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Displays that provide real-time arrival information for

buses, subways, light rail, and other transit vehicles are

available in many cities worldwide, at places such as rail stations,

transit centers, and major bus stops. However, providing

and maintaining such displays at, for example, every single bus

stop in a region is prohibitively expensive. With the increased

availability of powerful mobile devices and the public availability

of transit schedule data in machine-readable formats, many

tools have been developed to make this information available

on mobile devices.

Stuart Maclean, Daniel Daily, and their colleagues at the

University of Washington developed developed a series of innovative

transit tools, including one of the first online bus-tracking

systems, BusView.1 More recently, Google Transit, which

started as a Google Labs project in December 2005 (http://

maps.google. com/help/maps/transit/partners/faq.html), is

now directly integrated into the Google Maps product on many

mobile phones and provides transit trip-planning for more than

405 cities around the world (www.google.com/intl/en/landing/

transit/#mdy). Interfaces to Google Transit exist on a variety of

mobile devices, employing location sensors such as GPS and

Wi-Fi localization on the device to determine a starting location

for trip planning.

Besides being useful to transit riders around the world,

Google Transit is also significant for establishing a de facto

standard for exchanging transit schedule data: the Google

Transit Feed Specification (GTFS; http://code.google.com/transit/

spec/transit_feed_specification.html). The upshot is that

many of the transit agencies participating in the Google Transit

program have also released their transit scheduling data in the

GTFS format for third-party developers to work with. Development

ecosystems have grown out of this data’s public availability,

with many “transit hackers” working on innovative uses of

transit data. The Portland TriMet third-party applications page,

for example, lists more than 20 applications that use Portland’s

transit data, many targeted at providing transit data on mobile

devices and many of which use these devices’ localization capabilities

to return location-relevant results (http://trimet.org/

apps/index.htm). Similar ecosystems exist in San Francisco and

the surrounding Bay Area, Chicago, and other major cities.

One mobile application that employs GTFS transit data is the

Travel Assistance Device (TAD) developed at the University of

South Florida.2 TAD uses a mobile device’s GPS to detect a bus

rider’s location and prompt that person when his or her stop

is near. The user manually enters routes and desired stops into

the system for later detection. The application is specifically for

riders with cognitive impairments to increase their usability of

public transit.

Another mobile application to improve public transit’s usability

can be found in previous research at the University of Washington.

The Opportunity Knocks system3 provides a mobile application

to give cognitive assistance to transit riders. Like TAD,

Opportunity Knocks uses GPS data to model a user’s location,

but unlike TAD, it automatically detects the user’s current mode

of transportation from GPS traces and learns the important

places he or she typically travels to, such as home and workplace,

without manual labeling. On the basis of these learned

models, the application can automatically predict where a user

is headed given only a small amount of tracking data and can

detect when the user does something unexpected, such as forgetting

to get off the bus at the regular stop.

A third such example is the Mobility Agents system,4 also

intended for users with cognitive impairments. It provides

prompts to a traveler on a handheld device and simultaneously

communicates to a caregiver the traveler’s location and trip

status.         

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