Interpretation, Modeling and Visualization of Crowdsourced Road Condition Data

Nowadays almost everyone has a mobile phone and even the most basic smartphones often come embedded with a variety of sensors. These sensors, in combination with a large user base, offer huge potential in the realization of crowdsourcing applications. The crowdsourcing aspect is of interest especially in situations where users’ everyday actions can generate data usable in more complex scenarios. The research goal in this paper is to introduce a combination of models for data gathering and analysis of the gathered data, enabling effective data processing of large data sets. Both models are applied and tested in the developed prototype system. In addition, the paper presents the test setup and results of the study, including a description of the web user interface used to illustrate road condition data. The data were collected by a group of users driving on roads in western Finland. Finally, it provides a discussion on the challenges faced in the implementation of the prototype system and a look at the problems related to the analysis of the collected data. In general, the collected data were discovered to be more useful in the assessment of the overall condition of roads, and less useful for finding specific problematic spots on roads, such as potholes.