UberPool or OlaShare, aim to change the mentality of people using both personal and public transportation in India. By providing fares at 30-50% discount of their regular fares, which are themselves subsidised, they have become an attractive alternative for people in urban India.
However, I have heard complaints of how these carpooling initiatives often end up wasting time and money for people. Some stories speak of drivers waiting way beyond the stipulated time for passengers and often turning back to add an additional passenger. Others are disgusted about fellow riders and often feel uncomfortable having to travel with strangers.
The true challenge for both Uber and Ola lies in the sales incentive structure that has been built. Designed to enable both customers and drivers to push carpooling, it rewards drivers who take up car pooling by considering each passenger as a ride, and customers get a discounted fare. This results in drivers often going way out of their route to pick up additional car poolers , even if that inconveniences the existing passengers.
At the same time, passengers have not been informed of the risks of carpooling. They expect the service to be an extension of the current services and are dismayed by the difference in service quality. This can have a impact on future usage of carpooling and the cab service itself.
The answer lies in technology. While both Ola and Uber are using machine learning algorithms and data science to help improve efficiency, they need to improve the algorithms and build in features to avoid wastage of time and petrol. After all improving the time per ride allows drivers to pick up car poolers and build on their incentives.
Similarly carpooling firms need to incorporate customer satisfaction into the incentive mechanism. If drivers realise that an unsatisfied customer will also impact their sales incentives, it might help reduce some of the incidents. However customers also need to understand that if they do not turn up on time, they can lose the fare / can be penalised by not being allowed for future car pooling rides.
The challenge lies in marrying technology with the human touch. Understanding not just the patterns of rides but also the subtle tricks played by drivers and customers. That’s where Uber can potentially use the data of a Didi Mau while Ola still lacks access to the data and talent pool who can suitable make sense of such data.
While driverless cars are still some time away, I foresee a point where we may have automated shuttles with no drives plying on roads.At this time, rather than rebuilding Indian cities to accommodate new modes of public transportation, maybe it’s time we focussed on how options like carpooling or driverless shuttles may help reduce congestion, along with an approach of charging drivers of cars , a la Singapore.