Mobility in a city is represented as traffic flows in and out of defined urban travel or
administrative zones. While the zones and the road networks connecting them are fixed in …

The urban road networks undergo frequent traffic congestions during the peak hours and
around the city center. Capturing the spatiotemporal evolution of the congestion scenario in …

Prediction of traffic congestion is one of the core issues in the realization of smart traffic.
Accurate prediction depends on understanding of interactions and correlations between …

Increasing amount of urban data are being accumulated and released to public; this enables
us to study the urban dynamics and address urban issues such as crime, traffic, and quality …

Real-time traffic volume inference is key to an intelligent city. It is a challenging task because
accurate traffic volumes on the roads can only be measured at certain locations where …

Spatiotemporal models that incorporate information about the relationships between traffic
flows over space and time have recently become the mainstream of urban traffic forecasting …

Urban space exhibits rich and diverse organizational structures, which is difficult to
characterize and interpret. Modelling urban spatial structures in the context of mobility and …

Intelligent transportation systems are a key component in smart cities, and the estimation
and prediction of the spatiotemporal traffic state is critical to capture the dynamics of traffic …

This paper proposes a graph-based approach to representing spatio-temporal trajectory
data that allows an effective visualization and characterization of city-wide traffic dynamics …

Predicting the urban traffic flow is of great importance for urban planners to be used in long-
term prediction or in Intelligent Transport Systems (ITS) for short-term predictions. Traffic …