Large-Scale Assessment of Mobile Crowdsensed Data: A Case Study

Abstract

Mobile crowdsensing (MCS) is a well-established paradigm that leverages mobile devices' ubiquitous nature and processing capabilities for large-scale data collection to monitor phenomena of common interest. Crowd-powered data collection is significantly faster and more cost-effective than traditional methods. However, it poses challenges in assessing the accuracy and extracting information from large volumes of user-generated data. SmartRoadSense (SRS) is an MCS technology that utilises sensors embedded in mobile phones to monitor the quality of road surfaces by computing a crowdsensed road roughness index (referred to as $P_{PE}$ ). The present work performs statistical modelling of $P_{PE}$ to analyse its distribution across the road network and elucidate how it can be efficiently analysed and interpreted. Joint statistical analysis of open datasets is then carried out to investigate the effect of both internal and external road features on $P_{PE}$. Several road properties affecting $P_{PE}$ as predicted are identified, providing evidence that SRS can be effectively applied to assess road quality conditions. Finally, the effect of road category and the speed limit on the mean and standard deviation of $P_{PE}$ is evaluated, incorporating previous results on the relationship between vehicle speed and $P_{PE}$. These results enable more effective and confident use of the SRS platform and its data to help inform road construction and renovation decisions, especially where a lack of resources limits the use of conventional approaches. The work also exemplifies how crowdsensing technologies can benefit from open data integration and highlights the importance of making coherent, comprehensive, and well-structured open datasets available to the public.