[HTML][HTML] A systematic review of data fusion techniques for optimized structural health monitoring
Advancements in structural health monitoring (SHM) techniques have spiked in the past few
decades due to the rapid evolution of novel sensing and data transfer technologies. This …
decades due to the rapid evolution of novel sensing and data transfer technologies. This …
Fast-Yolo-rec: Incorporating Yolo-base detection and recurrent-base prediction networks for fast vehicle detection in consecutive images
Despite significant advances and innovations in deep network-based vehicle detection
methods, finding a balance between detector accuracy and speed remains a significant …
methods, finding a balance between detector accuracy and speed remains a significant …
Using CNN-LSTM to predict signal phasing and timing aided by High-Resolution detector data
This paper proposes a real-time signal timing prediction based on deep learning algorithms
that takes various traffic flow parameters as input and predicts signal timing parameters …
that takes various traffic flow parameters as input and predicts signal timing parameters …
Utilizing attention-based multi-encoder-decoder neural networks for freeway traffic speed prediction
A Abdelraouf, M Abdel-Aty… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Speed prediction is a crucial yet complicated task for intelligent transportation systems. The
challenge derives from the complex spatiotemporal dependencies of traffic parameters. In …
challenge derives from the complex spatiotemporal dependencies of traffic parameters. In …
Sequence-to-sequence recurrent graph convolutional networks for traffic estimation and prediction using connected probe vehicle data
A Abdelraouf, M Abdel-Aty… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Traffic estimation is imperative for conducting fundamental transportation engineering tasks
such as transportation planning and traffic safety studies. Additionally, traffic prediction is …
such as transportation planning and traffic safety studies. Additionally, traffic prediction is …
Comparative study of ten machine learning algorithms for short-term forecasting in gas warning systems
This research aims to explore more efficient machine learning (ML) algorithms with better
performance for short-term forecasting. Up-to-date literature shows a lack of research on …
performance for short-term forecasting. Up-to-date literature shows a lack of research on …
Vulnerable road users' crash hotspot identification on multi-lane arterial roads using estimated exposure and considering context classification
This research develops safety performance functions and identifies the crash hotspots
based on estimated vulnerable road users' exposure at intersections and along the roadway …
based on estimated vulnerable road users' exposure at intersections and along the roadway …
A data-driven network model for traffic volume prediction at signalized intersections
Network-wide traffic prediction at the level of an intersection can benefit transportation
systems management and operations. However, traditional traffic modeling approaches …
systems management and operations. However, traditional traffic modeling approaches …
Analyzing the difference between operating speed and target speed using mixed-effect ordered logit model
N Mahmoud, M Abdel-Aty, Q Cai… - Transportation …, 2022 - journals.sagepub.com
Desired operating speed (target speed) plays an important role in enhancing traffic
operations and providing safe mobility to road users. Understanding the difference between …
operations and providing safe mobility to road users. Understanding the difference between …
Signal phasing and timing prediction using connected vehicle data
Signal phasing and timing can be adaptive and actuated in practice. This makes it
challenging to understand what the cycle length and phase duration of the next few cycles …
challenging to understand what the cycle length and phase duration of the next few cycles …