A Crowdsensing Approach for Deriving Surface Quality of Cycling Infrastructure

TL;DR

This paper introduces a novel crowdsensing method using smartphone sensors to assess cycling surface quality, enabling improved routing and infrastructure planning for safer, more comfortable bicycle paths.

Contribution

It presents an innovative edge-based crowdsensing approach that leverages cyclist smartphone data for surface quality analysis, enhancing infrastructure maintenance and routing decisions.

Findings

Effective surface quality assessment through crowdsensed data

Potential for surface quality-aware routing and planning

Scalable approach using smartphone sensors

Abstract

Cities worldwide are trying to increase the modal share of bicycle traffic to address traffic and carbon emission problems. Aside from safety, a key factor for this is the cycling comfort, including the surface quality of cycle paths. In this paper, we propose a novel edge-based crowdsensing method for analyzing the surface quality of bicycle paths using smartphone sensor data: Cyclists record their rides which after preprocessed on their phones before being uploaded to a private cloud backend. There, additional analysis modules aggregate data from all available rides to derive surface quality information which can then used for surface quality-aware routing and planning of infrastructure maintenance.