A survey on physics informed reinforcement learning: Review and open problems
The inclusion of physical information in machine learning frameworks has revolutionized
many application areas. This involves enhancing the learning process by incorporating …
many application areas. This involves enhancing the learning process by incorporating …
ADCT-Net: Adaptive traffic forecasting neural network via dual-graphic cross-fused transformer
The rapid development of road traffic networks has provided a wealth of research data for
intelligent transportation systems. We are faced with vast high-dimensional traffic flow data …
intelligent transportation systems. We are faced with vast high-dimensional traffic flow data …
A deep marked graph process model for citywide traffic congestion forecasting
Forecasting citywide traffic congestion on large road networks has long been a nontrivial
research problem due to the challenge of modeling complex evolution patterns of …
research problem due to the challenge of modeling complex evolution patterns of …
Online spatio-temporal correlation-based federated learning for traffic flow forecasting
Traffic flow forecasting (TFF) is of great importance to the construction of Intelligent
Transportation Systems. To mitigate communication burden and tackle with the problem of …
Transportation Systems. To mitigate communication burden and tackle with the problem of …
Traffic Status Prediction Based on Multidimensional Feature Matching and 2nd-Order Hidden Markov Model (HMM)
Spatiotemporal data from urban road traffic are pivotal for intelligent transportation systems
and urban planning. Nonetheless, missing data in traffic datasets is a common challenge …
and urban planning. Nonetheless, missing data in traffic datasets is a common challenge …
Spatial–Temporal Dynamic Graph Convolutional Network With Interactive Learning for Traffic Forecasting
A Liu, Y Zhang - IEEE Transactions on Intelligent Transportation …, 2024 - ieeexplore.ieee.org
Accurate traffic forecasting is essential in urban traffic management, route planning, and flow
detection. Recent advances in spatial-temporal models have markedly improved the …
detection. Recent advances in spatial-temporal models have markedly improved the …
Predicting vehicle travel time on city streets for trip preplanning and predicting heavy traffic for proactive control of street congestion
S Nofal - Scientific reports, 2024 - nature.com
We investigate if the vehicle travel time after 6 h on a given street can be predicted, provided
the hourly vehicle travel time on the street in the last 19 h. Likewise, we examine if the traffic …
the hourly vehicle travel time on the street in the last 19 h. Likewise, we examine if the traffic …
TRECK: Long-Term Traffic Forecasting With Contrastive Representation Learning
Recent research mainly applies deep learning (DL) methods to short-term traffic forecasting.
However, there is a growing interest in long-term forecasting, which allows action …
However, there is a growing interest in long-term forecasting, which allows action …
An Integrated Intra-View and Inter-View Framework for Multiple Traffic Variable Data Simultaneous Recovery
Rapid advancements in traffic monitoring and sensing technologies have permitted the
multiplex and democratized gathering of numerous traffic data (eg speed, volume), depicting …
multiplex and democratized gathering of numerous traffic data (eg speed, volume), depicting …
Cost-effective Vehicle Recognition System in Challenging Environment Empowered by Micro-Pulse LiDAR and Edge AI
Vehicle recognition and classification are critical for a number of traffic applications, eg,
traffic signal control, traffic flow modeling, tolling, and logistics optimization. Commonly used …
traffic signal control, traffic flow modeling, tolling, and logistics optimization. Commonly used …