What 5 Million Scooter Rides in Austin Tell Us About the Future of Micromobility
by Erik Bauch, technical product lead & data scientist at Zoba. Erik holds a PhD in Physics from Harvard and has chosen scooters and bikes as his favorite modes of transportation. Follow him on Twitter @ErikBauch.
Zoba provides demand forecasting and optimization tools to shared mobility companies, from micromobility to car shares and beyond.
At Zoba, we provide demand forecasting and fleet optimization software as a service to micromobility companies working to improve their performance. In doing so, we tend to be focused on precise measurement and adjustment to capture demand in a given market. On some occasions, though, we like to zoom out and take a more macroscopic look at the micromobility industry.
The City of Austin has become a bit of a proving ground for micromobility companies over the last year. We dove into the micromobility data made available to the public by the city to see what there is to learn about the state of affairs in micromobility more generally.
The Austin dataset consists of 5 million scooter rides and 290,000 bike rides recorded between April 2018 and July 2019 (and counting). Ride attributes include trip distance, trip duration, trip start, and end time (rounded to the nearest quarter of an hour for anonymity). Until April 2019, the data also included start and end coordinates of trips (rounded to block-level precision).
Scooters in Austin
Austin, with an estimated population of 960,000, is not your typical scooter city. The city has eight licensed scooter operators, which provide more than 18,000 vehicles and cater close to 500,000 monthly rides at peak times. In comparison, San Francisco (estimated population of 880,000) had until recently limited the amount of scooter operators to exactly two, each with an allowed 625 devices — 1250 in total. Few cities come close to Austin’s supply levels.
Looking at monthly ride numbers, the impact of micromobility in Austin is striking: in the past 16 months, scooter rides have increased tenfold with ride numbers approaching 500,000 monthly trips in April and May 2019. This trend is modulated by individual events such as South by Southwest, the annual music-film-gaming-tech conference. This year in March, South by Southwest drew in more than 400,000 visitors and its impact on ridership is clearly visible. In addition to visitors, the overall ride numbers are impacted by the change of seasons, weather, supply levels, and other factors.
Scooter Economics
The viability of scooter sharing companies has caused quite a debate, with much of the information coming from opaque or dated sources. The open Austin data sheds light on the challenges of scooter sustainability.
Scooter Lifetime — The lifetime of scooter devices is currently one of the most contentious topics in micromobility. A Quartz analysis on micromobility in Louisville suggested that early scooter models generally did not last longer than a month, a claim that was heavily disputed by scooter operators. A recent LA Times article looked at Bird’s in-app data for Los Angeles and provides the most comprehensive analysis of scooter lifetimes to date. The analysis suggests that depending on the model of scooter, lifetimes vary between 2.5 and 5 months. However, the data are undisclosed and the reported values are therefore hard to verify.
The Austin dataset does not provide scooter lifetimes directly. We can, however, calculate the period during which a scooter was active in Austin by subtracting the earliest observed date a trip was recorded for the scooter from the latest. The chart below shows how long scooters remain active.
From 27,000 unique scooters deployed between April 2018 and January 2019, we find that the median active period of the typical Austin scooter was 53 days. We purposefully excluded all vehicles from the analysis that have been introduced after January 2019 as their active period is likely ongoing and would be artificially shortened otherwise.
The active period for Austin is longer than what has been reported for Louisville (average 29 days), but falls short of the lifetimes reported for Los Angeles (average 126 days). Moreover, we expect that this metric will change with time. Scooter providers including Bird and Lime are replacing earlier off-the-shelf scooter models with more rugged, durable devices at a remarkable speed. Better theft and damage management, as well as improved coordination with the city, will likely positively affect scooter lifetimes. For this reason, we extended our analysis and looked at the active period of scooters as a function of the month they were first deployed in Austin — and the results may come as a surprise.
Our analysis suggests that while scooters rolled out in April 2018 were generally active for less than two weeks, the active period of the average scooter in Austin has been rapidly increasing. Remarkably, the median active period of a scooter deployed in January of 2019 was at least 106 days — more than eight times longer than scooters deployed in April the year before.
Rides per scooter per day — One of the most important business metrics of a scooter other than lifetime is its average number of daily rides. Individual scooters with increased daily rides generate more revenue. Several temporal and spatial factors can influence the number of rides per day including day of week, time of day, large scale events, and bad weather.
Taking the active periods from all scooters, we count the total number of deployed vehicles at any given day and combine it with the daily rides between April 2018 and July 2019. From here we can calculate the average rides per scooter per day. Strikingly, while Austin has seen a steady increase in the number of active vehicles — more than 18,000 during South by Southwest — the rides per day per vehicle have plummeted.
In fact, while scooters in Austin collected between two and six trips a day in the earlier part of 2018, the average Austin scooter had just one trip per day in 2019. We note that while actual scooter utilization numbers may be slightly better — we can only estimate the amount of total active vehicles from the ride data — it’s clear that on a per vehicle basis, utilization decreased as supply increased. This is likely because supply outstripped demand, at least in areas that are currently serviced. Often, this effect is exacerbated by operators overservicing the same areas and under-servicing others. This calls to mind pictures of dozens of scooters piled on street corners.
Conclusion
Taken together, the Austin open mobility dataset sheds some light on the state of micromobility. It is clear from the data that adoption of new micromobility services has been rapid and that there is a large base of demand from both regular users and visitors. The data show that vehicle active periods have increased substantially over time, likely from the introduction of custom hardware. This bodes well for an industry so focused on unit economics.
It is also clear that demand is not infinite. While many cities have aggressively capped operators, Austin’s more generous permitting has led to supply levels that have depressed utilization. These rates — as low as one ride per vehicle per day — are often used as a sign of a great scooter bust. But there is reason to believe higher utilization rates are possible not just in lower supplied markets, but even in cities like Austin.
It is likely that lower utilization rates in Austin are in large part a byproduct of operators chasing overserviced, obvious demand hubs (e.g. the University of Texas). Operators often create a dangerous feedback loop of identifying the same high demand areas, massively oversupplying them, and then falsely equivalating the resultant utilization with true demand (more on that here). In doing so, they drive down their per vehicle performance while completely missing demand in underserved areas — something much harder to measure. We’ve seen first hand the power of shifting supply into such areas in our work. It’s not unreasonable to expect that utilization could be improved even with high supply levels were operators to invest in optimizing their fleets using data-centric methods.
In general, Austin tells a hopeful if developing story about the potential of micromobility. The signs are promising even on a very short term scale. For an industry still in its infancy, there are many reasons to be optimistic.
Zoba is developing the next generation of spatial analytics in Boston. If you are interested in spatial data, urban tech, or mobility, reach out at zoba.com/careers.
