An aggregation and visualization technique for crowd-sourced continuous monitoring of transport infrastructures

Abstract

Smartphones have revolutionized the way infrastructure health monitoring applications operate. Their ubiquitous sensing and communication capabilities have made measurement data for infrastructural health monitoring applications easily available. They, however, also introduced a new challenge, namely the huge amount of data that is generated. This new reality prompts the need for efficient techniques to handle, process, aggregate, and visualize this huge amount of streaming data. Continuous monitoring of transport infrastructures using crowd-sensing is a new concept being inspired by the power of active participation and contribution of the general public in provision of real-time measurement data about infrastructure characteristic. Even though, accuracy of smartphone's GPS is usually quite acceptable, the accumulated error can lead to large deviations, especially in presence of high speed. In this paper, we present a new algorithm for map matching of crowd-sourced measurements for monitoring ground transportation infrastructures to alleviate the impact of GPS inaccuracies for continuous monitoring of transport infrastructures using smartphones.