Occupant behavior modeling methods for resilient building design, operation and policy at urban scale: A review
Traditional occupant behavior modeling has been studied at the building level, and it has
become an important factor in the investigation of building energy consumption. However …
become an important factor in the investigation of building energy consumption. However …
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 …
Exploiting dynamic spatio-temporal graph convolutional neural networks for citywide traffic flows prediction
The prediction of crowd flows is an important urban computing issue whose purpose is to
predict the future number of incoming and outgoing people in regions. Measuring the …
predict the future number of incoming and outgoing people in regions. Measuring the …
Urban traffic prediction from spatio-temporal data using deep meta learning
Predicting urban traffic is of great importance to intelligent transportation systems and public
safety, yet is very challenging because of two aspects: 1) complex spatio-temporal …
safety, yet is very challenging because of two aspects: 1) complex spatio-temporal …
Exploiting dynamic spatio-temporal correlations for citywide traffic flow prediction using attention based neural networks
For intelligent transportation systems (ITS), predicting urban traffic crowd flows is of great
importance. However, it is challenging to represent various complex spatial relationships …
importance. However, it is challenging to represent various complex spatial relationships …
A data aggregation based approach to exploit dynamic spatio-temporal correlations for citywide crowd flows prediction in fog computing
Accurate and timely predicting citywide traffic crowd flows precisely is crucial for public
safety and traffic management in smart cities. Nevertheless, its crucial challenge lies in how …
safety and traffic management in smart cities. Nevertheless, its crucial challenge lies in how …
Deep spatio-temporal residual networks for citywide crowd flows prediction
Forecasting the flow of crowds is of great importance to traffic management and public
safety, and very challenging as it is affected by many complex factors, such as inter-region …
safety, and very challenging as it is affected by many complex factors, such as inter-region …
Online incremental machine learning platform for big data-driven smart traffic management
D Nallaperuma, R Nawaratne… - IEEE Transactions …, 2019 - ieeexplore.ieee.org
The technological landscape of intelligent transport systems (ITS) has been radically
transformed by the emergence of the big data streams generated by the Internet of Things …
transformed by the emergence of the big data streams generated by the Internet of Things …
Flow prediction in spatio-temporal networks based on multitask deep learning
Predicting flows (eg, the traffic of vehicles, crowds, and bikes), consisting of the in-out traffic
at a node and transitions between different nodes, in a spatio-temporal network plays an …
at a node and transitions between different nodes, in a spatio-temporal network plays an …
A deep learning approach for detecting traffic accidents from social media data
This paper employs deep learning in detecting the traffic accident from social media data.
First, we thoroughly investigate the 1-year over 3 million tweet contents in two metropolitan …
First, we thoroughly investigate the 1-year over 3 million tweet contents in two metropolitan …