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Abstract 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. 

The detection and counting of pedestrians plays a central role for the design of smart cities. Although the use of cameras for this task has been shown to have high accuracy, they come at a high cost and are susceptible to challenges such as poor lighting, fog, and obstructed views. Our study investigates audio-based pedestrian detection, combining potentially low cost sensors with advanced machine learning based audio analysis algorithms. With an audio sensor installed along the walkway, machine learning algorithms can tell from the audio whether there is a pedestrian or not, or how far the pedestrian is from the sensor. Results show the general feasibility of audio-based pedestrian detection but fall short of reaching the accuracy levels of video-based detection. 

MCVT (Making Computing Visible and Tangible) Cards are a toolkit of paper-based computing cards intended for use in the codesign of inclusive computing education. Working with groups of teachers and students over multiple design sessions, we share our toolkit, design drivers and material considerations; and use cases drawn from a week-long codesign workshop where seven teachers made and adapted cards for their future classroom facilitation. Our findings suggest that teachers valued the MCVT toolkit as a resource for their own learning and perceived the cards to be useful for supporting new computational practices, specifically for learning through making and connecting to examples of everyday computing. Critically reviewed by teachers during codesign workshops, the toolkit however posed some implementation challenges and constraints for learning through making and troubleshooting circuitry. From teacher surveys, interviews, workshop video recordings, and teacher-constructed projects, we show how teachers codesigned new design prototypes and pedagogical activities while also adapting and extending paper-based computing materials so their students could take advantage of the unique technical and expressive affordances of MCVT Cards. Our design research contributes a new perspective on using interactive paper computing cards as a medium for instructional materials development to support more inclusive computing education.