Development and evaluation of bidirectional LSTM freeway traffic forecasting models using simulation data
Long short-term memory (LSTM) models provide high predictive performance through their
ability to recognize longer sequences of time series data. More recently, bidirectional deep …
ability to recognize longer sequences of time series data. More recently, bidirectional deep …
Cooperative multi-camera vehicle tracking and traffic surveillance with edge artificial intelligence and representation learning
Traffic surveillance cameras are the eyes of the Intelligent Transportation Systems (ITS).
However, they are currently isolated and can only extract information from each of their fixed …
However, they are currently isolated and can only extract information from each of their fixed …
Modeling of freeway real-time traffic crash risk based on dynamic traffic flow considering temporal effect difference
Y Yang, Y Yin, Y Wang, R Meng… - Journal of transportation …, 2023 - ascelibrary.org
With the development of traffic detection facilities technology, it is currently possible to obtain
high-resolution traffic flow data. Due to the particular driving characteristics of vehicles on …
high-resolution traffic flow data. Due to the particular driving characteristics of vehicles on …
AARGNN: An attentive attributed recurrent graph neural network for traffic flow prediction considering multiple dynamic factors
Traffic flow prediction is a fundamental part of ITS (Intelligent Transportation System). Since
the correlations of traffic data are complicated and are affected by various factors, traffic flow …
the correlations of traffic data are complicated and are affected by various factors, traffic flow …
Rethinking of radar's role: A camera-radar dataset and systematic annotator via coordinate alignment
Radar has long been a common sensor on autonomous vehicles for obstacle ranging and
speed estimation. However, as a robust sensor to all-weather conditions, radar's capability …
speed estimation. However, as a robust sensor to all-weather conditions, radar's capability …
Joint resource management for mobility supported federated learning in Internet of Vehicles
G Wang, F Xu, H Zhang, C Zhao - Future Generation Computer Systems, 2022 - Elsevier
In recent years, the powerful combination of Multi-access Edge Computing (MEC) and
Artificial Intelligence (AI), called edge intelligence, promotes the development of Intelligent …
Artificial Intelligence (AI), called edge intelligence, promotes the development of Intelligent …
A novel spatio-temporal generative inference network for predicting the long-term highway traffic speed
Accurately predicting the highway traffic speed can reduce traffic accidents and transit time,
which is of great significance to highway management. Three essential elements should be …
which is of great significance to highway management. Three essential elements should be …
Integrating the traffic science with representation learning for city-wide network congestion prediction
Recent studies on traffic congestion prediction have paved a promising path towards the
reduction of potential economic and environmental loss. However, at the city-wide scale …
reduction of potential economic and environmental loss. However, at the city-wide scale …
Traffic density classification for multiclass vehicles using customized convolutional neural network for smart city
Building a traffic monitoring system for intelligent transportation systems (ITS) in the
develo** smart cities has drawn in a mass of consideration in the latest past. Since the …
develo** smart cities has drawn in a mass of consideration in the latest past. Since the …
Cooperative traffic signal assistance system for non-motorized users and disabilities empowered by computer vision and edge artificial intelligence
Abstract Information and communication technology has many promising benefits including
improvement the traffic network capacity, efficiency, and stability. However, to date, most of …
improvement the traffic network capacity, efficiency, and stability. However, to date, most of …