man on dockless scooter (FXQuadro/

Cities jump on scooter data for curbside insights

A new kind of data-driven public/private partnership is emerging as the number of dockless vehicles in cities grows. Both government entities and “urban mobility solution providers” -- think app-based rental scooters from Uber, Lime and Bird -- stand to benefit by sharing information such as vehicle use, curb regulations and traffic statistics.

Cities regulate curbs, parking and drop-off/pickup zones, so they need data to make sure mobility companies and their customers are in compliance. Cities can also use the data for planning and rezoning based on usage statistics. Companies, on the other hand, can improve their services by getting information from cities on construction zones and parking costs in a given area, for example, that they can pass to customers.

“The data that comes out of people using services, the aggregated data, is very valuable for cities" that use it for planning, prioritizing limited resources and improving existing infrastructure, whether that’s for cars or pedestrians or bike lanes or dockless vehicles, according to Michael Schnuerle, the city data officer for Louisville, Ky. The information, gathered by the city itself through 311 calls or data-sharing agreements with companies,  "helps us provide better services to our residents in general,”  he said.

And with micromobility fleets growing -- Berg Insight predicts almost 40 million vehicles in 2023 -- cities must devote some of their limited resources to their curbs, according to Stephen Goldsmith, project director of Data-Smart City Solutions at Harvard University. “We’re dealing with investments that the city makes: repair their sidewalks, make their streets work, have their curbs in good shape, and these are limited resources [that are] now valuable resources,” Goldsmith said. “Think about it. The curb has gone from a liability for a city to an asset, so how does a city manage this curb space?”

As with any budding relationship, there are thorns. A main one is that cities are committed to making data public, while companies must protect their customers’ personally identifiable information and their own proprietary information. Take, for instance, the feud between Uber and New York City, which wanted the company to share pickup and drop-off data. The ride-hailing giant cited privacy risks to riders.

Another issue is the lack of a standard protocol, Goldsmith said. Vendors and cities want data formats and exchanges that work in most places.

This was a point in an open letter that the Civic Analytics Network, of which Schnuerle is a member, posted through Harvard in December 2018. In it, CAN members recommend the use of the Mobility Data Specification (MDS), including the General Bikeshare Feed Specification (GBFS), for internal and external application programming interfaces.

The letter calls for real-time and historic data APIs, data on the location of vehicle distribution points, a monthly data file with origin/destinations and start and end times, and a web dashboard “where city leaders can login and see stats, graphs, and heat maps of historic and real-time usage by day, week, month, or year.”

“Because cities have been part of the conversation from the beginning and because mobility companies must provide the data to multiple cities out of the gate, then that has aligned us all on the GBFS data standards and the MDS data standards,” Schnuerle said.

Cloud-based data integration

Some cities are taking the matter into their own hands. Last year, Louisville signed deals with Bird and Lime in which both dockless scooter startups provide real-time and historic APIs.  Bird also gives the city a monthly data file and a web dashboard.

The real-time data can alert city officials to emergencies, while historic and aggregated data are important for planning and for ensuring that the companies are complying with regulations.

“It’s very interesting to see the origin and destination information for different trips at different times of day to determine where people are utilizing the scooters, where are they getting the most value in getting from one place to another,” Schnuerle said. "We’re using that information to plan what we traditionally call bike lane infrastructure, but we’re also using it to look at whatever we end up calling it -- slow lane or protected infrastructure."

In mid-January, the Austin, Texas, Transportation Department announced that it has published an MDS-based Dockless Vehicle Trips dataset that’s updated daily and represents more than 2 million such trips in the city. The department also built tools such as the Dockless Data Explorer, an interactive map that shows where trips start and end, and the Dockless Reporting Dashboard, which pulls out ley metrics from the dataset to provide monthly summaries. It also created the Austin Dockless API, which serves as a cloud-based geospatial interface to the dataset and powers the Explorer.

Washington, D.C., which has one of the largest dockless vehicle programs, announced in November 2018 that it was expanding micromobility options. As part of that, it partnered with Populus on the Populus Mobility Manager, a platform that integrates real-time data from the major operators, to help ensure equitable distribution of the vehicles. The vehicles are shown on a color-coded map so officials can quickly see where they’re concentrated.

“What we found is that the dockless program overall provides greater micromobility accessibility across the entire city, and also specifically in Ward 8, which is traditionally underserved,” according to Populus.

Coord is another cloud-based platform that can integrate data from multiple dockless vehicle companies. Borne of Alphabet’s Sidewalk Labs urban innovation arm, Coord created Surveyor, an app that uses augmented reality to collect curb asset data using an Apple iPhone.

Users log into the app and point their iPhone at fire hydrants, a parking sign, curb paint, bus stops and other relevant features, or assets, of the curb, and the app automatically computes their positions. The technology extracts characters from photos of signs to determine what they mean. For example, if a sign reads “School Zone,” “we extract the characters and determine what ‘school zone’ means in that city,” Coord CEO Stephen  Smyth said. “Then we spit out the answer to a driver through their app for their desired drop-off [location]… Using APIs on the backend, they get data about whether they can stop there.”

NACTO and the Open Transport Partnership launched SharedStreets in February 2018 as a transportation data standard and platform for public/private partnerships. It uses a simplified intersection-to-intersection referencing system for describing streets designed to be compatible with any public or private source of street data. The standard allows data to be ported from one map to another, say, from a traffic map to a parking map, because it enables geolocation specificity without using a proprietary map.

“What SharedStreets does is it allows you to match that information to a reference location so that you can move data back and forth with a shared reference along the street network,” said Mollie Pelon McArdle, co-director of the Open Transport Partnership and SharedStreets.

For cities, the first step to using this app is to run a map match so that they see data from a mobility company on their existing maps. Second, SharedStreets helps a city determine what metrics they want from company data. Because the platform is built on open source, cities can make changes as needed.

As dockless vehicle use grows -- in Portland alone, 472,069 trips had been taken on e-scooters as of Oct. 11, 2018, according to the Portland Bureau of Transportation -- cities need to band together to create policies and standards, Harvard’s Goldsmith said.

“A lot of work needs to be done, and it’s attainable and it’s critical, because as self-driving vehicles [grow], as more shared services [emerge], the curbs are going to become more valuable,” he said.

Editor's note: This article was changed Feb. 13 to clarify recommendations of the Mobility Data Specification. 

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