Mt-stnet: A novel multi-task spatiotemporal network for highway traffic flow prediction
Multi-step highway traffic flow prediction is crucial for intelligent transportation systems, and
existing works have made significant advancements in this field. However, the physical …
existing works have made significant advancements in this field. However, the physical …
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 …
PI-STGnet: Physics-integrated spatiotemporal graph neural network with fundamental diagram learner for highway traffic flow prediction
At present, traffic state prediction primarily relies on purely data-driven methods, ignoring the
incorporation of physical constraints within the field of traffic flow. Taking this as a starting …
incorporation of physical constraints within the field of traffic flow. Taking this as a starting …
Decomposition with feature attention and graph convolution network for traffic forecasting
Y Liu, X Wu, Y Tang, X Li, D Sun, L Zheng - Knowledge-Based Systems, 2024 - Elsevier
Traffic forecasting is a crucial task for enhancing the quality and efficiency of intelligent
transportation systems. In recent years, several neural networks have been proposed to …
transportation systems. In recent years, several neural networks have been proposed to …
Multi-task-based spatiotemporal generative inference network: A novel framework for predicting the highway traffic speed
Accurately predicting the highway traffic speed can reduce traffic accidents and transit time,
and it also provides valuable reference data for traffic control in advance. Three essential …
and it also provides valuable reference data for traffic control in advance. Three essential …
Link representation learning for probabilistic travel time estimation
Personalized origin–destination travel time estimation with active adversarial inverse reinforcement learning and Transformer
Travel time estimation is important for instant delivery, vehicle routing, and ride-hailing. Most
studies estimate the travel time of specified routes, and only a few studies pay attention to …
studies estimate the travel time of specified routes, and only a few studies pay attention to …
Will it get there? A deep learning model for predicting next-trip state of charge in Urban Green Freight Delivery with electric vehicles
W Lu, Z Yuan, T Wang, P Li, Y Zhang - eTransportation, 2024 - Elsevier
To enhance urban freight efficiency and green development, China has implemented the
Urban Green Freight Delivery (UGFD) project, which includes optimizing vehicle traffic …
Urban Green Freight Delivery (UGFD) project, which includes optimizing vehicle traffic …
Cross-View Location Alignment Enhanced Spatial-Topological Aware Dual Transformer for Travel Time Estimation
Accurately estimating route travel time is crucial for intelligent transportation systems. Urban
road networks and routes can be viewed from spatial and topological perspectives while …
road networks and routes can be viewed from spatial and topological perspectives while …
An OD time prediction model based on adaptive graph embedding
R Wang, Q Guo, S Dai, L Deng, Y **ao, C Jia - Physica A: Statistical …, 2025 - Elsevier
The accuracy of origin–destination (OD) time prediction is critical for intelligent transportation
systems. To address the spatiotemporal challenges of sparse road network data, this paper …
systems. To address the spatiotemporal challenges of sparse road network data, this paper …