معرفی
David Gundlegård is an Associate Professor at Linköping University's Department of Science and Technology (ITN), within the Communications and Transport Systems (KTS) faculty. His research focuses on urban transport logistics, data-driven traffic management, and sustainable mobility solutions. Key projects include the EU-funded SuRuMo initiative for rural mobility and the CODE PROBE system for real-time traffic analysis using GPS and mobile data. He has contributed to over 15 peer-reviewed publications since 2020, emphasizing machine learning applications in transport demand forecasting and infrastructure optimization. His work integrates advanced analytics with practical tools for urban planning, including collaboration between public and private sectors. Awards and grants are not explicitly listed, but his research has been recognized in the IVA 100 list (2023). He advises on projects involving construction logistics and smart city technologies, though no student names are documented.
Research interests span:
- Transportation Engineering and Urban Planning
- Machine Learning for Traffic Demand Prediction
- Data-Driven Network Analysis (GPS/Mobile Data)
- Sustainable Rural and Urban Mobility Strategies
- Collaborative Construction Traffic Management
Recent articles highlight trends in:
- Prediction models for construction transport flows
- Clustering techniques for traffic pattern analysis
- Integration of simulation tools in strategic planning
