How to classify/cluster clients of an application of transport like the Uber Model? I have a question about User segmentation 
I have a data-set of rides of an application that works at the same model as Uber. 
The attributes I have : Reservation it means the id of the Ride and statutCourse means the status of the ride cancelled or finished .....
the Id client and the entreprise is the company where the client works IDChaufeur is conductor ID and i have the geographic coordinations of the start point and the finish point i also have date and hour of the ride also real time and estimated time and estimated distance 



I want to classify/cluster clients profiles and conductors profiles

My problem is that one client can make many rides and I don't know if I can use many lines of rides for the same client in one dataset and than use this dataset to classify the clients into classes or profiles ?
If you want to cluster people, you need to have one row per person.
To do this you will need to group by IDClient/IDChauffeur and compute features like meanHourRide, stdHourRide, ...

By feeding the algorithm with multiple records of the same person (with the same travels for example), the clustering algorithm might create a cluster with similar travels (time/location/distance-wise) even though it is made by only one customer.

This would result in travel clustering, which is also possible but not what you want here.