Analyzing Road Users' Behavior: A Data Mining Approach Using Google Maps Popular Time and Web Scraping for Rest Area Visitation Patterns on Highways and Toll Roads.

This study aims to analyze the usage patterns of rest areas on highways and toll roads using a data mining approach, utilizing popular time information from Google Maps and web scraping techniques. We collected popular time data for rest areas along highways and expressways using web scraping technology and Google Maps API. The research methodology includes a data collection phase that extracts popular time information from Google Maps using web scraping techniques. The collected data was then analyzed using data mining techniques to identify road user visitation patterns, specifically at rest areas. The analysis included understanding the most popular times visitors visit rest areas and providing deep insights into road user preferences. The findings indicate specific time patterns during which visitors visit rest areas. These findings can have important implications in the planning and managing of road infrastructure, enabling improved services at rest areas according to road users' needs. They can also assist in determining appropriate locations for constructing new rest areas or necessary improvements to existing rest areas. [ABSTRACT FROM AUTHOR]