Rideshare Drivers: Does it pay to be picky?

Adam Simkin
Mar 1, 2019 · 4 min read

It may be easy to forget that the rideshare industry, now worth $76 Billion (soon to be $285 Billion), and soon to bring us two of the largest IPOs in history, was founded on the concept of freelancing, part-time drivers, making an extra buck on the way. Even the name “ride sharing” comes from the original idea of “sharing” your vehicle with another person on your way to where you’re going. But we’ve come a long way since the days when Uber was still called UberCab, and we’re not sitting up front and fist bumping any more: Rideshare demand has grown aggressively, and as a result more and more drivers have started working in ride sharing full time, up to 60% of drivers in NYC drivers.

Drivers have responded by strategizing on how to optimize their rideshare work. There are dozens of books on the subject, websites and blogs dedicated to helping drivers maximize their rideshare potential, and even entire companies founded on the vision of maximizing driver earnings, like Mystro and Farepilot. The rideshare driver of today is starting to look less and less like someone working a side-hustle on their way to yoga; drivers are experienced, informed, and looking to maximize earnings.

Drivers are also facing challenges like they’ve never faced before. If it isn’t the taxi industry leading violent protests against them, or cities refusing to grant new licenses, then it is the increasing pressure to reach a livable wage: multiple studies have now shown that rideshare drivers take home less than minimum wage. Facing these obstacles, it’s not surprising that 68% of drivers leave Uber after 6 months.

This all means that now, perhaps more than ever, drivers are seeking the best way to maximize their income. But there is little consensus on the right way to do that: work airport routes or avoid them at all costs? Don’t chase the surge, but maybe sometimes chase it a little bit? Work weekends to catch the drunks or the morning after to catch the hangovers?

And, most importantly: accept everything or long rides only?

This topic has been debated by the greatest rideshare minds, most notably The Rideshare Guy, but also here and here and here and — a lot. Some say long rides lock you in with a paying customer and you don’t need to worry about catching the next ride. Others say long rides take you out of areas of high demand, and you’re always returning empty.

Here at autofleet we’re always thinking about sustainable wages for rideshare drivers, so we needed concrete answers, and to settle this debate once and for all.

Simulation and Analysis:

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Vehicles at work in our simulator in New York City

Using our proprietary ride and vehicle simulator, we started by ingesting a sample set of yellow cab data in NYC, and focused on 4 representative days of activity from 2018. For the test, we created two fleets of 20 drivers each, with one fleet accepting 100% of the orders that came in, and the second only accepting the top 20% longest distance ride orders. In addition, we configured the demand prediction in the simulation to assume that drivers are knowledgeable and experienced: they know where high demand areas are, and travel to the closest such area whenever they are free.

Results:

So, everything or just long rides? It depends when.

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The graph above shows the normalized revenue production per vehicle for each hour of the day. In the early morning hours, drivers accepting all orders perform better, but from then on the picky drivers, accepting only the longer rides, take the rest of the day. But what drives this outcome?

In the graph below, we plotted a preference score between accepting all rides and only taking long rides (Y-axis: The higher the score, the more advantageous to accept all rides), versus the number of ride requests overall per vehicle per hour.

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The relationship between these two variables seems logical: When the overall demand level is lower (upper left side of the graph), accept everything. But when demand goes up (bottom right side of the graph), it pays to be picky.

But there is a catch…

There’s a big problem with applying this in reality: Uber and Lyft do not show the customer’s destination to the driver until the ride has been started. There is the highly controversial method of ride screening, and third-party apps that offer a long trip filter, or drivers can use their experience to target areas that generally offer longer rides. But as long as rideshare operators rely on independent drivers, hiding the destination of the ride is one of the greatest tools they have to ensure all rides are accepted.

This is the paradox of rideshare companies today: independent drivers power their platforms, but if they were allowed to be truly independent, the system falls apart. Every platform is using advanced methods of behavioral economics to manipulate driver behavior in the desired direction, but drivers always seem to catch on. For example, most rideshare driver guides recommend ignoring surges completely.

What should the rideshare industry do? Autonomous vehicles won’t have these problems, because they can be matched to orders without driver intervention, and directed to optimal areas to balance supply and demand. But what happens until then?

We will try to answer that in our next post.

Meanwhile, we would love to hear your thoughts on this topic, please comment below or email us at hey@autofleet.io.

autofleet

Our mission is to create the first, truly sustainable…

Adam Simkin

Written by

Been maximizing fleet utilization and minimizing unpaid miles as long as I can remember. VP Business Development @autofleet.io.

autofleet

autofleet

Our mission is to create the first, truly sustainable, Vehicle as a Service layer, providing elastic supply of vehicles to any source of demand

Adam Simkin

Written by

Been maximizing fleet utilization and minimizing unpaid miles as long as I can remember. VP Business Development @autofleet.io.

autofleet

autofleet

Our mission is to create the first, truly sustainable, Vehicle as a Service layer, providing elastic supply of vehicles to any source of demand

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