Förbättrad noggrannhet i skattning av trafikvolym för hållbar regional utveckling
Project name: Förbättrad noggrannhet i skattning av trafikvolym för hållbar regionalutveckling
Project leader: Qian Wang, Samhällsplanering och miljö
Funding: Region Stockholm
Period: 2025-01-01 – 2026-12-31
The goal is to develop a tool to improve the accuracy of traffic volume estimation on small, residential roads in Stockholm County. Reliable traffic volume data is essential for analyzing the social and environmental impacts of the transport system. The tool will use a predictive model to describe existing environments and assist in planning future developments. The aim is to promote sustainable regional development and healthy living environments in the Stockholm Region.
Currently, traffic counts are only available for major roads, while traffic volume estimates for small roads are often unreliable. Moreover, studies that improve traffic volume estimations are typically limited to small areas and short-term forecasts.
We will combine an agent-based scheduling model with machine learning techniques to estimate traffic volume using data on land use, networks, and other determining factors. The model takes into account dynamic changes in land use and transport infrastructure in regional planning.
Research questions:
- What factors influence traffic volume on different types of roads?
- Which machine learning algorithms are most effective for estimating traffic flows on small residential roads?
- How can the model be applied at a regional level?
Relevance:
The relevance of this project lies in its contribution to the regional development challenge, outlined in the 2050 regional development plan, to accommodate population growth while improving the region’s environment and residents’ health.
