1. Policy In Motion: Route360 Bryce Adams, Elizabeth Joseph, Julie Lindsey, Charles E. Maddox, Lauren Waters 1

2. Agenda The Challenge The Solution: Route360 How Does It Work? Will It Work? How Do We Get There? Conclusion Questions and Answers 2

3. The Challenge Citizens Cannot compare transportation alternatives using a unified platform City Cannot analyze citizens’ transportation preferences and needs 3

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14. The Challenge Citizens Cannot compare transportation alternatives using a unified platform City Cannot analyze citizens’ transportation preferences and needs 14

15. The Solution: Route360 Citizens City 15

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17. How Does Route360 Work? Pulls information from transportation vendors Compiles and provides data on: Trip time Total cost Environmental impact Real-time arrival information Parking availability Special event road closures Collects data on user preferences 17

18. Research Support 18

19. The Benefits of Route360 Individuals in Austin Improved experiences with public transportation Greater decision making autonomy The Capital Metro Transit Authority Increased ridership Improved public perceptions The City of Austin Improved future planning Efficient data collection 19

20. “Route360: How Austin Gets Around” How Do We Get There? 20

21. How Do We Get There? Expenses Tiered implementation Phase I: $113,000 Phase II: $68,000 Phase III: $58,000 Projected Expenses Personnel App Creation Marketing Revenues Projected Revenue Alternatives Fully funded by the City Public-Private partnerships 21

22. How Do We Get There? 22

23. How Do We Get There? April 2013: Create City of Austin planning committee. Summer 2013: Host stakeholder meetings. Open app design competition. August 2013: Close design competition. Award contract. January 2014: Begin beta testing. Kick off marketing campaign. March 2014: Finalize implementation. Rollout app to the entire City of Austin. 23

24. Conclusion 24

25. Questions & Answers 25

26. Works Cited Dziekan, K. & Kottenhoff, K. (2007). “Dynamic at-stop real-time information displays for public transopt: Effects on customers,” Transportation Research Part A: Policy & Practice, 41(6), p. 489-501. Ferris, B., Watkins, K., & Borning, K. (2010). “OneBusAway: Results from providing real-time travel information for public transit,” CHI 2010: Bikes & Buses. Ferris, B. (2011). “OneBusAway: Improving the usability of public transit,” ProQuest Dissertations & Theses. Watkins, K.E., Ferris, B., Borning, A., Rutherford, G.S., Layton, D. (2011). “Where is my bus? Impact of real-time information on the perceived and actual wait time of transit riders.” Transportation Research Part A: Policy & Practice, 45(8), p. 839-848. Zhang, F., Shen, Q., & Clifton, K.J. (2008). “Examination of traveler response to real-time information about bus arrivals using panel data,” Transportation Research Record, 2082, p. 107-115. Tang, L. & Thakuriah, P.V. (2012). “Ridership effect of real-time bus information system: A case study in the City of Chicago,” Transportation Research Part C, 22, p. 146-161. Budic, I.Z.D. (1994). “Effectiveness of geographic information systems in local planning,” Journal of the American Planning Association, 60(2), p. 244-263. Johnston, R.A. & de la Barra, T. (2000). “Comprehensive regional modeling for long-range planning: linking integrated urban models and geographic information systems,” Transportation Resarch Part A: Policy & Practice, 34(2), p. 125-136. Barry, J.J. et. al. (2002). “Origin and estimation in New York City with automated fare system data,” Planning and Administration, 1817, p. 183-187. 26

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32. OneBusAway (King Co.) New interface for existing real-time bus arrival information Launched summer 2008, steadily increasing use since then Survey of users (n = 488) recruited through notices More male & young than general ridership, self-reported Similar income levels, represents 10% of daily user base 92% somewhat or much more satisfied with public transit Cited certainty, ease, and flexibility in comments Age significantly negatively correlated with satisfaction 91% reported shorter wait times 78% said they were more likely to walk to a different route Statistically significant increase in feelings of safety 32

33. ShuttleTrac (UMD) Interface for real-time university shuttle arrival information Installed summer 2006, implemented spring 2007 Pre- (n=1679) and Post- (n=1306) launch surveys targeting entire student body Post survey began only two weeks after launch Statistically significant improvement in: Overall satisfaction Feeling of security at night Improved perception of on-time performance No effect on self-reported shuttle trips Suggests stop location and route changes to increase ridership 33

34. Bus Tracker (Chicago) Staggered launches of real-time bus information service Implemented August 2006 to May 2009 Longitudinal study over 2002-10, controlling for outside factors Implementation of Bus Tracker on a route led to: Statistically significant increase in ridership An extra 126 rides per day or a ~2% increase Greater increases in later implementations could signify: Cumulative effect More connectivity along later routes 34