For the first time, Skip is introducing tip-over detection technology for dockless mobility and a public dataset on scooter tip-overs.
In 2018, we began designing S3, Skip’s first electric fleet scooter. S3 uses unique sensors and onboard vehicle software to benefit both riders and cities. Earlier this month, we began public testing in Washington DC, and we’ve seen promising results so far.
Tip-overs are the most noticeable problem with dockless mobility. There are several contributing factors, such as careless parking, vandalism, and vehicle designs that fall easily due to a high center of gravity or weak kickstands. The result is at best unsightly, and at worst hazardous for sidewalk users, especially those with disabilities.
In Washington DC, Skip operates with a permit for 720 scooters. During an average 15-day period, we receive 6 community complaints about poor parking and riding, including tip-overs, or 0.0006 daily complaints per scooter. The rate is low because complaints take time and effort to submit, so we often only hear about the most egregious issues. Instead, community members and city staffers frequently find it faster to pick up scooters out of courtesy or frustration.
The onus should not be on community members. So we built tip-over detection to create automated alerts to respond proactively, instead of responding reactively to public complaints. In the process, we have started to analyze more comprehensive data to improve deployment operations, infrastructure investments, user education, and vehicle design.
We rely primarily on a 3-axis accelerometer to detect a scooter’s orientation, the same sensor used by smartphones to switch between portrait and landscape mode. When any S3 detects a tip-over, our system records the vehicle ID, GPS location, and timestamp. Over the last 15 days, we detected an average of 0.49 daily alerts per scooter, or 54 daily alerts from 111 scooters.
Tip-over detection also records when a scooter is placed upright. Of all tip-over alerts we received, 11% were placed upright within 5 minutes, 39% within 30 minutes, 53% within an hour, and 74% within 3 hours.
We are starting to analyze data by ward, neighborhood, intersection, time of day, and day of week. Patterns are already being used by our operations team to improve fleet parking and deployment and adjust staffing schedules and routes. The data is available for transportation planners, regulators, and city leaders to improve infrastructure, measure compliance, and make data-driven policy decisions.
We can also look at the correlation between high winds and tip-overs, a common question about dockless mobility. S3 was designed with a low center of gravity and a stronger kickstand to resist falling. Recent daily data suggests this design is working. High wind speeds, ranging from 6 to 15 mph, did not correlate with a higher frequency of scooter tip-overs.
Clear sidewalks and reducing the burden on communities are crucial to making scooters a fun and effective transportation option that everyone can support. Our next step is to reduce the number of tip-overs and improve our response time — we’ll share more as we learn. We’ve also publicly posted our dataset for anyone who would like to dig in further.
Tip-over detection is the first of many features enabled by S3’s new sensors and software. Technology developed at Skip is bringing a new level of data reporting, accountability, and safety to micromobility.
If you have ideas about improving scooter parking, data sharing and analysis, or if you would like to learn about joining our technical team, we’d love to hear from you!
