Understanding the effects of drivers’ perceived quality of information and suggestions on the use of social navigation applications

Social navigation applications like Waze have recently gained popularity as more localities experience regular traffic congestions. They use crowd-sourced traffic data in suggesting the fastest route to its users. While most literature focus on designing efficient ways to sense data, and developing techniques to reduce their sparsity and error, there are not much work investigating how the perceived quality of provided traffic information and optimal routes affects actual navigating behaviors. In this preliminary work, we conducted an initial semi-structured qualitative study with six drivers that use social navigation applications. Apart from interviews, we also recorded some of their trips. Our initial analysis revealed their criteria in choosing a route to follow, changes in navigation behavior, and reliance on the prior knowledge of others. We plan to extend this in a future work to derive design considerations in improving the trustworthiness of social navigation systems.