Human mobility data in the COVID-19 pandemic: characteristics, applications, and challenges
The COVID-19 pandemic poses unprecedented challenges around the world. Many studies
have applied mobility data to explore spatiotemporal trends over time, investigate …
have applied mobility data to explore spatiotemporal trends over time, investigate …
State-of-the-art deep learning: Evolving machine intelligence toward tomorrow's intelligent network traffic control systems
Currently, the network traffic control systems are mainly composed of the Internet core and
wired/wireless heterogeneous backbone networks. Recently, these packet-switched …
wired/wireless heterogeneous backbone networks. Recently, these packet-switched …
A novel k-means clustering algorithm with a noise algorithm for capturing urban hotspots
With the development of cities, urban congestion is nearly an unavoidable problem for
almost every large-scale city. Road planning is an effective means to alleviate urban …
almost every large-scale city. Road planning is an effective means to alleviate urban …
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 …
Trajectory data mining: an overview
Y Zheng - ACM Transactions on Intelligent Systems and …, 2015 - dl.acm.org
The advances in location-acquisition and mobile computing techniques have generated
massive spatial trajectory data, which represent the mobility of a diversity of moving objects …
massive spatial trajectory data, which represent the mobility of a diversity of moving objects …
Deep irregular convolutional residual LSTM for urban traffic passenger flows prediction
Urban traffic passenger flows prediction is practically important to facilitate many real
applications including transportation management and public safety. Recently, deep …
applications including transportation management and public safety. Recently, deep …
Urban big data fusion based on deep learning: An overview
Urban big data fusion creates huge values for urban computing in solving urban problems.
In recent years, various models and algorithms based on deep learning have been …
In recent years, various models and algorithms based on deep learning have been …
The simpler the better: a unified approach to predicting original taxi demands based on large-scale online platforms
Taxi-calling apps are gaining increasing popularity for their efficiency in dispatching idle
taxis to passengers in need. To precisely balance the supply and the demand of taxis, online …
taxis to passengers in need. To precisely balance the supply and the demand of taxis, online …
Urban function classification at road segment level using taxi trajectory data: A graph convolutional neural network approach
Extracting hidden information from human mobility patterns is one of the long-standing
challenges of urban studies. In addition, exploring the relationship between urban functional …
challenges of urban studies. In addition, exploring the relationship between urban functional …
U-air: When urban air quality inference meets big data
Information about urban air quality, eg, the concentration of PM2. 5, is of great importance to
protect human health and control air pollution. While there are limited air-quality-monitor …
protect human health and control air pollution. While there are limited air-quality-monitor …