This paper serves as an introduction and overview of the potentially useful models and
methodologies from artificial intelligence (AI) into the field of transportation engineering for …

We present a novel approach for traffic forecasting in urban traffic scenarios using a
combination of spectral graph analysis and deep learning. We predict both the low-level …

Trajectories drawn in a common reference system by all the vehicles on a road are the
ultimate empirical data to investigate traffic dynamics. The vast amount of such data made …

Merging is, in general, a challenging task for both human drivers and autonomous vehicles,
especially in dense traffic, because the merging vehicle typically needs to interact with other …

The Next Generation Simulation (NGSIM) trajectory data sets provide longitudinal and
lateral positional information for all vehicles in certain spatiotemporal regions. Velocity and …

We study how to estimate real time queue lengths at signalized intersections using
intersection travel times collected from mobile traffic sensors. The estimation is based on the …

In this paper we identify the origins of stop-and-go (or slow-and-go) driving and measure
microscopic features of their propagations by analyzing vehicle trajectories via Wavelet …

We present a physics-informed deep learning (PIDL) approach to tackle the challenge of
data sparsity and sensor noise in traffic state estimation (TSE). PIDL strengthens a deep …

Nowadays, among the microscopic traffic flow modeling approaches, the car-following
models are increasingly used by transportation experts to utilize appropriate intelligent …

Owing to their dynamic and multidisciplinary characteristics, Unmanned Aerial Vehicles
(UAVs), or drones, have become increasingly popular. However, the civil applications of this …