This work is part of a research project called NewMob, funded by the French National Research Agency (ANR) and supervised by Université Paris Cité and Ergocentre.In France, we are witnessing strong growth in the number and type of Personal Light Electric Véhicule (PLEVs), as well as an increase in the number and severity of accidents involving these users. (Osnir, 2023) As a result, there is a general need for more knowledge about the behaviors of the various users of PLEVs and the risk situations they encounter on their journeys. The research question we will develop in this article is therefore as follows: To what extent can taking human factors into account in the risk situations encountered by users of new mobility help to improve their safety in the city?To investigate this question, we have set up three long-term studies in France's three biggest cities: Paris, Lyon and Marseille. These studies are part of the ergonomic analysis of human activity. (Leplat, 2015) Field methods were used to track and analyze the journeys of 150 participants: 65 electric bikes, 60 electric scooters and 25 gyroroues. Over a 2-month period, participants recorded all their daily journeys (video and data), and were able to report any risky situations they encountered during their journeys. For each situation, the participant had to fill in a detailed form, enabling us to analyze a total of around 955 risky situations collected. Two self-confrontation interviews enabled us to analyze the participants' situated experience of risky situations, their ways of dealing with incidents, and the strategies and compromises they implemented during their journeys.Vehicle dynamic data were recorded for a total of 9,124 trips, covering a distance of over 33,000 kilometers. This data include acceleration, speed, braking and lateral variations, enabling us to gather a wealth of statistical data, and to objectively qualify the criteria for identifying a high-risk situation (emergency avoidance, braking force, etc.). A major challenge of this project is to be able to cross-analyze the subjective data collected during interviews with objective vehicle dynamics data, enabling us to establish a complete overview of the different risk situations encountered by users of new urban mobility in France. Initial results are emerging from ongoing data analysis, and we will be able to present in greater detail the cross-profiling of participants across the various study methods (type of vehicle, number of risky situations, severity of situations, average speed of journeys, speeds in risky situations, participants' relationship to risk, etc.). Additionally, we will present the results of analyses on the main types of risky situations encountered by participants, along with comparisons between the different cities analyzed.