Project

Full title: Safe inclusive traffic infrastructure: data and AI for decision making

The SAFER associated pre-study SINTIA takes the first step in building an empirically and scientifically supported methodology for decision making in the area of traffic infrastructure development. The methodology, based on AI-enabled insights and indicators from objective traffic data, will ensure that the changes in infrastructure give immediate and considerable positive effects on safety and inclusiveness of active mobility.

In this pre-study, the team will use a concrete use case from Jönköping municipality: which type of pedestrian crossing is optimal for safety and comfort for children walking and cycling to school? The guiding questions are to what extent the insights required for the choice of a crossing layout can be obtained today from state-of-the-art traffic data, and what insights are missing? How can AI help extract the missing insights?

The pre-study will result in an analysis of available and missing insights for making decisions in active mobility infrastructure development, based on Jönköping’s use case, and a proposal of AI methods that can be utilized to obtain the missing insights. Moreover, a dataset of traffic behaviors and safety insights will be produced and made available for researchers and practitioners in the field of traffic safety through the SAFER data catalogue.

DAIMOND is a continuation of this pre-study.