arXiv AI
Jun 18
Large-Scale OD Matrix Estimation with A Deep Learning Method
arXiv:2310. 05753v2 Announce Type: replace Abstract: The estimation of origin-destination (OD) matrices is a crucial aspect of Intelligent Transport Systems (ITS).
A DeepLearning Framework for Dynamic Estimation of Origin-Destination Sequence
arXiv:2307. 05623v2 Announce Type: replace-cross Abstract: OD matrix estimation is a critical problem in the transportation domain.
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arXiv Machine Learning
Jul 8
Deep Reinforcement Learning for Dynamic Origin-Destination Matrix Estimation in Microscopic Traffic Simulations Considering Credit Assignment
arXiv:2511. 06229v3 Announce Type: replace Abstract: This paper focuses on dynamic origin-destination matrix estimation (DODE), a crucial calibration process necessary for the effective application of microscopic traffic simulations.
arXiv Machine Learning
Jul 30
Neural Architecture Search for Traffic Prediction: A Survey of Methods, Challenges, and Future Directions
arXiv:2607. 26467v1 Announce Type: new Abstract: Traffic prediction is a core task in intelligent transportation systems, supporting applications such as adaptive signal control, route guidance, and ride-hailing dispatch.
Hugging Face Trending Papers
Jul 29
Neural Architecture Search for Traffic Prediction: A Survey of Methods, Challenges, and Future Directions
Traffic prediction is a core task in intelligent transportation systems, supporting applications such as adaptive signal control, route guidance, and ride-hailing dispatch. Deep learning models, including graph convolutional networks, recurrent networks, and Transformers, achieve strong results on standard benchmarks, but their architectures are designed by hand, requiring significant expert effort and producing models that often generalize poorly across cities and datasets.
arXiv AI
Jul 28
Capacity-Aware Deep Learning for Generalizable Traffic Volume Estimation Across Links and Cities
arXiv:2607. 24056v1 Announce Type: cross Abstract: Network-wide traffic volume estimation typically relies on propagating measurements from fixed sensors, making performance highly dependent on sensor density and limiting deployment in sparsely instrumented networks.
arXiv AI
Jun 15
Q-Net: Queue Length Estimation via Kalman-based Neural Networks
arXiv:2509. 24725v4 Announce Type: replace-cross Abstract: Estimating queue lengths at signalized intersections is a long-standing challenge in traffic management.
arXiv AI
Jun 6
AIS-Based Vessel Trajectory Prediction Using Memory-Augmented Neural Networks
arXiv:2606. 06311v1 Announce Type: new Abstract: Accurate vessel trajectory prediction is essential for safe and efficient maritime operations, enabling collision avoidance and supporting route optimization.
arXiv AI
2d ago
Simulation-Driven Vehicular Traffic Data Augmentation: Extending Sensor Coverage Through Virtual Sensing
arXiv:2608. 13993v1 Announce Type: new Abstract: Urban traffic management relies on sensor networks whose spatial coverage is limited by deployment costs and privacy regulations.
arXiv AI
Jul 22
GUIDED Network-Agnostic Feature Initialization for Spatial Transferability in GNN-based Models
arXiv:2607. 19270v1 Announce Type: cross Abstract: The Traffic Assignment Problem is a fundamental but computationally expensive component of transportation planning.
arXiv Machine Learning
Jul 7
Deep Learning Network-Temporal Models For Traffic Prediction
arXiv:2603. 11475v2 Announce Type: replace Abstract: Accurate prediction of multivariate time series is essential for emerging network intelligent control, observability, and management functions.
arXiv AI
Jun 30
Instance-Conditioned Adaptation for Large-scale Generalization of Neural Routing Solver
arXiv:2405. 01906v3 Announce Type: replace Abstract: In modern intelligent transportation systems (ITS), particularly in freight transportation and logistics, real-time route planning is crucial.
arXiv AI
6d ago
Learning-Based Behavior Planning for Automated Driving: Real-World Integration and Deployment
arXiv:2608. 12198v1 Announce Type: cross Abstract: Recent research in machine and deep learning has shown the potential of learningbased motion planning approaches to improve the driving behavior of automated vehicles, especially in complex environments.