Data is the new oil, even for e-scooters

Building on some of the ideas mentioned in our last article, we’d like to continue the discussion around open data and the machine economy as by-products of the internet of things. Helbiz is in a unique position as we are directly working on mobility services (with embedded IoT) and blockchain applications. Because of this, we feel we have a responsibility to adhere to the ideologies surrounding web3 as we build the services for getting around the cities of tomorrow.

Data is the new oil

The amount of data being produced and collected has grown exponentially as more and more devices have been connected by the internet. Application usage, e-commerce transactions, and user preferences are just some of the ways human behavior is being stored on databases. The UN estimated that the numbers of internet users more than doubled from 2000–2015. And that kind of usage creates an unfathomable amount of data, and as corporations came to the realization, personal information became an enormously valuable asset.

The amount of data being produced is set to explode once we have over 31 billion IoT devices deployed by 2020. These devices will communicate over a 5G network, enabling faster connections and greater bandwidth. This is set to create a machine economy where devices transfer amongst themselves storage, electricity, data, and even payments. This proliferation of communication between devices will generate more data than we know what to do with, and it’s where we can begin to explore opportunities that transcend today’s closed business models. We can’t fathom what kind of data will be generated, how it will be enriched, or what kinds of conclusions will be drawn by the machine learning algorithms analyzing it.

Data is the new oil in an economy driven by software.

Trading data between organizations and machines will be a mutually beneficial exercise that creates entirely new business models, making use of data that would otherwise go unused. That being said, we need to rethink how we collect, store, and expose data — end users should have complete control of their data and have the security of encryption.

How can we guarantee the integrity of data as it is being consumed by many systems? How can we protect the identity of users? What’s the most efficient way to route payments between data consumers and producers, without totally compromising decentralization? How should data be exposed between providers without allowing bad actors to get their hands on it? How do we scale systems to support all the data being consumed in real-time, from hundreds or perhaps thousands of sources? These are just some of the obstacles preventing the realization of Big Data.

Micromobility data policies

In the context of urban mobility and e-scooters, the data being produced can be extremely valuable to cities looking to understand how their citizens get around. In fact, cities are creating detailed data requirements for mobility providers and are using it as input for more organized future transportation plans. When reading some of the micromobility data sharing policies from cities such as Nashville, Santa Monica, Dallas, and Houston, two interesting policy features stand out

• A universal agreement on the need for trip and fleet availability data
• A variety of approaches toward customer feedback and other data

If many cities are asking for the same sets of data from all mobility providers, the standardization of said data is imperative. There is an initiative to define such standards in the context of blockchain technologies — the Mobility Open Blockchain Initiative (MOBI). It plans to connect global mobility providers with blockchain innovators to collaborate on the development of services such as:

• Vehicle identity, history and data tracking
• Autonomous machine and vehicle payments, and data markets
• Usage-based mobility pricing, and payments for vehicles, insurance, energy, congestion, pollution, and infrastructure.

In regards to mobility providers, there is already an effort to create a data standard and API specification by the city of Los Angeles — the Mobility Data Specification (MDS).

The specification is a way to implement realtime data sharing, measurement and regulation for municipalities and mobility as a service providers. It is meant to ensure that governments have the ability to enforce, evaluate and manage providers.

Although it brings convenience for cities to process the data they receive from many providers, we can take advantage of a shared format by pooling everyone’s data as part of an open data market.

E-scooter trips in an open market

What exactly is an open data market? Simply put, it’s a secure and mutually beneficial exchange of data between entities — and it’s an inevitable result of the IoT revolution. It’s also a paradigm shift, forcing businesses to re-think their competitive strategies within an open ecosystem.

The most obvious information collected from mobility providers would be around trips. Taken from the MDS:

A trip represents a journey taken by a mobility as a service customer with a geo-tagged start and stop point.

This trip data would then enable cities to make informed observations about geographical areas or boroughs that most support micromobility options, the streets most in need of a multimodal re-design, and how these new options interact with the rest of the transportation system. But more importantly, any machine learning algorithm that processes this information can rely on a much larger dataset when everyone follows the same standard.

In an urban environment equipped with sensors across traffic lights and city landmarks, it’s not hard to imagine the data points vehicle_type, trip_duration , and route being used for other algorithms. For example, trip data from several providers would provide a clearer picture of the congestion situation around busy streets, enabling smart traffic control options to be deployed. As stated in a new report from Deloitte:

Fine-grained traffic flow data created by sensors in infrastructure and vehicles allow intelligent systems to optimize traffic flow by adjusting traffic lights and other signals.

The report goes a step further to imagine how the in-transit experience can be molded into something else, whether it be more entertaining, productive, or relaxing.

For example, if a scooter is in the vicinity of a Starbucks sensor and

1. the user has opted in to sharing their GPS location in real-time,
2. the route data point of the scooter trip is processed and a coffee shop has not already been advertised to the rider,
3. two nearby speed sensors indicate the scooter is coming to a stop,
4. a temperature sensor notifies of warm weather,
5. and the user has shared a history of enjoying coffee drinks…
6. … then Starbucks can display a promotion for a cold brew

This simple example is meant to demonstrate how data produced in real-time can be used in junction to offer experiences and services impossible before technologies such as 5G and blockchain. As ridiculous or far out there as it sounds, location-based advertising is already being deployed. However, it will take on new forms as more and more machines are able to transmit real-time data about the devices and humans interacting with them.

Helbiz