We describe the design and evaluation of a system named Quantified Traveler (QT). QT is a Computational Travel Feedback System. Travel Feedback is an established programmatic method whereby travelers record travel in diaries, and meet with a counselor who guides her to alternate mode or trip decisions that are more sustainable or otherwise beneficial to society, while still meeting the subject's mobility needs. QT is a computation surrogate for the counselor. Since counselor costs can limit the size of travel feedback programs, a system such as QT at the low costs of cloud computing, could dramatically increase scale, and thereby sustainable travel. QT uses an app on the phone to collect travel data, a server in the cloud to process it into travel diaries and then a personalized carbon, exercise, time, and cost footprint. The subject is able to see all of this information on the web. We evaluate with 135 subjects to learn if subjects let us use their personal phones and data-plans to build travel diaries, whether they actually use the website to look at their travel information, whether the design creates pro-environmental shifts in psychological variables measured by entry and exit surveys, and finally whether the revealed travel behavior records reduced driving. Before and after statistical analysis and the results from a structural equation model suggest that the results are a qualified success.3
Recently, transit agencies have begun opening their route configuration and schedule data to the public, as well as providing online application programming interfaces to real-time bus positions and arrival estimates. On the basis of this infrastructure for providing transit data over the Internet, the authors developed an algorithm to calculate the travel times of K shortest paths in a public transportation network where all wait and travel times were known only in real time. Although there was a large body of work on routing algorithms in transit networks, the authors took cues from an algorithm to find the shortest paths in road networks, called transit node routing. This approach was based on observation of intuitive behavior by humans: when taking transit, travelers looked for a particular set of transfer points that connected transit routes that led from the origin and destination. A lookup table was precomputed of feasible paths between the origin stop of every bus route to the terminus of every other bus route by using the transfer points. This precomputation of paths significantly reduced the computation time and number of real-time arrival requests to transit agency servers, the bottleneck in computing this problem. The computational complexity of the algorithm is linear in real time, and implementation results show that queries from a web server are returned in 3 s in the worst case.
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