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Enhancing the business model and improving driver compensation in the Ride Sharing Industry

Enhancing the business model and improving driver compensation in the Ride Sharing Industry

GEORGIA – 13 Jul, 2017 – The common refrain in the on-demand transportation space is that drivers are not being compensated properly and as a result there is a very high churn rate in the industry. Support from the public is being sort from drivers to complete a survey found here https://www.surveymonkey.com/r/researchondrivers The results from the survey will help to provide scientific evidence to underpin the case for a change of strategy by current players in the industry.

It is understood that one of the big issues in the current deployed Transport Network Companies (TNCs) models is that of poor vehicle/driver utilization rates.  This became a topic of interest to Mr. Michael Treasure while pursuing his Doctoral degree program research at the George Washington University in the area of on demand transportation and the usage of autonomous vehicle in the ride sharing space.  This interest lead Mr. Treasure to have personally carried out, in the Atlanta market, over 1,250 rides on both Uber’s & Lyft’s platforms. In carrying out these rides Mr. Treasure carefully and methodologically documented each ride, collecting data such as total miles driven on each ride, the portion of the ride in which the rider was present, unloaded miles and the wait times between rides.   

The results from his research carried out on the data shows that the average loaded trip miles by a vehicle/driver was 8.87 miles, while the average unloaded trip mile was 4.21 miles. The conclusion from this was that a vehicle/driver unloaded miles per trip relative to loaded miles per trip was approximately 47.46%. That is, for every 8.87 miles driven with a passenger the vehicle/driver traveled 4.21 miles without a passenger. Additionally, his analysis showed that the average ride time of a loaded trip was 18.51 minutes, while the average wait time for the next ride was 23.66 minutes. The conclusion Mr. Treasure drew from analysis of the data was that on average, in any given period of a vehicle’s/driver’s work shift, the vehicle/driver spent more time waiting for the next ride than spent giving a rider a ride.

These two results combined and highlighted in his research meant that that the vehicle/driver utilization rates, measured in loaded miles relative to unloaded miles and trip time relative to wait time, were approximately 52% and 48% respectively. These poor utilization rates revelation led Mr. Treasure to conclude that there were significant improvement potentials to be had in the TNC model for vehicle/driver utilization rates.

How does Mr. Treasure’s results translate to driver earnings with existing TNCs? He was able to use the data to develop a model to determine, for example, if one was interested in finding the average day’s revenue for 150 loaded miles travelled with 300 minutes of trip time for that day, a driver would earn $133.30. However, from the calculated utilization rates, it would have meant that the vehicle/driver would have amassed for the day another 138 unloaded miles and 398 minutes of wait time. Looked at in another context, this would have meant that the vehicle/driver would have had an 11 hour and 38 minutes’ work day, driving more than 288 miles for the day, which would translate to less than 12/hr. in gross revenue or approximately $0.46/mile. These poor results would in part explain why drivers are grossly unhappy with their earnings, majority just work a few hours per week and why there is such high churn rates in the industry.

Mr. Treasure’s hypothesis is that a TNC, with focus on optimizing revenue/hr., minimizing vehicle unloaded miles and minimizing vehicle wait time, will be able to achieve better utilization rates, and provide drivers with a much more equitable revenue share. This would then lead to the vehicle/driver pool levels used in a TNC model to be rebalanced to longer term workers and better vehicle utilization, which will lower the aggregate pool level of vehicles needed to satisfy ridership demand. Such a strategy will be essential to a TNC’s profitability in augmenting drivers with autonomous vehicle deployment.

To scientifically test his hypothesis, Mr. Treasure has taken a two-prong strategy. First, he is conducting a driver survey to help established whether drivers, if compensated better, and without TNCs having to increase their effective pay out rate to drivers, would be willing to work longer hours and for longer periods. The survey may be found here https://www.surveymonkey.com/r/researchondrivers. Second, he has developed an algorithm to solve the utilization problem and has founded a company along with his cofounders to commercially demonstrate, at scale, the viability of his hypothesis. This new TNC commercial model has been deployed under the brand Lymousine (www.Lymousine.com).

If this is an area of interest to you and you want to see improvements in the structure of the compensation relationship between drivers and ride sharing companies then if you are a driver, we invite you to please take the survey here https://www.surverymoneky.com/r/researchondrivers. If you are a rider and is interested in helping to support a better compensation structure for riders then visit www.Lymousine.com to learn more.

Drivers who complete the survey will be placed in raffle to win 1 of 3 prizes for an Amazon gift card worth up to $100.

Media Contact
Company Name: TreasureCom Technologies, Inc
Contact Person: Michael Treasure
Email: [email protected]
Country: United States
Website: www.Lymousine.com