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 …
Developing a BERT based triple classification model using knowledge graph embedding for question answering system
The current BERT-based question answering systems use a question and a contextual text
to find the answer. This causes the systems to return wrong answers or nothing if the text …
to find the answer. This causes the systems to return wrong answers or nothing if the text …
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 …
Stochastic weight completion for road networks using graph convolutional networks
Innovations in transportation, such as mobility-on-demand services and autonomous driving,
call for high-resolution routing that relies on an accurate representation of travel time …
call for high-resolution routing that relies on an accurate representation of travel time …
A quasi-dynamic air traffic assignment model for mitigating air traffic complexity and congestion for high-density UAM operations
In spite of the significant effects of COVID-19, UAM operations are still expected to grow
smoothly and healthily in the near future. If such dense UAM traffic relies on tactical planning …
smoothly and healthily in the near future. If such dense UAM traffic relies on tactical planning …
Lightpath: Lightweight and scalable path representation learning
Movement paths are used widely in intelligent transportation and smart city applications. To
serve such applications, path representation learning aims to provide compact …
serve such applications, path representation learning aims to provide compact …
Finding k-shortest paths with limited overlap
In this paper, we investigate the computation of alternative paths between two locations in a
road network. More specifically, we study the k-shortest paths with limited overlap (k SPwLO …
road network. More specifically, we study the k-shortest paths with limited overlap (k SPwLO …
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 …