Web scraping of driiveme
I worked for several seasons for a travel insurance company that organized rental vehicles for foreign clients with broken-down cars. Every summer there was a shortage of vehicles in the tourist areas that forced us to organize transport services up to 100km for our clients. It was an ordeal for large families on a low budget.
I discovered that this shortage was due to the rental of the entire fleet, and also to the vehicles that were “disappearing”. After having been in contact with all the big agencies in France, I discovered that during the summer period a large number of vehicles were migrating from tourist areas to less frequented agencies.
I also discovered driver services that re-routed these vehicles one by one, like the Driiveme.com website that offers to any license holder to repatriate these vehicles for free. In the form of advantageous rentals, you provide a service to agencies.
For statistical and predictive purposes, I have set up a web scraping program that will constantly collect the site’s announcements in order to draw up a map of the routes according to time and to predict the surplus and shortage of vehicles from one region to another.
The program is in python, intelligently manages web data downloads, and includes logging and external mailing functionality. [source]
Collection of web data by Python program to predict the rental vehicle fleet depending on tourist seasons.