Using Location Data for Guiding Micromobility Outcomes

Morgan Herlocker
Mar 26 · 4 min read

Scooter and bike share services present cities an opportunity to move more people in less space while providing more equitable transportation options. Most cities expect private mobility companies to further policy goals around safety, equity, and sustainability in return for their use of public infrastructure. Location data — information about where things are in the world, such as phones or scooters — is ubiquitous today, and can help monitor how micromobility services interact with street infrastructure. This post will explore the capabilities of this data, as well as the hardware limitations.

Location Accuracy

For example, a phone with a good signal will commonly report a lower bound of 10 meters horizontal accuracy with a 67% confidence interval. This means that for a device continuously reporting its location while sitting still, about 2/3 of the reports will be less than 10 meters away, while the remaining 1/3 of reports will be farther away.

Micromobility Enforcement

Instead of probabilistically determining whether a particular scooter violated a rule, cities should use location data in aggregate to understand how people interact with infrastructure, and use signage and other forms of enforcement to guide behavior. If geofencing is a hard requirement, applying noise filters to location data and calculating acceptable false positive statistical thresholds is needed.

Update Frequency

The impact of this effect is that location data can only be used to estimate the speed of a vehicle to around 7 miles per hour of precision. Considering scooters only travel at a max speed of 20 miles per hour, this error range makes it nearly impossible to reliably enforce speed restrictions using location data alone. Instead, cities should measure aggregate speeds through safety corridors, which can be measured with a much higher level of accuracy. If prevailing speeds are higher than safety allows, cities can work with companies on hardware modifications for speed caps, post additional signage, or station a physical presence in the area of concern.

High Level Patterns

At SharedStreets, we are building open source tools and specifications to help make understanding micromobility data easier. We are writing a specification for micromobility aggregate metrics which extends the City of Los Angeles’ Mobility Data Specification (MDS), as well as a reference implementation of the processing infrastructure to generate these metrics called the SharedStreets Micromobility Connector.

SharedStreets

SharedStreets

Morgan Herlocker

Written by

building street communication protocols @sharedstreets

SharedStreets

SharedStreets