This manuscript investigates the integration of micromobility solutions in four Swiss contexts, with a primary focus on Lugano as the testing area. Through urban analysis, urban experiment, and social testing, the Inclusive MicroMob project introduced Genny Zero, an innovative micromobility device, to assess its impact on urban mobility. The findings highlight key factors for successful micromobility integration, including the need for an interdisciplinary approach that includes a holistic urban overview, an improved road safety analysis, and clear regulatory frameworks. This study also emphasizes the importance of participatory urban governance in addressing community needs and ensuring accessibility. Policy recommendations are provided to support the development of inclusive and sustainable micromobility systems in medium-sized cities.

This paper aims to explore public participation for activating underground built heritage (UBH). It describes and analyses practices of stakeholders’ engagement in different UBH assets, based on experiences gathered in the scope of the European COST Action ‘Underground4value’. It brings together five inspiring cases from Italy, Portugal, Spain, Switzerland and the United Kingdom, in which digital and mobile technologies were used as tools to improve community experiences in UBH. Thus, the paper discusses ‘smartness’ from the perspective of people and communities around cultural assets, where ‘smartness’ becomes a new connotation and a pathway to advance (local) knowledge and know-how. Therefore, this paper takes on the challenge to define a smart city as an ecosystem for people’s empowerment and participation, and, in particular, to explore social tools for creating new values in heritage placemaking—where sharing knowledge becomes a fundamental principle.

Understanding how people and vehicles move through public spaces is essential for designing inclusive, safe, and efficient urban spaces. In the Mendrisio district (Switzerland), we deployed a low-cost, AI-powered sensor to monitor pedestrian and vehicle flows in different seasons. The sensor uses camera-based image recognition to detect and classify objects in real time while preserving privacy by avoiding biometric or identity-related data capture, in full compliance with GDPR. The system was developed using open source hardware and software. Processes video frames on edge using a lightweight machine learning model optimized for embedded devices and periodically transmits summary data (object type, direction, timestamp) via NB-IoT to a centralized data platform. The collected data was used to generate temporal analyses and heatmaps of space usage and to validate the classification accuracy in different weather and lighting conditions. Field tests demonstrated the sensor’s capability to operate autonomously for extended periods with low power consumption, while highlighting limitations in NB-IoT connectivity in specific locations. Despite these constraints, the system provided valuable information on public space utilization, identifying peak hours and spatial patterns relevant for mobility planning and urban design. This approach offers a replicable and cost-effective solution for municipalities seeking data-driven support for decision-making. By combining privacy-sensitive AI, open technologies, and standard data models (such as SensorThings API), the project contributes to a more transparent and inclusive digital urban ecosystem.