Intelligent transportation systems (ITSs) gather information about traffic conditions by
collecting data from a wide range of on-ground sensors. The collected data usually suffer …

In this paper, we present our work on clustering and prediction of temporal evolution of
global congestion configurations in a large-scale urban transportation network. Instead of …

Event detection is gaining increasing attention in smart cities research. Large-scale mobility
data serves as an important tool to uncover the dynamics of urban transportation systems …

Intelligent transport systems (ITS) require data with high spatial and temporal resolution for
applications such as modeling, traffic management, prediction and route guidance …

The identification and analysis of spatiotemporal traffic patterns in road networks constitute a
crucial process for sophisticated traffic management and control. Traditional methods based …

Particle filters are important techniques to support data assimilation for large-scale spatial
temporal simulation systems. Distributed particle filters improve the performance of particle …

In this paper, we propose to perform clustering and temporal prediction on network-level
traffic states of large-scale traffic networks. Rather than analyzing dynamics of traffic states …

With advancements in sensor technologies, intelligent transportation systems can collect
traffic data with high spatial and temporal resolution. However, the size of the networks …

Detecting urban mobility patterns is crucial for policymakers in urban and transport planning.
Mobile phone data have been increasingly deployed to measure the spatiotemporal …

We investigate different ways of generating approximate solutions to the inverse Ising
problem (IIP). Our approach consists in taking as a starting point for further perturbation …