Traffic prediction plays a crucial role in alleviating traffic congestion which represents a
critical problem globally, resulting in negative consequences such as lost hours of additional …

Intelligent transportation (eg, intelligent traffic light) makes our travel more convenient and
efficient. With the development of mobile Internet and position technologies, it is reasonable …

Traffic time series forecasting is challenging due to complex spatio-temporal dynamics-time
series from different locations often have distinct patterns; and for the same time series …

We propose two solutions to outlier detection in time series based on recurrent autoencoder
ensembles. The solutions exploit autoencoders built using sparsely-connected recurrent …

Estimating the travel time of any path (denoted by a sequence of connected road segments)
in a city is of great importance to traffic monitoring, route planning, ridesharing, taxi/Uber …

Due to the continued digitization of industrial and societal processes, including the
deployment of networked sensors, we are witnessing a rapid proliferation of time-ordered …

Correlated time series (CTS) forecasting plays an essential role in many cyber-physical
systems, where multiple sensors emit time series that capture interconnected processes …

The online car-hailing service has gained great popularity all over the world. As more
passengers and more drivers use the service, it becomes increasingly more important for the …

When planning routes, drivers usually consider a multitude of different travel costs, eg,
distances, travel times, and fuel consumption. Different drivers may choose different routes …

Real-time traffic prediction from high-fidelity spatiotemporal traffic sensor datasets is an
important problem for intelligent transportation systems and sustainability. However, it is …