[HTML][HTML] Unsupervised machine learning in urban studies: A systematic review of applications
Unsupervised learning (UL) has a long and successful history in untangling the complexity
of cities. As the counterpart of supervised learning, it discovers patterns from intrinsic data …
of cities. As the counterpart of supervised learning, it discovers patterns from intrinsic data …
Traffic pattern mining and forecasting technologies in maritime traffic service networks: A comprehensive survey
Maritime traffic service networks and information systems play a vital role in maritime traffic
safety management. The data collected from the maritime traffic networks are essential for …
safety management. The data collected from the maritime traffic networks are essential for …
A Bayesian tensor decomposition approach for spatiotemporal traffic data imputation
The missing data problem is inevitable when collecting traffic data from intelligent
transportation systems. Previous studies have shown the advantages of tensor completion …
transportation systems. Previous studies have shown the advantages of tensor completion …
Bayesian temporal factorization for multidimensional time series prediction
Large-scale and multidimensional spatiotemporal data sets are becoming ubiquitous in
many real-world applications such as monitoring urban traffic and air quality. Making …
many real-world applications such as monitoring urban traffic and air quality. Making …
Graph convolutional adversarial networks for spatiotemporal anomaly detection
Traffic anomalies, such as traffic accidents and unexpected crowd gathering, may endanger
public safety if not handled timely. Detecting traffic anomalies in their early stage can benefit …
public safety if not handled timely. Detecting traffic anomalies in their early stage can benefit …
Spatial-temporal potential exposure risk analytics and urban sustainability impacts related to COVID-19 mitigation: A perspective from car mobility behaviour
Abstract Coronavirus disease-2019 (COVID-19) poses a significant threat to the population
and urban sustainability worldwide. The surge mitigation is complicated and associates …
and urban sustainability worldwide. The surge mitigation is complicated and associates …
Missing value imputation for traffic-related time series data based on a multi-view learning method
In reality, readings of sensors on highways are usually missing at various unexpected
moments due to some sensor or communication errors. These missing values do not only …
moments due to some sensor or communication errors. These missing values do not only …
Spatial-temporal traffic speed patterns discovery and incomplete data recovery via SVD-combined tensor decomposition
Missing data is an inevitable and ubiquitous problem in data-driven intelligent transportation
systems. While there are several studies on the missing traffic data recovery in the last …
systems. While there are several studies on the missing traffic data recovery in the last …
Urban anomaly analytics: Description, detection, and prediction
Urban anomalies may result in loss of life or property if not handled properly. Automatically
alerting anomalies in their early stage or even predicting anomalies before happening is of …
alerting anomalies in their early stage or even predicting anomalies before happening is of …
Data sources and approaches for building occupancy profiles at the urban scale–A review
Buildings' occupant profiles at the urban scale play an important role in various applications
like Urban Building Energy Modeling (UBEM) and assessing energy consumption patterns …
like Urban Building Energy Modeling (UBEM) and assessing energy consumption patterns …