mlsanityData to 14 Oct 2026

Understanding Pedestrian Movement Using Urban Sensing Technologies: The Promise of Audio-based Sensors

arXiv 2406.09998 ↗First posted 14 Jun 2024eess.AS cs.AI cs.LGcited by 1

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Abstract (arXiv)

While various sensors have been deployed to monitor vehicular flows, sensing pedestrian movement is still nascent. Yet walking is a significant mode of travel in many cities, especially those in Europe, Africa, and Asia. Understanding pedestrian volumes and flows is essential for designing safer and more attractive pedestrian infrastructure and for controlling periodic overcrowding. This study discusses a new approach to scale up urban sensing of people with the help of novel audio-based technology. It assesses the benefits and limitations of microphone-based sensors as compared to other forms of pedestrian sensing. A large-scale dataset called ASPED is presented, which includes high-quality audio recordings along with video recordings used for labeling the pedestrian count data. The baseline analyses highlight the promise of using audio sensors for pedestrian tracking, although algorithmic and technological improvements to make the sensors practically usable continue. This study also demonstrates how the data can be leveraged to predict pedestrian trajectories. Finally, it discusses the use cases and scenarios where audio-based pedestrian sensing can support better urban and transportation planning.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 11 of its 48 references that are in the map. First posted on arXiv 14 Jun 2024.

Builds on

11 of 48 references.

References · 11

  1. 2017Attention Is All You Needcited by 60,361
  2. 2021Masked-attention Mask Transformer for Universal Image Segmentationcited by 2,351
  3. 2016CNN Architectures for Large-Scale Audio Classificationcited by 704
  4. 2021AST: Audio Spectrogram Transformercited by 643
  5. 2016Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Predictioncited by 333
  6. 2020Trajectory Forecasts in Unknown Environments Conditioned on Grid-Based Planscited by 72
  7. 2019Predicting Citywide Crowd Flows in Irregular Regions Using Multi-View Graph Convolutional Networkscited by 40
  8. 2017Predicting Citywide Crowd Flows Using Deep Spatio-Temporal Residual Networkscited by 34
  9. 2023Metropolitan Scale and Longitudinal Dataset of Anonymized Human Mobility Trajectoriescited by 7
  10. 2023ASPED: An Audio Dataset for Detecting Pedestrianscited by 3
  11. 2018Real-time Pedestrian Detection Approach with an Efficient Data Communication Bandwidth Strategycited by 1

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