Autonomous driving technology pledges safety, convenience, and energy efficiency. Its
challenges include the unknown intentions of other road users: communication between …

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 …

The classical K Shortest Paths (KSP) problem, which identifies the k shortest paths in a
directed graph, plays an important role in many application domains, such as providing …

Eco-friendly navigation (aka eco-routing) finds a route from A to B in a road network that
minimizes the greenhouse gas (GHG) emission or fuel/energy consumption of the traveling …

Motivated by the increasing availability of vehicle trajectory data, we propose learn-to-route,
a comprehensive trajectory-based routing solution. Specifically, we first construct a graph …

With the growing volumes of vehicle trajectory data, it becomes increasingly possible to
capture time-varying and uncertain travel costs, eg, travel time, in a road network. The …

Communication between vehicles and road infrastructure can enable more efficient use of
the road network and hence reduce congestion in urban areas. This improvement can be …

Increasingly massive volumes of vehicle trajectory data hold the potential to enable higher-
resolution traffic services than hitherto possible. We use trajectory data to create a high …

We address the problem of choosing the best paths among a set of candidate paths
between the same origin–destination pair. This functionality is used extensively when …

With the growing volumes of vehicle trajectory data, it becomes increasingly possible to
capture time-varying and uncertain travel costs in a road network, including travel time and …