Machine learning applications in surface transportation systems: A literature review
Surface transportation has evolved through technology advancements using parallel
knowledge areas such as machine learning (ML). However, the transportation industry has …
knowledge areas such as machine learning (ML). However, the transportation industry has …
[HTML][HTML] Recent advances in traffic signal performance evaluation
D Leitner, P Meleby, L Miao - Journal of traffic and transportation …, 2022 - Elsevier
Signal retiming is a prominent way that transportation agencies use to fight congestion and
change of traffic pattern. Performance evaluations of traffic conditions at signalized …
change of traffic pattern. Performance evaluations of traffic conditions at signalized …
Intelligent decision-making model in preventive maintenance of asphalt pavement based on PSO-GRU neural network
The milage of asphalt pavement growth explosively around the world in the past decades
resulted in a tremendous maintenance workload. Preventive maintenance (PM) is an …
resulted in a tremendous maintenance workload. Preventive maintenance (PM) is an …
SIND: A drone dataset at signalized intersection in China
Y Xu, W Shao, J Li, K Yang, W Wang… - 2022 IEEE 25th …, 2022 - ieeexplore.ieee.org
Intersection is one of the most challenging scenarios for autonomous driving tasks. Due to
the complexity and stochasticity, essential applications (eg, behavior modeling, motion …
the complexity and stochasticity, essential applications (eg, behavior modeling, motion …
Using naturalistic driving data to identify driving style based on longitudinal driving operation conditions
N Lyu, Y Wang, C Wu, L Peng… - Journal of intelligent and …, 2022 - ieeexplore.ieee.org
Purpose-An individual's driving style significantly affects overall traffic safety. However,
driving style is difficult to identify due to temporal and spatial differences and scene …
driving style is difficult to identify due to temporal and spatial differences and scene …
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 …
[HTML][HTML] Uncertainty-aware probabilistic graph neural networks for road-level traffic crash prediction
Traffic crashes present substantial challenges to human safety and socio-economic
development in urban areas. Developing a reliable and responsible traffic crash prediction …
development in urban areas. Developing a reliable and responsible traffic crash prediction …
A transfer learning framework for proactive ramp metering performance assessment
Transportation agencies need to assess ramp metering performance when deploying or
expanding a ramp metering system. The evaluation of a ramp metering strategy is primarily …
expanding a ramp metering system. The evaluation of a ramp metering strategy is primarily …
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 …