Mobility and Transportation Analytics

Many techniques for the planning of mobility and transportation services ignore the stochastic nature of the underlying problem, which makes the creation of efficient and reliable plans difficult. Since large amounts of data from service operation are available, how can we explore these such that they help making transportation services more reliable? And what is the value of including large amounts of data arising in service operation? We analyze large transportation data sets, aggregate them with methods of predictive analytics, and extend methods of robust and stochastic optimization to create reliable, traveler-friendly trip recommendations as well as reliable delivery plans for time-window-based transportation services (prescriptive analytics).

→ Further details: Prof. Ehmke