Toertje (Elisabetta Jarova)

Toertje aims to enhance its bicycle data platform into an advanced service that promotes a modal shift towards cycling, and supports policy makers with data-driving cycling insights. A combination of historical, semi-dynamic and real-time data permits Toertje to offer personalized route advice for cyclists as well as powerful analysis tools for governments and infrastructure managers.

The platform’s goal is to promote bicycle safety, predict mobility trends, and optimize cycling infrastructure. The developed enhancements and its impact will be evaluated through a pilot study in Zwolle, a city with high cycling rates. The project thus contributes to sustainable urban mobility and informed policy decision-making.

The consortium consists of Toertje (project lead), Mobycon, University of Twente (Transport Studies Group), and Monotch, thereby combining expertise from mobility, data analytics and traffic enginerring to maximize societal impact. 

University of Twente is the project’s academic partner, responsible for the development of a method that offers personalized travel and route advice, tailored to individuals and adapted based on the current and near-future traffic conditions. The aim is to establish a self-learning platform: through interactions with the user, advice is further improved and the platform learns how an individual balances criteria. Machine learning methods are fed with static, dynamic and real-time data, and these methods are used to detect changes in infrastructure and bicycle patterns.