Traffic prediction using artificial intelligence: Review of recent advances and emerging opportunities
Traffic prediction plays a crucial role in alleviating traffic congestion which represents a
critical problem globally, resulting in negative consequences such as lost hours of additional …
critical problem globally, resulting in negative consequences such as lost hours of additional …
A survey on graph neural networks for time series: Forecasting, classification, imputation, and anomaly detection
Time series are the primary data type used to record dynamic system measurements and
generated in great volume by both physical sensors and online processes (virtual sensors) …
generated in great volume by both physical sensors and online processes (virtual sensors) …
[HTML][HTML] How machine learning informs ride-hailing services: A survey
In recent years, online ride-hailing services have emerged as an important component of
urban transportation system, which not only provide significant ease for residents' travel …
urban transportation system, which not only provide significant ease for residents' travel …
Region-level traffic prediction based on temporal multi-spatial dependence graph convolutional network from GPS data
H Yang, X Zhang, Z Li, J Cui - Remote Sensing, 2022 - mdpi.com
Region-level traffic information can characterize dynamic changes of urban traffic at the
macro level. Real-time region-level traffic prediction help city traffic managers with traffic …
macro level. Real-time region-level traffic prediction help city traffic managers with traffic …
Intelligent decision-making model in preventive maintenance of asphalt pavement based on PSO-GRU neural network
The milage of asphalt pavement growth explosively around the world in the past decades
resulted in a tremendous maintenance workload. Preventive maintenance (PM) is an …
resulted in a tremendous maintenance workload. Preventive maintenance (PM) is an …
STGNN-TTE: Travel time estimation via spatial–temporal graph neural network
Estimating the travel time of urban trajectories is a basic but challenging task in many
intelligent transportation systems, which is the foundation of route planning and traffic …
intelligent transportation systems, which is the foundation of route planning and traffic …
A new multi-data-driven spatiotemporal PM2. 5 forecasting model based on an ensemble graph reinforcement learning convolutional network
X Liu, M Qin, Y He, X Mi, C Yu - Atmospheric Pollution Research, 2021 - Elsevier
Spatiotemporal PM2. 5 forecasting technology plays an important role in urban traffic
environment management and planning. In order to establish a satisfactory high-precision …
environment management and planning. In order to establish a satisfactory high-precision …
On region-level travel demand forecasting using multi-task adaptive graph attention network
Accurate travel demand forecasting at the regional level benefits to urban traffic
management and service operations. Irregular regions can be naturally represented by …
management and service operations. Irregular regions can be naturally represented by …
A new ensemble deep graph reinforcement learning network for spatio-temporal traffic volume forecasting in a freeway network
Spatio-temporal traffic volume forecasting technologies can effectively improve freeway
traffic efficiency and the travel comfort of humans. To construct a high-precision traffic …
traffic efficiency and the travel comfort of humans. To construct a high-precision traffic …
A GAN framework-based dynamic multi-graph convolutional network for origin–destination-based ride-hailing demand prediction
Z Huang, W Zhang, D Wang, Y Yin - Information Sciences, 2022 - Elsevier
Ride-hailing demand prediction plays an important role in ride-hailing vehicle scheduling,
traffic condition control and intelligent transportation system construction. Accurate and real …
traffic condition control and intelligent transportation system construction. Accurate and real …