MEASUREMENT METHODS FOR SUSTAINABLE MOBILITY

Urban Mobility Systems face major challenges in road safety, greenhouse gas emissions, air quality, and monitoring vulnerable groups, such as pedestrians and cyclists. Solving these requires dependable, scalable, and affordable sensors that can produce detailed mobility data in various urban settings. This thesis introduces, develops, and tests measurement methods based on GNSS and low-cost IoT tech, making three key scientific contributions in smart, sustainable mobility. The first contribution creates a lab-based framework for verifying GNSS velocity measurements using an SDR testbed. It highlights that poor satellite geometry causes unrealistic velocity spikes due to ill-conditioning in least-squares estimation (LSE). Tikhonov regularisation is used to fix these issues, improving reliability for Intelligent Transport Systems. The second contribution tests pedestrian speed estimation using GNSS-enabled smartphones in different environments, like open, semi-urban, and dense urban, while stationary. Results show both devices measure near-zero velocities with high consistency, demonstrating that consumer smartphones can reliably detect waiting pedestrians at traffic junctions without special infrastructure. The third contribution develops a non-invasive wireless sensor network for sensing crowds in heritage-protected urban zones. The sensor node is embedded in a ceramic cobblestone that blends with traditional paving. BLE passive scanning generates a crowd index tracking pedestrian activity over time, validated in controlled and real-world tests, without storing personal data. Altogether, these contributions provide a privacy-preserving, infrastructure-free sensing framework that supports safer, more sustainable urban mobility planning.