Machine learning-based Road Safety Prediction Strategies for Internet of vehicles (IoV) enabled vehicles: A systematic literature review
KR Reddy, A Muralidhar - IEEE Access, 2023 - ieeexplore.ieee.org
This systematic literature review aims to investigate the current state-of-the-art in machine
learning (ML) based road traffic analysis, hindrance estimation, and predicting vehicle safety …
learning (ML) based road traffic analysis, hindrance estimation, and predicting vehicle safety …
Spatio-temporal fusion and contrastive learning for urban flow prediction
Urban flow prediction is critical for urban planning, management, and safety. However,
owing to the inherent instability of urban flows, prediction accuracy requires the fusion of …
owing to the inherent instability of urban flows, prediction accuracy requires the fusion of …
[PDF][PDF] Parameter Tuned Deep Learning Based Traffic Critical Prediction Model on Remote Sensing Imaging.
Remote sensing (RS) presents laser scanning measurements, aerial photos, and high-
resolution satellite images, which are utilized for extracting a range of traffic-related and road …
resolution satellite images, which are utilized for extracting a range of traffic-related and road …
Traffic congestion classification using GAN-Based synthetic data augmentation and a novel 5-layer convolutional neural network model
Private automobiles are still a widely prevalent mode of transportation. Subsequently, traffic
congestion on the roads has been more frequent and severe with the continuous rise in the …
congestion on the roads has been more frequent and severe with the continuous rise in the …
Identification and evaluation of the effective criteria for detection of congestion in a smart city
A Mohanty, SK Mohanty, B Jena… - IET …, 2022 - Wiley Online Library
The delay in transportation of necessary items is due to traffic congestion throughout the
world. This is a serious phenomenon which results in waste of time and fuel. The detection …
world. This is a serious phenomenon which results in waste of time and fuel. The detection …
Mobile networks-on-chip mapping algorithms for optimization of latency and energy consumption
With the advancement in technology, it is now possible to integrate hundreds of cores onto
single silicon semiconductor chip or silicon die. In order to provide communication between …
single silicon semiconductor chip or silicon die. In order to provide communication between …
A mobility forecasting framework with vertical federated learning
FZ Errounda, Y Liu - 2022 IEEE 46th Annual Computers …, 2022 - ieeexplore.ieee.org
With the prevalence of mobile devices and location-based services, forecasting human
mobility has become a critical topic in ubiquitous computing. Existing forecasting …
mobility has become a critical topic in ubiquitous computing. Existing forecasting …
A novel hybrid deep learning algorithm for smart city traffic congestion predictions
A research is an intellectual system, the vehicular adhoc network (VANET), supplies cars in
the network with critical information. Over 150,000 individuals are impacted by traffic …
the network with critical information. Over 150,000 individuals are impacted by traffic …
[PDF][PDF] Intelligent Slime Mould Optimization with Deep Learning Enabled Traffic Prediction in Smart Cities.
Intelligent Transportation System (ITS) is one of the revolutionary technologies in smart cities
that helps in reducing traffic congestion and enhancing traffic quality. With the help of big …
that helps in reducing traffic congestion and enhancing traffic quality. With the help of big …
Measuring exposure and contribution of different types of activity travels to traffic congestion using GPS trajectory data
This study proposes a data-driven framework for understanding the space-time patterns of
exposure and contribution of different activities to traffic congestion in urban road networks …
exposure and contribution of different activities to traffic congestion in urban road networks …