Abstract
The deployment of moving sensor platforms (e.g., self-driving cars, drones, and other instances) with advanced sensing is rapidly increasing the capture of human features at unprecedented temporal and spatial scales, especially in cities. This thesis advances knowledge on extracting information from this novel data source for traffic research and practice, while highlighting implications for privacy and beyond. Findings provide insights for traffic control and management, policy development, and anyone involved in responsible urban innovation.
| Original language | English |
|---|---|
| Qualification | Doctor of Philosophy |
| Awarding Institution |
|
| Supervisors/Advisors |
|
| Award date | 1 May 2025 |
| Print ISBNs | 978-90-5584-366-4 |
| DOIs | |
| Publication status | Published - 2025 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
-
SDG 11 Sustainable Cities and Communities
Keywords
- Mobile sensor systems
- Pedestrians and cyclists
- Active modes
- Moving sensor platforms
- Connected autonomous vehicles
- Intelligent autonomous systems
- Data collection
- Data fusion
- Real-time systems
- Trajectory reconstruction
- Multiple target tracking
- Road networks
- Traffic data
- Mobility
- Traffic monitoring and control
- Advanced traffic management systems
- Privacy
- Surveillance
Fingerprint
Dive into the research topics of 'Eyes in Motion: A New Traffic Sensing Paradigm for Pedestrians and Cyclists'. Together they form a unique fingerprint.Cite this
- APA
- Author
- BIBTEX
- Harvard
- Standard
- RIS
- Vancouver