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.

利用新型声音传感技术扩大城市感知人群的能力，并评估基于麦克风传感器相比其他形式的行人感知的优势和局限性。提供一个包含高质量音频记录和视频记录的大规模数据集 ASPED，用于标记行人计数数据。基准分析显示了使用声音传感器进行行人跟踪的前景，尽管需要算法和技术改进使传感器能够实际使用。同时，演示了如何利用数据预测行人轨迹，并讨论了基于声音的行人感知在城市和交通规划中的应用场景。

利用城市感知技术了解行人活动：音频传感器的潜力

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

Predicting pedestrian movement is critical for human behavior analysis and
also for safe and efficient human-agent interactions. However, despite
significant advancements, it is still challenging for existing approaches to
capture the uncertainty and multimodality of human navigation decision making.
In this paper, we propose SocialVAE, a novel approach for human trajectory
prediction. The core of SocialVAE is a timewise variational autoencoder
architecture that exploits stochastic recurrent neural networks to perform
prediction, combined with a social attention mechanism and a backward posterior
approximation to allow for better extraction of pedestrian navigation
strategies. We show that SocialVAE improves current state-of-the-art
performance on several pedestrian trajectory prediction benchmarks, including
the ETH/UCY benchmark, Stanford Drone Dataset, and SportVU NBA movement
dataset. Code is available at: this https URL

本文提出了一种基于时间的变分自编码器的人行道预测方法，该方法结合了社交关注机制和向后后验近似，能够更好地提取行人导航策略，实现了在多个基准测试集上超越现有最先进性能。