ESTNet: embedded spatial-temporal network for modeling traffic flow dynamics
Accurate spatial-temporal prediction is a fundamental building block of many real-world
applications such as traffic scheduling and management, environment policy making, and …
applications such as traffic scheduling and management, environment policy making, and …
[HTML][HTML] Intelligent transportation systems: A survey on modern hardware devices for the era of machine learning
The increasing complexity of Intelligent Transportation Systems (ITS), that comprise a wide
variety of applications and services, has imposed a necessity for high-performance Modern …
variety of applications and services, has imposed a necessity for high-performance Modern …
Traffic prediction using multifaceted techniques: A survey
Road transportation is the largest and complex nonlinear entity of the traffic management
system. Accurate prediction of traffic-related information is necessary for an effective …
system. Accurate prediction of traffic-related information is necessary for an effective …
An autoencoder and LSTM-based traffic flow prediction method
Smart cities can effectively improve the quality of urban life. Intelligent Transportation System
(ITS) is an important part of smart cities. The accurate and real-time prediction of traffic flow …
(ITS) is an important part of smart cities. The accurate and real-time prediction of traffic flow …
Short-term traffic flow forecasting method with MB-LSTM hybrid network
Q Zhaowei, L Haitao, L Zhihui… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
Deep learning has achieved good performance in short-term traffic forecasting recently.
However, the stochasticity and distribution imbalance are main characteristics to traffic flow …
However, the stochasticity and distribution imbalance are main characteristics to traffic flow …
Deep spatio-temporal adaptive 3d convolutional neural networks for traffic flow prediction
Traffic flow prediction is the upstream problem of path planning, intelligent transportation
system, and other tasks. Many studies have been carried out on the traffic flow prediction of …
system, and other tasks. Many studies have been carried out on the traffic flow prediction of …
Predicting cycle-level traffic movements at signalized intersections using machine learning models
Predicting accurate traffic parameters is fundamental and cost-effective in providing traffic
applications with required information. Many studies adopted various parametric and …
applications with required information. Many studies adopted various parametric and …
Intersection traffic prediction using decision tree models
Traffic prediction is a critical task for intelligent transportation systems (ITS). Prediction at
intersections is challenging as it involves various participants, such as vehicles, cyclists, and …
intersections is challenging as it involves various participants, such as vehicles, cyclists, and …
[HTML][HTML] A multi-Layer CNN-GRUSKIP model based on transformer for spatial− TEMPORAL traffic flow prediction
Traffic flow prediction remains a cornerstone for intelligent transportation systems (ITS),
influencing both route optimization and environmental efforts. While Recurrent Neural …
influencing both route optimization and environmental efforts. While Recurrent Neural …
Neural computing for grey Richards differential equation to forecast traffic parameters with various time granularity
J He, S Mao, AKY Ng - Neurocomputing, 2023 - Elsevier
The existing traffic parameter prediction methods generally adopt a single prediction model,
but the fusion of different theories and methods can complement each other and improve the …
but the fusion of different theories and methods can complement each other and improve the …