tend to be more cautious in traffic—are significantly underrepresented among cyclists in the city.
Technologies Involved
The Bike SP system communicates with the SPTrans system, informing the municipal system of the amount to be paid to each cyclist. In addition, the system integrates with several other applications.
For example, the TomTom API calculates distances between registered locations, ensuring routes are suitable for bicycles. Crashlytics is used to report errors in the app during usage, allowing system managers to address issues. Firebase Cloud Messaging (FCM) enables notifications to be sent simultaneously to all users. AppCheck helps prevent fraudulent trip registrations by verifying, through cryptographic methods, that the app in use is authentic, checking its digital signature.
Artificial Intelligence
Artificial intelligence is used in the project to determine whether a journey was actually made by bike: machine learning is applied to analyse the mobile phone’s accelerometer. In addition, an analytics dashboard is currently being developed to help the project team make sense of the 30,000 journeys. It will feature a dashboard for the organised visualisation of this data. In this analysis system, clustering techniques will be applied to detect patterns and group users into categories – for example, those most affected by the incentive will be identified, making it possible to understand for whom (which user profile) specifically the public policy will be developed.