Trancity combine the use of cutting-edge technologies in the computing area, such as cloud infrastructure, platforms and techniques for processing huge amount of data (big data of real time location and ticketing system from thousands of buses, alerts such as flooding, traffic accidents, and video monitoring), the use of machine learning algorithms for prediction and automation of data analysis and recent techniques to protect privacy in the processing of personal data.

Trancity also has a module that estimates the volume of greenhouse gas and air pollutant emissions, from each bus circulating through the city, based its average speed and details about the vehicle (such as its engine technology, and type of fuel). The platform is offered as a Software as a Service, with unlimited number of users for a given organisation, and every new feature implemented is available for all users and clients.

Our platform has already been used by 15 different cities in Latin America and Europe and we are ready to serve new customers. Today we are working on the implementation of new functionalities such as the automation of analysis of origin-destination and passenger flows, using ticketing data.

BHTrans officials, our partners in this pilot, after a first period using Trancity, asked for customizations in our platform to be made and facilitate their daily tasks, such as the visualisation of all traffic lights in the city, the flood risk areas, the analysis by user-defined areas/polygons and not only by corridor. All these customizations are very important because we are addressing the real necessities of the ones who need those information.

Citizens benefit from a more reliable and fair public transport, and information once the analysis was used to communicate with them at Twitter. BHTrans reported that they make modifications to the transit lines quicker and now are able to measure the impact of proposed actions, for instance traffic lights time and itinerary modifications. Also, they were able to further charge the terms of the contract, as they had evidence based on data, representing an efficiency gain for public management.

• Better transit planning for public managers: origin-destination matrices directly from the ticketing system (instead of one matrix every 10 years, with traditional methods).
• Better communication with population: i.e. CO2 and pollutants emissions measurements in real-time, and comparison to the sustainable development goals (instead of estimations using a large-scale approach).
• We measured the number of buses monitored on our platform, which today stands at 28,000, which corresponds to 16 million passengers and a total of more than 56 million citizens who can benefit from improved services, planned from the use of the platform.

One challenge was to adapt the bureaucratic processes with the dynamics of the pandemics and the speed of information provided by Trancity. Even though public managers were able to detect lines where the bus offer was not adapted to demand, the bureaucratic process required to modify service orders introduced huge delays on the adaptation of public transport during pandemics. The lessons learned will inform the next bidding process so that new contracts can be more easily and dynamically adapted.

There was also a challenge related to integrating Waze data into Trancity. Even though we had already worked with Waze before, Waze does not yet provide licences for private partners. We would have to be a sub-licensee for the city of Belo Horizonte, which didn't happen in time for the project despite our efforts. We contacted Waze about this case and they are trying to support new licensing modes so that private partners can work along with cities using Waze data to solve traffic problems.

It is very important that public authorities own transit data so they can use and manage as it is more convenient for the population. Indeed, to make Trancity work it needs two types of data: Automatic Vehicle Location (AVL) and a General Transit Feed System (GTFS). In many cities where private operators run the bus network, the granting authority doesn’t have access to the AVL data, whether because the private operator owns it, or only because it is not clearly specified in the terms of the contract that the AVL data should be published as open data.

Also, most of the cities don’t yet have the public transport data in a structured GTFS format. It can be very time and cost consuming to produce it from scratch. It is also important that there is continuity over different mandates, that technical teams are able to rely on high-quality systems independently of the mayor party. That the solution implemented is not discontinued from a term to another.

Our innovation was firstly designed to meet the demand of the São Paulo Municipality, for better management and control of their bus network. Since then, our innovation has been deployed in more than 15 cities in Latin America and Europe, providing a high-quality solution for cities varying from 150,000 to more than 12 million inhabitants. In its actual state, Trancity fits in any city in the world, and as it is in constant evolution, with new features being developed constantly, there is an immense potential to be continuously replicated to more and more cities, because Trancity uses data in a global standard format.

This project allowed us to learn a number of things. First of all, bus contracts must be quickly adapted to a new reality of more dynamic cities, where the demand for transport can vary drastically from one day to the other. This is a fundamental issue if we want to use data to have bus networks better adapted to citizen demands. We also learnt that the possibilities of using data to improve traffic and transport management are endless. The city of Belo Horizonte already had other demands for data integration such as using Waze data to compare bus traffic with car traffic and integrating bus speed data with semaphore programming to automatically give priority to buses to improve operational speed.