มีเรื่องขอความช่วยเหลือค่ะ ช่วยแปลให้หน่อยได้ไหมคะ เอาคร่าวๆก็พอ D isplays 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 datas 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 Portlands 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 devices GPS to detect a bus riders 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 transits 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 users location, but unlike TAD, it automatically detects the users 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 travelers location and trip status. เราไม่สามารถจริงๆ ค่ะ (พอดีมีคนขอความช่วยเหลือมาอีกที)
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