A Traffic Decision Support System Assisted by Mobile Network Data

A traffic decision support system based on an integrated architecture combines a digital traffic model with real-time data processing capabilities. It provides estimates of traffic demand in specific areas and can forecast future traffic states. The model ingests a continuous stream of different realtime traffic data from various sensors, including vehicle counts, speed, direction, and type. Additionally, two types of mobile network data are used as support input. First, anonymized mobile data offering insights into mobility patterns across the area of interest within a selected timeframe. This data is reliable, objective, and consistent, providing more comprehensive and statistically relevant information than traditional survey-based data. Moreover, it gives valuable insights into macro and meso-scale migration patterns. Second, statistical data on mobile network load allows for real-time assessments of traffic density and population estimates in specific areas. By aggregating these different sets of real time data and implementing specific mobile network data for model improvement, the proposed system can create a digital twin of the traffic network able to emulate and predict the current real world traffic situations. Results of traffic modelling and data analysis yield operational support and can be used for both current traffic solutions and future planning.