Outlier detection for multidimensional time series using deep neural networks
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 …
deployment of networked sensors, we are witnessing a rapid proliferation of time-ordered …
End-to-end learning of driving models with surround-view cameras and route planners
For human drivers, having rear and side-view mirrors is vital for safe driving. They deliver a
more complete view of what is happening around the car. Human drivers also heavily exploit …
more complete view of what is happening around the car. Human drivers also heavily exploit …
Finding top-k shortest paths with diversity
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 …
directed graph, plays an important role in many application domains, such as providing …
Stochastic origin-destination matrix forecasting using dual-stage graph convolutional, recurrent neural networks
Origin-destination (OD) matrices are used widely in transportation and logistics to record the
travel cost (eg, travel speed or greenhouse gas emission) between pairs of OD regions …
travel cost (eg, travel speed or greenhouse gas emission) between pairs of OD regions …
Personalized and situation-aware multimodal route recommendations: the FAVOUR algorithm
P Campigotto, C Rudloff, M Leodolter… - IEEE Transactions on …, 2016 - ieeexplore.ieee.org
Route choice in multimodal networks shows a considerable variation between different
individuals and the current situational context. Personalization and situation awareness of …
individuals and the current situational context. Personalization and situation awareness of …
Diversified top-k route planning in road network
Route planning is ubiquitous and has a profound impact on our daily life. However, the
existing path algorithms tend to produce similar paths between similar OD (Origin …
existing path algorithms tend to produce similar paths between similar OD (Origin …
Learning to route with sparse trajectory sets
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 …
a comprehensive trajectory-based routing solution. Specifically, we first construct a graph …
PACE: a PAth-CEntric paradigm for stochastic path finding
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 …
capture time-varying and uncertain travel costs, eg, travel time, in a road network. The …
Fast stochastic routing under time-varying uncertainty
Data are increasingly available that enable detailed capture of travel costs associated with
the movements of vehicles in road networks, notably travel time, and greenhouse gas …
the movements of vehicles in road networks, notably travel time, and greenhouse gas …
Context-aware, preference-based vehicle routing
Vehicle routing is an important service that is used by both private individuals and
commercial enterprises. Drivers may have different contexts that are characterized by …
commercial enterprises. Drivers may have different contexts that are characterized by …