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Bikestream Litedeck

Bikestream Litedeck

The Bikestream Litedeck provides an overview of the Bikestream project. The Litedeck provides a short background to the project, a short discussion of the problems motivating the project, the opportunities that Bikestream is trying to capture and create, and the solutions the Bikestream project deems will help resolve or mitigate the problems, a short overview of the databike proof-of-concept produced under the project, and future directions for the project.

If you have any questions, comments or concerns, please send an email to ledgerback@gmail.com with the subject line: "@BikestreamLitedeck Feedback".

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August 17, 2020


  1. None
  2. Bikestream Litedeck Version 0.3 [2020-08-17]

  3. Contents - Introduction - Short Background - Problems - Opportunities

    - Solutions - Use-cases (PoC) - Future Directions
  4. Introduction - Intro slidedeck to Bikestream. Bikestream is a project

    at the intersection of mobility, micromobility, transportation, data economy, platform cooperativism and fitness/health tracking. - The goal of the Bikestream project is three-fold. The first goal is to develop proof-of-concept (PoCs) (which has been accomplished) electric-assisted bike (“e-bike”, “ebike”) that can store, manipulate, and transmit data from the e-bike (“databike”) to an external system/network where the cyclist of the e-bike has control over how their cycling data is shared, accessed and monetized. Primarily, this external system should be based on blockchain and other Web3 technology to provide the greatest guarantees of access control and security. The second goal is to educate cyclists and users of shared mobility services about how their data is stored, shared, and used by transportation network companies and third-party companies, and how they can create and share data for their own benefit and for transportation and scientific benefits. The third goal is to develop alternative and emerging models of governance, technologies and business models to incentivize cyclists to own and manage their data and empower autonomy and sovereignty over micromobility modals. - This is a collaborative research project so please feel free to add slides and make comments.
  5. Short Background: Part 1 • The Bikestream project focuses on

    the following areas: ◦ Micromobility ◦ Sustainability ◦ Health/Fitness ◦ Data governance ◦ Web3 technologies • The Bikestream project focuses specifically on micromobility (e-bikes) for a couple of reasons: ◦ Most people can connect the labor or effort involved in cycling with the generation of mobility data (i.e., if people have to put in physical effort, they can better understand that data generation is tied to their own acts) ◦ Micromobility is starting to grow tremendously in the past 5 years (and really mobility-as-a-service overall in the past 10 years) with new services just hitting cities and this is a new emerging area which is ripe for discussion on data governance and potential positive disruption for social good ◦ Most people are familiar with bicycles in the USA and generally can operate a bicycle ◦ E-bikes are also a growing market and their introduction can lead to new discussions on the future of transportation and re-orienting how we view urban planning ◦ The majority of micromobiltiy servies are MaaS and do not, as of yet, offer options for people to use their own electric-assisted devices ◦ The emergence of the platform cooperativism movement and its impact on bike delivery and other bike-based courier services.
  6. Short Background: Part 2 - Data Silos: A data silo

    is a database that is disconnected from other databases. The problem with data silos is that this data may be provide valuable insights to other parties if they could obtain access to the database. THe lack of access also prevents valuable use of this data, rather than simply leaving it in the database. - Fitness/Health Tracking apps and data: Fitness and health tracking apps such as Strava, Garmin, and MapMyRun collect copious amount os information from users of their apps and much of this data (unclear if true) is stores by these companies and shared with third-parties for advertising and other monetization purposes. Additionally, most of this information is not shared with local transportation agencies which could use this information for their transportation planning and other transportation-related issues. - Bikesharing/ Mobility-as-a-Service: MaaS, and in the area of bikesharing, has steadling grown since 2018. MaaS for bikesharing comes in two forms: (1) docked and (2) dockless. In docked mode, the bikes must be rented and returned to a central location owned or leased by the company. In dockless mode, the bikes can be rented and returned without needing to go to a central location.
  7. Short Background: Part 3 - Shared mobility: Shared mobility refers

    to the sharing of a means of transportation with another (e.g., bikesharing, ridesharing, etc.) - Civic Analytics: Civic analytics refers to data specification standards that are used to inform governmental agencies (often transportation agencies) about how MaaS providers are operating in a local jurisdiction. Two prominent data specifications are the General Bikesharing Feed System (GBFS) by the National Bikesharing Association (NBA) and the Mobility Data Specification (MDS) by the Open Mobility Foundation (OMF). - Mobility Data: Mobility data refers to any and all data related to mobility, including trip information, geolocation, and sensor data gained from mobility devices. - Data Privacy/Stewardship: Data privacy refers to how entities take measures to ensure the privacy/confidentiality of user data. Data Stewardship refers to how companies preserve, share and use user data with the user’s interests in mind.
  8. Problems: Part 1 - There are multiple issues that are

    arising in the micromobility industry and at its intersection with transportation and technology overall. - Some of the problems we sought to address were: - Transportation network companies (TNCs) sharing user-generated data with third-parties that users may or may not be aware of (e.g., TNCs may share user-generated data with law enforcement and transportation agencies and the extent or necessity of the amount of data is always in question).1 - TNCs prohibiting disclosure (or lack of transparency) of their algorithms (development, usage, etc.) and software to users of their platforms (i.e., use of closed-source software) - TNCs using user-generated data to train machine learning models for proprietary use and monetary gain (e.g., sharing data with advertisers) at the expense of users - Fitness and health tracking apps sharing user-generated data with third parties that users may or may not be aware of and data privacy concerns such as securing of user data and preventing data breaches ( (e.g., Tracking apps may share user-generated data with law enforcement and transportation agencies and the extent or necessity of the amount of data is always in question).3 - New mobility data specifications are targeted towards TNCs (and rightfully so), but there should be a need to develop a specification specifically for users to offer this data to their local transportation agencies themselves - The high cost of pre-assembled e-bikes (“... e-bikes sell for more than four times the price of traditional bicycles…”) make them not the most viable option for most middle to low income people to purchase when considering a bicycle as a means of transportation. - Many people have a bicycle that is not be used to its full potential (i.e., wasting resources) often because of their urban living space (E.g., suburbs with little to no bike lanes, cities with mass sprawl, tough road conditions) - Lack of PoCs concerning blockchain and mobility - Lack of education among users about how their data is used and concerning MaaS companies
  9. Problems: Part 2 - Lack of education or opportunities for

    cyclists to contribute to transportation research - Lack of education among professional bicycle operators about cooperative models - Issues with modifying pre-assembled e-bikes can make it tough for users to find maintenance or services or change parts on their own (i.e., removal of right to improve) - Lack of uniformity on regulations (local and beyond) on micromobility modals. - High rate of pedestrian and cyclists deaths in the USA.2 - Control of geolocation mapping by very few companies which are often not known for good data privacy practices (e.g., Google) - TNCs (primarily Uber and Lyft) tend not to play well with public transportation and other TNCs, which often lead to a lack of transparency around payments, mobility options, and inhibition of user choice.
  10. Opportunities - There are many opportunities here for creating a

    grassroots-based project and movement around micromobility that can excel at the local level and be borderless. - Some of the opportunities that can be captured are: - The e-bike market is expected to “reach almost 24 billion U.S. dollars in 2025.” - E-bikes are still relatively unknown in the US market. - E-bikes are the largest growing sector of bicycle sales and electric vehicle sales.3, 4 - E-bikes can claim the 10-15 mile treks which are the majority of motor vehicle trips in the USA (i.e., unbundling of rides with motor vehicles).1 - Motor vehicle sales and ridesharing services are slowing down.2 - Citizen science projects have shown veracity in the past and can be used here for cyclists to improve their own cycling conditions and behavior with cyclist-owned or -operated models - Growing movement and backlash against major geolocation mapping providers (E.g., Google), sharing economy and major TNCs (e.g., Uber and Lyft) - Rides on shared micromobility services are rising fast.2 - Autonomous motor vehicles are not coming anytime soon, but this could change for autonomous databikes.1 - Low-end hardware can be used to create, e-bikes, databikes and autonomous databikes - Urban transportation leaders are moving towards open data approaches.5
  11. Solutions - To capture the opportunities and mitigate or solve

    the problems mentioned earlier, we have thought of the following non-exhaustive solutions: - Create models for cyclists to develop their own databikes: - Creating conversion models for converting traditional bicycles to e-bikes and from e-bikes to databikes by attaching on-frame computers - Creating, using or modifying existing open hardware and design models to develop e-bikes and databikes - Creating a marketplace for all kinds of e-bike-related data (cyclist behavior, geolocation, vibration, heart rate, fitness, etc.) that is secured with blockchain technology to prevent data privacy issues and secure payment channels - Creating a public-common partnership (PCP) where five major stakeholders work together on micromobility solutions: - Local Government and transportation agencies - Mobility Cyclist Association - Ledgerback-DCRC and other research/technical/advocacy organizations - Bicycle retailers - Transportation and Logistics Industry organizations - Developing business cases which empower individual users to have user autonomy and users as a collective to govern their data (storage, sharing, monetization, etc.) - Developing a citizen science network of databike users for vibration data, health data, and all other kinds of data, and for aiding in research projects
  12. Proof-of-Concept: Databike Zeta 001 (DBZ-001) - DBZ-001 is our first

    PoC in the Bikestream project. This concept we wanted to prove with the DBZ-001 is that we can develop a databike for real-time streaming of geolocation and internal electrical component information where the cyclist (or operator) has control over how and when their mobility data (aka consumer mobility data) is shared with unknown third parties, and creating the potential for a financial incentive through data sharing with unknown parties. - The primary use-case the PoC addressed is the development of a financial incentive for cyclists to 1) convert their bikes into e-bikes, and 2) share their data with unknown third parties. - DBZ-001 presents a general model for converting a traditional bicycle to a databike. Additionally, the development of a data stream and data marketplace on Streamr, a data sharing service on the Ethereum blockchain. - The DBZ-001 has two on-frame single-board computers (SBCs), a Raspberry Pi 3 B+ and a Cycle Analyst 3 (CA3). - The DBZ-001 collects geolocation (from a connection to a smartphone via bluetooth; info sent as NMEA strings) and internal electrical information (from the CA3) (“zeta-info”) - The zeta-info is stored and processed on the RPi 3+ via a Python program into a JSON format and is outputted to Streamr’s command line interface (CLI) that connects the Python program to the publishing feature of Streamr and connects the JSON-formatted zeta-info to the data stream created on Streamr, which can be accessed in the Streamr data marketplace - To make the data publicly available, we used Streamr Core, Streamr Marketplace and Streamr CLI. We used Streamr Core to create a data stream for the zeta-info, the Streamr marketplace to create a data product (comprised of multiple data streams of zeta-info that can be obtained from our DBZ-001 and from anyone who chooses to join the Cyclist Association product by adding their data stream) that is publicly accessible, and the Streamr CLI to connect the Python program to the data stream on Streamr so that we could run the RPi 3+ in headless mode (without a keyboard or display) and achieve real time data streaming.
  13. Proof-of-Concept: Databike Zeta 001 (DBZ-001)

  14. Future Directions - Develop more PoCs that take the project

    to the next level such as an autonomous databike. - Start developing relationships with entities on our contact list in theses areas (mobility, sustainability, databikes, blockchain, etc.) - Further ideating and developing business cases for Bikestream - Finishing documentation on Bikestream and the DBZ-001 PoC - Seeking grant and investment opportunities - Recruiting and adding new members to the project - Further ideating and developing citizen science cases for Bikestream