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

arXiv: https://arxiv.org/abs/2406.09998 · HTML: https://mlsanity.com/paper/2406.09998/

## Key facts

- Cited by 1 papers 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 (most cited first, 11 of 11)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Masked-attention Mask Transformer for Universal Image Segmentation](https://mlsanity.com/paper/2112.01527/) (2021)
- [CNN Architectures for Large-Scale Audio Classification](https://mlsanity.com/paper/1609.09430/) (2016)
- [AST: Audio Spectrogram Transformer](https://mlsanity.com/paper/2104.01778/) (2021)
- [Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction](https://mlsanity.com/paper/1610.00081/) (2016)
- [Trajectory Forecasts in Unknown Environments Conditioned on Grid-Based Plans](https://mlsanity.com/paper/2001.00735/) (2020)
- [Predicting Citywide Crowd Flows in Irregular Regions Using Multi-View Graph Convolutional Networks](https://mlsanity.com/paper/1903.07789/) (2019)
- [Predicting Citywide Crowd Flows Using Deep Spatio-Temporal Residual Networks](https://mlsanity.com/paper/1701.02543/) (2017)
- [Metropolitan Scale and Longitudinal Dataset of Anonymized Human Mobility Trajectories](https://mlsanity.com/paper/2307.03401/) (2023)
- [ASPED: An Audio Dataset for Detecting Pedestrians](https://mlsanity.com/paper/2309.06531/) (2023)
- [Real-time Pedestrian Detection Approach with an Efficient Data Communication Bandwidth Strategy](https://mlsanity.com/paper/1808.09023/) (2018)

## Built on by (most cited first, 1 of 1)

- [Audio-Based Pedestrian Detection in the Presence of Vehicular Noise](https://mlsanity.com/paper/2509.19295/) (2025)

## 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.