Policy In Motion: Route360 Bryce Adams, Elizabeth Joseph, Julie
Lindsey, Charles E. Maddox, Lauren Waters 1
Slide 2
Agenda The Challenge The Solution: Route360 How Does It Work?
Will It Work? How Do We Get There? Conclusion Questions and Answers
2
Slide 3
The Challenge Citizens Cannot compare transportation
alternatives using a unified platform City Cannot analyze citizens
transportation preferences and needs 3
Slide 4
4
Slide 5
5
Slide 6
6
Slide 7
7
Slide 8
8
Slide 9
9
Slide 10
10
Slide 11
11
Slide 12
12
Slide 13
13
Slide 14
The Challenge Citizens Cannot compare transportation
alternatives using a unified platform City Cannot analyze citizens
transportation preferences and needs 14
Slide 15
The Solution: Route360 Citizens City 15
Slide 16
16 -
Slide 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
Slide 18
Research Support 18
Slide 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
Slide 20
Route360: How Austin Gets Around How Do We Get There? 20
Slide 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
Slide 22
How Do We Get There? 22
Slide 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
Slide 24
Conclusion 24
Slide 25
Questions & Answers 25
Slide 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
Slide 27
Proposed Budget 27
Slide 28
28
Slide 29
29
Slide 30
30
Slide 31
31
Slide 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
Slide 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
Slide 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
