A Tale of Ride-hailing: Experiences from Austin, Texas
Detailed ridership data from a ride-hailing company in Austin, Texas shed light on how the “Uber-Economy” is impacting the local transportation system
Highlights
- Ride Austin stepped in as the ride-hailing alternative when Uber and Lyft ceased operations in Austin, Texas in 2016.
- In 2017, they released data for all trips (1.5 million) booked on the platform between June 2016 and April 2017.
- Of 250,000 unique riders, the top 40,000 riders (12.5%) made over 55% of all trips.
- Ride-hailing was found to compete with transit — over 60% of trips on Ride Austin could have been by competitive transit offering a single-seat ride.
- 35–40% of all miles traversed by the drivers were dead-head miles — leading to added traffic on the roads.
Ride-hailing services, as the flag-bearer of the “shared economy”, have transformed the mobility landscape of cities and disrupted the existing transportation planning and policy-making paradigms. Cities have been grappling with booming congestion, environmental concerns, and mismanaged curb spaces since the upturn of the economy following the 2008 recession, and many of these externalities have been linked to ride-hailing services like Uber and Lyft.
As these companies continue to hold on to their data, citing privacy concerns and sensitive trade secrets, city councils have been turning to proxy data sources and rider surveys to establish the causality between urban congestion and ride-hailing. This comes on the heels of a recent report which attributes a little over half the observed worsening traffic delays and increased vehicle miles traveled in San Francisco to ride-hailing companies (Uber and Lyft), with other factors such as population and employment growth, and changes in the roadway network contributing towards the other half of the increase.
Uber and Lyft’s actions in 2016 illustrates the friction between the ride-hailing industry and policymakers trying to regulate this new industry. In 2016, Uber and Lyft pulled out of Austin, Texas after the city voted to prevent these companies from self-regulating their drivers, but rather make them conform to stricter regulations passed by the city council. To fill the void caused by these companies exiting the Austin market, a non-for-profit company Ride Austin started operations the same year.
Ride Austin released a treasure trove of all trips hailed on the platform between June 2016 and April 2017 for policy research. This rich dataset with 1.5 million trips included information about trip start and end time and location, trip distance, fares and tips, vehicles, wait times, along with consistent driver and rider identifiers. This opened up a number of opportunities for planners and policy-makers alike to understand the synergistic/competitive forces in action between ride-hailing and other modes (transit in particular), temporal and spatial patterns of trips, and impact on the system performance. We dug deeper to answer three pressing questions –
Who uses ride-hailing services?
Our analysis shows that nearly 250,000 unique riders used Ride Austin services in the one year, but only 40,000 remained active at the time data were made available. These frequent riders made over 55% of all trips on the service. Surprisingly, a vast majority used the service fewer than five times, with many using it just one time (low retention rate). On average, riders paid $14 per trip ($1/min). While riders were willing to pay such steep fares, it is likely that fares impacted the retention rate among riders. It is also likely that at such a steep price point, the service was out of the reach of lower income households.
How do ride-hailing services complement/compete with transit?
We routed the vehicle trips through an open-source transit routing engine to see how many of the Ride Austin trips could be made on transit. More than 60% of the trips could have been provided by a single-seat (no transfers) transit service suggesting the competing nature of Ride Austin with transit. For 78% of these single-seat trips, the next bus would have arrived within 15 minutes of the ride-share starting times.
Moreover, the findings show that riders were willing to wait between 6–9 minutes (31% - 43% of total ride time) for the service. So, despite high fares and relatively long wait times, riders probably chose Ride Austin because of the on-demand, door-to-door, and personalized nature of the service.
How do ride-hailing services impact overall system performance?
Unsurprisingly, system usage was concentrated in the urban core where a combination of lower per-capita vehicle ownership and high parking costs make ride-hailing attractive. Also, a majority of trips were made during the evenings/late nights and over the weekend — probably to support safe driving. Crucially, findings indicate that for every mile spent on transporting riders, the service added at least 35–40% deadheading miles spent searching for the next customer. In cities challenged with congestion, such deadhead miles exacerbate a bad problem.
If you’d like to read more on this topic, you can find the full article published here or reach out to the authors of the research below.
Contributors:
- Anurag Komanduri(akomanduri@camsys.com)
- Zeina Wafa (zwafa@camsys.com)
