We consider the problem of modeling trajectories of drivers in a road network from the perspective of inverse reinforcement learning. Cars are detected by sensors placed on sparsely distributed points on the street network of a city. As rational …
Estimation of origin-destination (OD) flows in transportation networks is a major step in transportation planning. We are interested in estimating OD flows given data on traffic link volumes over a sequence of days. We propose a dynamic hierarchical …
Earthmoving processes are extremely important for road construction because of the high costs involved. Discrete-event simulation has been used as a decision-support tool for earthworks planning and operation in the last few decades; however, less …
In this paper, we propose a dynamic linear model (DLM) for the estimation of timevarying OD matrices from link counts. Our main research objective is to investigate whether we can overcome the non-identifiability problem, which occurs in static …