Ricerc@Sapienza

Understanding the dynamics of demand over space and time is essential for many applications across the entire transportation domain. Since we cannot directly observe the demand yet, we need to estimate the demand from whatever data and information are available. Unfortunately, the Dynamic Origin-Destination Estimation (DODE) is still one of the most challenging problems in transportation to date. The complexity in estimating dynamic OD matrices is that the problem is severely under-determined, a fact recognized since the early days of OD estimation and is emphasized in virtually all contemporary OD research.
Dynamic Traffic Assignment (DTA) models are a reliable tool to replicate complex traffic conditions, monitor and make short-term forecasts of the flow and speed of the network. The most crucial input in both online and offline dynamic traffic assignment is the OD demand. The success of the DTA applications relies on the quality of this fundamental input. Recent advances in real-time DTA applications in large networks call for robust and efficient methodologies for online OD demand estimation and short-term demand predictions.
With the rapid development of mobile-internet technologies, on-demand mobility services have become increasingly popular and largely shaped the way people travel. Therefore, demand prediction is a fundamental component in supply-demand management systems of ride-sourcing platforms. With accurate short-term demand prediction, the platforms make precise decisions on real-time matching and idle vehicle reallocation.
The main objective of this research project is to use Big Data resources, such as vehicle positions, to estimate and forecast demand in the form of Dynamic Origin-Destination matrices to be used as an input for DTA. In addition, gathering this data with machine learning techniques and artificial intelligence to prevailing demand patterns can be used for online demand forecasting, specifically predicting ride-sourcing demand.