Typically when associating with Uber rides, the riders are provided with the option to rate the drivers. In the event, the Uber ride is rated below 5 stars then the app will prompt the rider to get information as to what went awry and amiss. Feedback is shared with the driver base in an incognito mode, which gives them comprehension as to where the mess up originated and why they did not obtain the full stellar rating. Currently, the tables have turned where the driver base can now rate the riders from henceforth. Uber displays a list of reasons which the driver base could provide if the rating for a passenger was less than 5 stars.
They are displayed below constituting Wait Time, Number of Riders, Patience associated, Behavior, Seeking new routes and finally Others.
If the passenger has been subject to a bad rating from only one driver in an unusual case, then it wont matter much. In the event, the specific rider has been blackmarked for the same reason in a month’s timeframe then the rider will be alerted regarding the same and the passenger has to ensure that he or she should not repeat the mistake once again when going for the next ride.
The rider feedback feature has been there ever since a very long time. However, this is the first time they will be aware of the same.
The quick growth tools bolster the enterprises to progress ahead as the brands try to resist the incessant multiplying need from the brands related to extra apps.
The change is relevant to mobiles being featured for quicker digital capability encompassing the internet of things, business program administration and augmented actuality. Both ambient intelligence and artificial intelligence will make strides this year.
This new feature is related to Uber’s campaign named “180 days of change”. Further recent changes benefitting the drivers will encompass the aspect of tipping and be providing a wide range of options related to rides cancellation.
Featured along with the Uber’s new passenger rating attribute, Uber is further implementing a brand new flat rate for every UberPool passenger the driver base picks up. This concept of Pool riders is a menace and source of annoyance for the riders as they are associated with less pay. The pool rides presently will be associated with Pickup fares which vary from city to city and are associated to be $.50 in Miami and ranges to $1.00 for every ride in the city of San Francisco.
The pool routes now come featured with relatively few turns and the rider base will be henceforth requested to avail the rides by going to the nearest street corner for the ride rather than wait from their house doorsteps to get a ride.
Ambient intelligence examples feature powerful building systems which respond when in the vicinity of the user base and the environment where the lighting and temperature are changed accordingly.
There will be a growth in the manipulation of chatbot where they will be featured with a voice-controlled user connection this year.
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