arXiv AI
Jun 18
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.
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).
Related stories
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.
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
Sep 21
Learning to Move Cities: Deep Meta-Models and Reinforcement Policies for Calibration and Control in Urban Networks
arXiv:2609.21945v1 Announce Type: new Abstract: Urban transportation networks present complex optimization challenges spanning calibration of high-fidelity simulators and real-time operational contro...
arXiv Machine Learning
Sep 23
JAMPR+/L2D: scalable neural heuristic for constrained vehicle routing problems in dynamic environment
arXiv:2609.26275v1 Announce Type: new Abstract: The vehicle routing problems with real-world constraints (we consider vehicles capacity limits, time windows constrains, pickup-and-delivery multi-depo...
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
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
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 Machine Learning
Aug 17
Smart routes: a system for development and comparison of algorithms for solving vehicle routing problems with realistic constraints
arXiv:2608. 14140v1 Announce Type: new Abstract: The problem of route optimization with realistic constraints is becoming extremely relevant in the face of global urban population growth.
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 AI
Aug 13
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.
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.