Leveraging Sidewalk Robots for Walkability-Related Analyses

TL;DR

This paper investigates whether sidewalk delivery robots can serve as scalable platforms for walkability analysis. It introduces a three-domain feature framework derived from robot sensor data and validates it with field data from a KTH campus deployment (101 trips, 900 segment records). The study finds that sidewalk characteristics such as density, width, and surface irregularity significantly shape pedestrian speeds and trajectories, and that robot speeds track pedestrian dynamics, enabling a real-time proxy for walkability. The proposed data-collection framework supports continuous monitoring to inform urban design, maintenance prioritization, and inclusive, walking-friendly city development.

Abstract

Walkability is a key component of sustainable urban development. In walkability studies, collecting detailed pedestrian infrastructure data 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 segment records. 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.

Figures (11)

• Figure 1: Work flow of sidewalk robot-based data collection and processing for walkability analysis
• Figure 2: SVEA robot
• Figure 3: Network used in the experiments on KTH campus
• Figure 4: Sidewalk segmentation
• Figure 5: Pedestrian detection