Data Sharing

Unlike other forms of transportation, E-scooters provide cities with an opportunity to collect particularized data and learn more about how their residents and visitors travel. Ultimately, data can act as a solution to many problems that have been identified by cities, as well as a tool to accelerate the progress made in their goals to provide safer, more efficient modes of transportation. As E-scooters have emerged on the scene, a host of cities have begun issuing permits to E-scooter companies that are contingent on the companies sharing data collected within the city.[1] Regardless of whether E-scooters are currently present within the city or will be in the future, most cities within the U.S. have seen it as a wise step to require compliance with data sharing requirements.

What has been learned?

Data collected from trips taken on E-scooters is capable of revealing a lot of valuable information. Such information may show the number of trips that have been taken, the average mileage per trip, the average time span per trip, the most prevalent hours of usage generally, the areas within a city with the highest E-scooter traffic, etc. The table below is an illustration of just a few cities that have collected this sort of data:

The type of data collected, however, is not limited to the things listed in the above chart. In fact, cities can compile a seemingly endless array of information and tailor the type of information collected to their own interests and goals. Some cities, like Portland, have required companies to place a specific amount of E-scooters within a underserved area of the city and provide information as to how those communities are using them. For instance, the average trip distance in Portland was 1.15 miles.[6] However, the average trip by those living in East Portland—a historically underserved, African-American community within Portland—was roughly 1.6 miles.[7] Thus, this data has shown Portland that certain areas of their population are actually relying on cheap modes of transportation, like E-scooters, for longer distance travel.

Using the Data

The data collected from E-scooter companies is incredibly important for cities to learn more about their transportation needs. Unlike other forms of transportation—including private vehicles, public transportation, and ride-sharing platforms like Uber—E-scooters provide cities with the ability to learn their transportation strengths and weaknesses. Some valuable insights from E-scooter travel may include:

1. A better idea of where roadway and sidewalk improvements should be made;
2. The impact that electric modes of transportation may have on pollution;
3. Possible reduction in roadway congestion;
4. Travel patterns and preferences of consumers;
5. Solutions to first- and last-mile problems;
6. Receptiveness of population to alternate modes of transportation generally; and
7. City specific needs relating to fleet size and usage.
8. Overall compliance with traffic laws, etc.[8]

Legal Language

Cities have taken different approaches as to how they regulate E-scooters and the legal vehicle used to enforce those regulations. This may include passing a city ordinance, entering into a memorandum of understanding with providers, issuing permits with strict guidelines, or issuing request for proposals. Regardless, it is important for cities to consider specific language that will be used to provide clear guidance to E-scooter companies.

Potential Language:

(1) In General: As a condition for operation, a PROVIDER must provide to the City:

1. 1. a monthly report that includes Applicable Data for all E-scooters deployed by the PROVIDER in the City; and
   2. access to the near real-time location of each E-scooter within the City.

(3) Anonymized Date:

1. 1. A PROVIDER may anonymize trip data provided to the City each month if it fully describes the method used for anonymization.

(2) Definitions:

1. 1. Applicable Data – includes the following:
      1. total mileage traveled on all E-scooters supplied within the city;
      2. total trips taken by each scooter provided within the city;
      3. average distance traveled per trip;
      4. A usage factor for each hour of each day that is to be calculated by dividing the total number of in-use minutes in that hour for all E-scooters by the total number of operable minutes in that hour for all E-scooters;
      5. amount of active and nonactive E-scooters for a given day within the calendar month;
      6. the mean battery life of all E-scooters within the City for a given day;
      7. the maximum and mean speeds, in miles per hour, of trips taken on the E-scooters within the calendar month;
      8. a list of customer and non-customer complaints regarding the usage, parking, functionality, and enforcement of E-scooters; and
      9. a list of all crashes reported by either a user or non-user of an E-scooter, including any information provided to the PROVIDER regarding the circumstances of the crash.
   2. Crash(es) — all incidents where an E-scooter is involved in a collision with another E-scooter, a  car, a pedestrian, an inanimate object (such as trees, buildings, polls, etc.), or where a user of an E-scooter falls off of the E-scooter during use. A crash includes the instances listed above regardless of whether injury occurs to an individual or property.

* This language is modified from Atlanta, Ga., Ordinance 18-O-1322,  § 150-406 (last updated Jan. 1, 2019); Providence, R.I., Ordinance § 23-24 (Aug. 18, 2018). For further clarity regarding terminology, see “About Us & FAQs.”

Sources:

[1] Regina Clewlow, The Opportunity to Reshape Cities with Shared Mobility Data, Forbes (Oct. 10, 2018), https://www.forbes.com/sites/reginaclewlow/2018/10/10/the-opportunity-to-reshape-cities-with-shared-mobility-data/#43761883617f; see also, e.g., Portland Bureau of Transp., 2018 E-Scooter Findings Report 6 (2018) (stating that data collection was a requirement on all companies who received a permit from Portland); Providence, R.I., Code § 23-24(3)(d)(vi) (2018) (“Applicant must agree to share all data with the City at no cost in order to be eligible for authorization.”).

[2] Portland Bureau of Transp., supra note 1. These statistics pertain specifically to E-scooters and the data was collected from three companies participating in the pilot program.

[3] Charlotte Dep’t of Transp., Ridership Data: Charlotte’s Shared Mobility Pilot Program (2017–18), https://charlottenc.gov/Transportation/Programs/Documents/Factsheet-SharedMobility.pdf. Note, some of these numbers include bike share services. A star indicates that the statistic relates specifically to E-scooters.

[4] Dockless Mobility Data, austintexas.gov, http://austintexas.gov/DocklessMobility (last updated Mar. 5, 2019). Austin, Texas, has established probably the most comprehensive data sharing platform. A real-time reporting dashboard can be viewed to track dockless mobility usage. Note, these statistics apply to dockless mobility generally. However, a significant portion of the statistics gathered are attributable to E-scooters specifically.

[5] Mayor Muriel Bowser, District of Columbia, Dockless Vehicle Sharing Demonstration: Evaluation 24–25 (2018), https://ddot.dc.gov/sites/default/files/dc/sites/ddot/publication/attachments/Dockless%20Demonstration%20Evaluation%2012-17-18_FINAL.pdf. Note, these numbers pertain to dockless mobility generally, including E-scooters and bike share platforms. A star indicates that the statistic relates specifically to E-scooters.

[6] Portland Bureau of Transp., supra note 1.

[7] Id.

[8] See generally Aarian Marshall, Still Smarting from Uber, Cities Wise Up About Scooter Data, Wired (Sep. 18, 2018), https://www.wired.com/story/cities-scooter-data-remix-uber-lyft/.