The amount of fare that Uber and Lyft users pay depends on various factors, which include the type of car, distance, time, city, base rates, and chances of matching for the sake of pooling. A simple fare calculation model used often entails the addition of base fare, distance, and time. However, credit for the lyft app estimates varry because others factors such as car type and demand and supply dynamics have to be factored in, in cases where demand outstrips supply due to emergencies or peak commuting hours.
Basic Fare Determination
Fare pricing starts with the base rate, which depends on the type of car that a rider opts to use. The base rates may also differ based on the city of service.The base rate is then added to the estimated cost per mile to determine the fare. The basic fare estimate can be determined from provided charts, but it is prudent to note that the actual fare may differ due to surge pricing.
Demand and supply
Demand often outstrips supply in peak commuting hours or during emergencies. As such, demand and supply are among the factors that determine prices on Uber. Uber has implemented a dynamic pricing policy that helps in determining prices during high demand and emergency periods.
When demand increases, the wait time and unfulfilled requests also increase. Such scenarios often occur in the early morning when most drivers take their time off to rest on weekends when revelers want to make their home rest too. In such cases, Uber temporarily increases the fare rates so as to attract more taxi drivers that will fulfill the increase in requests.
Matching Up and Pooling
Matching up for the sake of pooling is also one among the factors that determine fare prices on Uber. When there is a likelihood of riders sharing one taxi on a route, Uber’s algorithm lowers the charge based on the calculated chance of matching. Matching up for pooling purposes is often common when most people are heading to or leaving the city center during peak commuting hours.
Surge pricing that occurs as a result of dynamics that are present in emergencies may lead to very high pricing. To avoid such occurrences, Uber has set an algorithm that determines maximum surge pricing in certain cities in cases of emergencies. For example, during disasters, Uber sets fare prices at a level that matches the particular town’s highest price in the previous month.
Additionally, Uber service launch periods also affect fare pricing because the company heavily subsidizes the cost of rides during launches to attract more users of their services. In essence, a rough estimate of fare depends on an amalgam of all these factors.…