Traffic state estimation and prediction
As a consequence of the increased congestion in major cities, the need for more accurate traffic information with better coverage is growing all over the world. In parallel with this demand, considerably more traffic data is being collected from a variety of new and existing sources. Technologies like Bluetooth and WiFi together with different GPS-devices, smart phones, cell phone data and more traditional data sources like radar and loop detectors have the potential to create a highly comprehensive traffic database. Despite the abundance of traffic data, the types and formats available do not represent an immediate means for improving overall data quality. Their temporal and spatial resolution as well as the aggregation, accuracy and precision differ substantially. Therefore, models and algorithms that enable an optimal combination of data are necessary in order to produce accurate and reliable estimates and predictions of the current and future traffic state.
The purpose of my research is to develop models that can estimate and predict the traffic state, based on the real-time traffic data available in Stockholm.
Advisors: Professor Jan Lundgren, Dr. Clas Rydergren.
University: Linköpings universitet.