Survey on traffic prediction in smart cities
The rapid development in machine learning and in the emergence of new data sources
makes it possible to examine and predict traffic conditions in smart cities more accurately …
makes it possible to examine and predict traffic conditions in smart cities more accurately …
Short-term traffic flow forecasting: An experimental comparison of time-series analysis and supervised learning
The literature on short-term traffic flow forecasting has undergone great development
recently. Many works, describing a wide variety of different approaches, which very often …
recently. Many works, describing a wide variety of different approaches, which very often …
Causal Deep Learning: Encouraging Impact on Real-world Problems Through Causality
Causality has the potential to truly transform the way we solve a large number of real-world
problems. Yet, so far, its potential largely remains to be unlocked as causality often requires …
problems. Yet, so far, its potential largely remains to be unlocked as causality often requires …
Short‐term traffic speed prediction for an urban corridor
B Yao, C Chen, Q Cao, L Jin, M Zhang… - Computer‐Aided Civil …, 2017 - Wiley Online Library
Short‐term traffic speed prediction is one of the most critical components of an intelligent
transportation system (ITS). The accurate and real‐time prediction of traffic speeds can …
transportation system (ITS). The accurate and real‐time prediction of traffic speeds can …
Geospatial data to images: A deep-learning framework for traffic forecasting
W Jiang, L Zhang - Tsinghua Science and Technology, 2018 - ieeexplore.ieee.org
Traffic forecasting has been an active research field in recent decades, and with the
development of deep-learning technologies, researchers are trying to utilize deep learning …
development of deep-learning technologies, researchers are trying to utilize deep learning …
Causal deep learning
Causality has the potential to truly transform the way we solve a large number of real-world
problems. Yet, so far, its potential largely remains to be unlocked as causality often requires …
problems. Yet, so far, its potential largely remains to be unlocked as causality often requires …
Short‐term traffic flow prediction with linear conditional Gaussian Bayesian network
Traffic flow prediction is an essential part of intelligent transportation systems (ITS). Most of
the previous traffic flow prediction work treated traffic flow as a time series process only …
the previous traffic flow prediction work treated traffic flow as a time series process only …
Citywide traffic speed prediction: A geometric deep learning approach
JQ James - Knowledge-Based Systems, 2021 - Elsevier
Accurate traffic speed prediction is critical to modern internet of things-based intelligent
transportation systems. It serves as the foundation of advanced traffic management systems …
transportation systems. It serves as the foundation of advanced traffic management systems …
Discovering congestion propagation patterns in spatio-temporal traffic data
Traffic congestion is a condition of a segment in the road network where the traffic demand is
greater than the available road capacity. The detection of unusual traffic patterns including …
greater than the available road capacity. The detection of unusual traffic patterns including …
Wireless sensor networks for traffic management and road safety
Wireless sensor networks (WSN) employ self-powered sensing devices that are mutually
interconnected through wireless ad-hoc technologies. This study illustrates the basics of …
interconnected through wireless ad-hoc technologies. This study illustrates the basics of …