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Data Sharing Guidance for Public Transit Agencies—Now and in the Future (2020)

Chapter: Bibliography and Other Resources

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Suggested Citation:"Bibliography and Other Resources." National Academies of Sciences, Engineering, and Medicine. 2020. Data Sharing Guidance for Public Transit Agencies—Now and in the Future. Washington, DC: The National Academies Press. doi: 10.17226/25696.
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Page 58
Page 59
Suggested Citation:"Bibliography and Other Resources." National Academies of Sciences, Engineering, and Medicine. 2020. Data Sharing Guidance for Public Transit Agencies—Now and in the Future. Washington, DC: The National Academies Press. doi: 10.17226/25696.
×
Page 59
Page 60
Suggested Citation:"Bibliography and Other Resources." National Academies of Sciences, Engineering, and Medicine. 2020. Data Sharing Guidance for Public Transit Agencies—Now and in the Future. Washington, DC: The National Academies Press. doi: 10.17226/25696.
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Page 60

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Bibliography and Other Resources Abella, A., M. Ortiz-de-Urbina-Criado, and C. De-Pablos-Heredero. (2017). A model for the analysis of data- driven innovation and value generation in smart cities’ ecosystems. Cities, 64, 47–53. American Bus Benchmarking Group. (2019). https://americanbusbenchmarking.org/?page_id=11. Antrim, A., and S.J. Barbeau. (2013). The many uses of GTFS data–opening the door to transit and multimodal applications. Location-Aware Information Systems Laboratory, University of South Florida, Tampa. Barbeau, S.J. (2018A). Quality Control-Lessons Learned from the Deployment and Evaluation of GTFS- Realtime Feeds. No. 18-05585. Barbeau, S.J. (2018B). Closing the Loop: Improving Transit through Crowdsourced Information. Transportation Research Record: Journal of the Transportation Research Board, No. 2672, pp. 224–234. Barry, J., R. Newhouser, A. Rahbee, and S. Sayeda. (2002). Origin and Destination Estimation in New York City with Automated Fare System Data. Transportation Research Record: Journal of the Transportation Research Board, No. 1817, pp. 183–187. Bosselman, A. (2019). Uber Partners with Denver Transit—What Could Go Wrong? Retrieved from https:// denver.streetsblog.org/2019/01/31/use-uber-for-denver-transit-company-is-our-overlord-and-savior-as- it-crushes-the-epic-failure-that-is-rtds-mobile-app/. Brakewood, C., N. Ghahramani, J. Peters, E. Kwak, and J. Sion. (2017). Real-Time Riders: A First Look at User Interaction Data from the Back End of a Transit and Shared Mobility Smartphone App. Transportation Research Record: Journal of the Transportation Research Board, No. 2658, pp. 56–63. http://dx.doi.org/ 10.3141/2658-07. Brakewood, C., and R. Paaswell. (2017). Assessing NJ Transit’s Mobile App for User’s Receptiveness to Geotargetting. UTRC/RF Grant No: 49198-43-27. University Transportation Research Center—Region 2, The City College of New York, NY. Brauneis, R., and E. Goodman. (2017). Algorithmic Transparency for the Smart City. (SSRN Scholarly Paper No. ID 3012499). Retrieved from: https://papers.ssrn.com/abstract=3012499. California Public Utilities Commission. Decision Adopting the Renewable Auction Mechanism. D. 10-12-048. Filed 21 August 2008. Cambridge Systematics, Inc. (2018). NCHRP Research Report 868: Cell Phone Location Data for Travel Behavior Analysis. Transportation Research Board, Washington, DC. https://doi.org/10.17226/25189. Catalá, M. (April 2016). FTA Open Data Policy Guidelines. FTA Report No. 0095. U.S. Department of Trans- portation, Washington, DC. Chen, R., B. Fung, B.C. Desai, and N.M. Sossou. (2012). Differentially private transit data publication: a case study on the Montreal transportation system. In Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, Beijing, China, pp. 213–221. Cheshire, T. (2017). TfL plans to make £322m by collecting data from passengers’ mobiles via Tube Wi-Fi. Sky News. https://news.sky.com/story/tfl-may-make-322m-by-selling-on-data-from-passengers-mobiles-via- tube-wifi-11056118. Chester, M., and A. Horvath. (2010). Life-cycle assessment of high-speed rail: the case of California. Environmental Research Letters, Vol. 5, No. 1: 014003. Colorado Public Utilities Commission. (2015). Recommended Decision of Administrative Law Judge G. Harris Adams Amending Rules. Proceeding No. 14R-0394EG. In the Matter of the Proposed Rules Relating to Data Access and Privacy for Electric Utilities, 4 Code of Colorado Regulations 723-3 and Data Access and Privacy Rules for Gas Utilities, 4 Code of Colorado Regulations 723-4. Conger, K. (Aug. 7, 2019). Uber Wants to Sell You Train Tickets. And Be Your Bus Service, Too. The New York Times. 58

Bibliography and Other Resources   59   Conradie, P., and S. Choenni. (2014). On the barriers for local government releasing open data. Government Information Quarterly, 31(1), S10–S17. Corazza, M.V., U. Guida, A. Musso, E. Petracci, M. Tozzi, D. Vasari, and E. de Verdalle. (2018). A Predictive Maintenance System for Bus Fleets: Innovation and Research from the Case Study of Ravenna. Presented at 97th Annual Meeting of the Transportation Research Board, Washington, DC. No. 18-00906. Crunchbase. (2019). Location Based Services Companies. https://www.crunchbase.com/hub/location-based- services-companies#section-investments. Last retrieved July 15, 2019. Dawes, S.S. (2010). Stewardship and Usefulness: Policy Principles for Information-Based Transparency. Govern- ment Information Quarterly, 27(4), pp. 377–383. https://doi.org/10.1016/j.giq.2010.07.001. Deloitte LLP. (2017). Assessing the Value of TfL’s open data and digital partnerships. http://content.tfl.gov.uk/ deloitte-report-tfl-open-data.pdf. Enwemeka, Z. (2016). The MBTA Says This Is the Best Transit App for Greater Boston Riders. WBUR Boston. https://www.wbur.org/bostonomix/2016/09/06/mbta-best-transit-app. Erhardt, G.D. (2016). How smart is your smart card? Evaluating transit smart card data with privacy restrictions and limited penetration rates. Transportation Research Record: Journal of the Transportation Research Board, No. 2544, pp. 81–89. Flanigan, K. (2019). Video, Phone Data Led Cops to Missing Boston Woman. necn.com. https://www.necn.com/ news/new-england/Olivia-Ambrose-Kidnapping-Suspect-Victor-Pena-Court-Documents-504750322.html. Green Button Alliance. DataGuard Energy Data Privacy Program. 8 Aug 2018. Webinar. Gordon, J., H.N. Koutsopoulos, N.H.M. Wilson, J.P. Attanucci. (2013). Automated Inference of Linked Transit Journeys in London Using Fare-Transaction and Vehicle Location Data. 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60   Data Sharing Guidance for Public Transit Agencies—Now and in the Future National Academies of Sciences, Engineering, and Medicine. (2018). Data Matters: Ethics, Data, and Interna- tional Research Collaboration in a Changing World: Proceedings of a Workshop. The National Academies Press, Washington, DC. doi: 10.17226/25214. National Association of City Transportation Officials (NACTO). (2018). Ford Motor Co., Uber, and Lyft Announce Agreement to Share Data Through New Platform That Gives Cities and Mobility Companies New Tools to Manage Congestion, Cut Greenhouse Gases, and Reduce Crashes. https://nacto.org/2018/ 09/26/ford-uber-lyft-share-data-through-sharedstreets-platform/. National Conference of State Legislatures. Data Security Laws. http://www.ncsl.org/research/telecommunications- and-information-technology/data-security-laws-state-government.aspx. Accessed June 14, 2019. Peralta Quiros, T. (2018). Data Analytics for Transport Planning: Five Lessons from the Field. The World Bank Group Transport for Development Blog. http://blogs.worldbank.org/transport/data-analytics-transport- planning-five-lessons-field. Rosado, W. Data Detour: Analytics Will Move Transportation Forward. (2014). Wired. https://www.wired.com/ insights/2014/07/data-detour-analytics-will-move-transportation-forward/. Salzberg, A. (2018). A new way of partnering with cities. Uber Newsroom. https://www.uber.com/newsroom/ cities-as-partners/. Sánchez-Martínez, G.E., and Munizaga, M. (2016). Workshop 5 report: Harnessing big data. Research in Transportation Economics, 59, pp. 236–241. Schweiger, C.L. (2015). TCRP Synthesis 115: Open Data: Challenges and Opportunities for Transit Agencies. Transportation Research Board, Washington, DC. Schweiterman, J.P., M. Livingston, and S. Van Der Slot. (2018). Partners in Transit: A Review of Partnerships between Transportation Network Companies and Public Agencies in the United States. Chaddick Institute for Metropolitan Development at DePaul University. Policy Series, Chicago, IL. SEE Action 2012. State & Local Energy Efficiency Action Network. A Regulator’s Privacy Guide to Third-Party Data Access for Energy Efficiency, December. Shared-Use Mobility Center. (2019). Objective-Driven Data Sharing for Transit Agencies in Mobility Partnerships. White Paper. SharedStreets. https://sharedstreets.io/. Shelton, T., A. Wiig, and M. Zook. (2014). The “Actually Existing Smart City.” Cambridge Journal of Regions Economy and Society, 8(1), 13–25. http://dx.doi.org/10.1093/cjres/rsu026. Teale, C. (2019) 1 Year in: Uber’s ‘rocket shot’ Cincinnati Partnership. Smart Cities Dive. February 11, 2019. https://www.smartcitiesdive.com/news/uber-cincinnati-partnership-one-year-later/547964/. Thomas, L.W. (2017). TCRP Legal Research Digest, 48: Legal Issues Concerning Transit Agency Use of Electronic Customer Data. Transportation Research Board, Washington, DC. Thomas, L.W. (2018). TCRP Legal Research Digest 52: Legal Implications of Video Surveillance on Transit Systems. Transportation Research Board, Washington, DC. TransitCenter (2018). The Data Transit Riders Want: A Shared Agenda for Public Agencies and Transit Application Developers. New York, NY. Transport for London. (n.d.). Event data from mobile devices. https://tfl.gov.uk/corporate/privacy-and-cookies/ event-data-from-mobile-devices. Accessed March 8, 2019. Transport for London. (2017). Review of the TfL WiFi pilot: Our findings. http://content.tfl.gov.uk/review-tfl- wifi-pilot.pdf. Transport for London. (2018). Annual Advertising Report. http://content.tfl.gov.uk/tfl-advertising- report-1718.pdf. U.S. Department of Energy. (2015). United States Department of Energy (US DOE) data privacy and the smart grid: a voluntary code of conduct (VCC). Washington, DC. Wang, Y. (2014). A Study of Open Payment Fare Systems: System Design, Fare Engine Algorithm and GTFS Extension. MS thesis. Massachusetts Institute of Technology, Cambridge. Windmiller, S., T. Hennessy, and K. Watkins. (2014). Accessibility of Communication Technology and the Rider Experience: Case Study of Saint Louis, Missouri, Metro. Transportation Research Record: Journal of the Transportation Research Board, No. 2415, pp. 118–126. World Bank. (2016). The World Bank Launches New Open Transport Partnership to Improve Transportation through Open Data. World Bank Group. http://www.worldbank.org/en/news/press-release/2016/12/19/ the-world-bank-launches-new-open-transport-partnership-to-improve-transportation-through-open-data. Zaslavsky, A., C. Perera, and D. Georgakopoulos. (2013). Sensing as a Service and Big Data. ArXiv, Cornell University, Ithaca, NY. http://doi.org/arXiv:1301.0159.

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 Data Sharing Guidance for Public Transit Agencies—Now and in the Future
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Transit agencies are beginning to harness the value of external data, but challenges remain.

The TRB Transit Cooperative Research Program's TCRP Research Report 213: Data Sharing Guidance for Public Transit Agencies – Now and in the Future is designed to help agencies make decisions about sharing their data, including how to evaluate benefits, costs, and risks.

Many transit agencies have realized benefits from sharing their internal data sets, ranging from improved customer information, to innovative research findings that help the transit agency improve performance.

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