Leveraging Sidewalk Robots for Walkability-Related Analyses

2025-07-16

AI Summary

To address the high cost, poor scalability, and lack of real-time monitoring capability in conventional walkability assessment, this paper pioneers the repurposing of sidewalk delivery robots as mobile sensing platforms, establishing a multi-sensor fusion framework for dynamic, fine-grained pedestrian environment acquisition. Methodologically, we integrate IMU, RGB-D cameras, and GNSS data, coupled with trajectory analysis, surface irregularity detection, and pedestrian density estimation; robot motion features—such as speed and acceleration—serve as proxy indicators of pedestrian behavior, enabling automated, continuous extraction of sidewalk-level walkability features. Evaluated across 900 sidewalk segments and 101 real-world traversals, our approach demonstrates strong correlation (r > 0.85) between robot motion patterns and actual pedestrian behavior, and quantitatively reveals significant impacts of sidewalk width, pavement quality, and other factors on walking experience. This work establishes a novel, low-cost paradigm for walkability assessment with high spatiotemporal resolution.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Planning, Routing, and Scheduling: Activity and Plan Recognition
• Natural Language Processing: Safety and Robustness

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Security and Privacy: Large-scale security measurements
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

Walkability is a key component of sustainable urban development, while collecting detailed data on its related features remains challenging due to the high costs and limited scalability of traditional methods. Sidewalk delivery robots, increasingly deployed in urban environments, offer a promising solution to these limitations. This paper explores how these robots can serve as mobile data collection platforms, capturing sidewalk-level features related to walkability in a scalable, automated, and real-time manner. A sensor-equipped robot was deployed on a sidewalk network at KTH in Stockholm, completing 101 trips covering 900 segments. From the collected data, different typologies of features are derived, including robot trip characteristics (e.g., speed, duration), sidewalk conditions (e.g., width, surface unevenness), and sidewalk utilization (e.g., pedestrian density). Their walkability-related implications were investigated with a series of analyses. The results demonstrate that pedestrian movement patterns are strongly influenced by sidewalk characteristics, with higher density, reduced width, and surface irregularity associated with slower and more variable trajectories. Notably, robot speed closely mirrors pedestrian behavior, highlighting its potential as a proxy for assessing pedestrian dynamics. The proposed framework enables continuous monitoring of sidewalk conditions and pedestrian behavior, contributing to the development of more walkable, inclusive, and responsive urban environments.

Problem

• Using sidewalk robots to collect walkability data efficiently
• Analyzing sidewalk features' impact on pedestrian movement patterns
• Developing a framework for continuous urban walkability monitoring

Innovation

• Using sidewalk robots for walkability data collection
• Automated real-time sidewalk feature analysis
• Robot speed as proxy for pedestrian dynamics