mlsanityData to 7 Oct 2026

Multi-Source Urban Traffic Flow Forecasting with Drone and Loop Detector Data

arXiv 2501.03492 ↗First posted 7 Jan 2025cs.LGcited by 3

Not on the map. Why?

Abstract (arXiv)

Traffic forecasting is a fundamental task in transportation research, however the scope of current research has mainly focused on a single data modality of loop detectors. Recently, the advances in Artificial Intelligence and drone technologies have made possible novel solutions for efficient, accurate and flexible aerial observations of urban traffic. As a promising traffic monitoring approach, drone-captured data can create an accurate multi-sensor mobility observatory for large-scale urban networks, when combined with existing infrastructure. Therefore, this paper investigates the problem of multi-source traffic speed prediction, simultaneously using drone and loop detector data. A simple yet effective graph-based model HiMSNet is proposed to integrate multiple data modalities and learn spatio-temporal correlations. Detailed analysis shows that predicting accurate segment-level speed is more challenging than the regional speed, especially under high-demand scenarios with heavier congestions and varying traffic dynamics. Utilizing both drone and loop detector data, the prediction accuracy can be improved compared to single-modality cases, when the sensors have lower coverages and are subject to noise. Our simulation study based on vehicle trajectories in a real urban road network has highlighted the added value of integrating drones in traffic forecasting and monitoring.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 24 of its 67 references that are in the map. First posted on arXiv 7 Jan 2025.

Builds on

24 of 67 references.

References · 24

  1. 2017Attention Is All You Needcited by 59,932
  2. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  3. 2021Masked Autoencoders Are Scalable Vision Learnerscited by 7,339
  4. 2022Are Transformers Effective for Time Series Forecasting?cited by 1,400
  5. 2017Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecastingcited by 1,125
  6. 2019Graph WaveNet for Deep Spatial-Temporal Graph Modelingcited by 820
  7. 2020Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networkscited by 563
  8. 2020Adaptive Graph Convolutional Recurrent Network for Traffic Forecastingcited by 525
  9. 2019MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixingcited by 442
  10. 2019GMAN: A Graph Multi-Attention Network for Traffic Predictioncited by 433
  11. 2018T-GCN: A Temporal Graph ConvolutionalNetwork for Traffic Predictioncited by 399
  12. 2018The highD Dataset: A Drone Dataset of Naturalistic Vehicle Trajectories on German Highways for Validation of Highly Automated Driving Systemscited by 276
12 more
  1. 2020Spatial-Temporal Transformer Networks for Traffic Flow Forecastingcited by 179
  2. 2022Spatial-Temporal Identity: A Simple yet Effective Baseline for Multivariate Time Series Forecastingcited by 149
  3. 2018Traffic Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecastingcited by 145
  4. 2023Spatio-Temporal Graph Neural Networks for Predictive Learning in Urban Computing: A Surveycited by 140
  5. 2019Set Functions for Time Seriescited by 137
  6. 2023PDFormer: Propagation Delay-Aware Dynamic Long-Range Transformer for Traffic Flow Predictioncited by 135
  7. 2022Pre-training Enhanced Spatial-temporal Graph Neural Network for Multivariate Time Series Forecastingcited by 117
  8. 2021Learning dynamic and hierarchical traffic spatiotemporal features with Transformercited by 26
  9. 2022Two-layer adaptive signal control framework for large-scale dynamically-congested networks: Combining efficient Max Pressure with Perimeter Controlcited by 12
  10. 2019Incorporating dynamicity of transportation network with multi-weight traffic graph convolutional network for traffic forecastingcited by 11
  11. 2024Advanced computer vision for extracting georeferenced vehicle trajectories from drone imagerycited by 10
  12. 2022Evaluating a Signalized Intersection Performance Using Unmanned Aerial Datacited by 3

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 1.

3 papers.

  1. 2026Unveiling Stochasticity: Universal Multi-modal Probabilistic Modeling for Traffic Forecastingcited by 1
  2. 2025Scalable Dynamic Origin-Destination Demand Estimation Enhanced by High-Resolution Satellite Imagery Datacited by 0
  3. 2025Ctx2TrajGen: Traffic Context-Aware Microscale Vehicle Trajectories using Generative Adversarial Imitation Learningcited by 0