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

Weijiang Xiong, Robert Fonod, Alexandre Alahi, Nikolas Geroliminis

TL;DR

This paper tackles urban traffic speed forecasting by fusing drone-derived observations with loop-detector data to predict both segment-level and regional speeds. It presents HiMSNet, a concise spatio-temporal graph model with a Local State Evolution encoder and Global Message Exchange to integrate multi-source data, and introduces SimBarca, a simulation-based Barcelona dataset for large-scale evaluation. Through extensive experiments, it demonstrates that drone data significantly improves predictive accuracy, particularly in partial/noisy-data scenarios, and that incorporating spatial correlations via GME further enhances performance. The work offers practical insights for deploying drone-assisted traffic monitoring and provides open data and code to advance research in multi-source urban forecasting.

Abstract

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.

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

TL;DR

This paper tackles urban traffic speed forecasting by fusing drone-derived observations with loop-detector data to predict both segment-level and regional speeds. It presents HiMSNet, a concise spatio-temporal graph model with a Local State Evolution encoder and Global Message Exchange to integrate multi-source data, and introduces SimBarca, a simulation-based Barcelona dataset for large-scale evaluation. Through extensive experiments, it demonstrates that drone data significantly improves predictive accuracy, particularly in partial/noisy-data scenarios, and that incorporating spatial correlations via GME further enhances performance. The work offers practical insights for deploying drone-assisted traffic monitoring and provides open data and code to advance research in multi-source urban forecasting.

Abstract

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.
Paper Structure (23 sections, 11 equations, 17 figures, 3 tables)

This paper contains 23 sections, 11 equations, 17 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. GNN-Based Methods
  4. Transformer-Based Methods
  5. Research Gap and Contribution
  6. Methodology
  7. Multi-Source Traffic Forecasting
  8. Model Architecture
  9. Overall Pipeline
  10. Building Blocks and Implementation
  11. Dataset Creation
  12. Simulate Vehicle Trajectories
  13. Compute Traffic Variables
  14. Sample Extraction and Sensor Simulation
  15. Experiments
  16. ...and 8 more sections

Figures (17)

  • Figure 1: The two-layer model structure of HiMSNet. The local layer processes all available time series data of a road segment, and concatenates the feature vectors of different data modalities into a joint feature. The global layer exchanges messages across the road network, and enhance the local joint features with vicinity information.
  • Figure 2: Regional prediction branch. This branch groups the segment-level features by spatial regions (Figure \ref{['fig: region cluster and grid']}), averages the features by group, and use them to predict regional traffic.
  • Figure 3: The simulated urban transportation network with detailed views of an intersection and a road segment (ID 9971), which will be used as visualization example in the experiments.
  • Figure 4: Traffic variable extraction with trajectory splits
  • Figure 5: Training sample extraction using a sliding window approach
  • ...and 12 more figures