When using smart mobile navigation to avoid urban congestion, we embody a big data thinking mode, that is, "relevance rather than causality". This way of thinking is based on the characteristics of big data analysis, and the relationship between data is more important than causality.
In the application of navigation, we usually don't care about the specific reasons of a certain section, but about whether the section is congested and how to bypass it. Big data analysis helps us identify which road sections are related to congestion, rather than exploring the root causes of congestion.
Intelligent mobile navigation applications rely on real-time data streams to provide accurate traffic information. These data come from other users' mobile phones, traffic cameras, sensors and so on. They are generated and analyzed at a very fast speed in order to provide users with real-time traffic conditions.
Characteristics of Big Data Analysis
By analyzing a large number of historical and real-time data, navigation applications can identify traffic patterns, such as which sections are prone to congestion in a specific time period. These models help the application to predict future traffic conditions and advise users to choose the best route. Big data analysis usually involves probabilistic prediction, not deterministic prediction. Route suggestions provided by navigation applications are based on probabilistic models, which predict which routes are most likely to avoid congestion, rather than ensuring that they must avoid congestion.
The navigation application will also analyze users' driving habits and preferences to provide more personalized route suggestions. This analysis is not based on causal mechanism, but on the correlation of user behavior. When using navigation applications, users and systems are making data-driven decisions. These decisions are not based on intuition or experience, but on the results of data analysis.