In recent years, and especially since the development of the smartphone, enormous amounts of data relevant for transportation have become available. These data hold out the potential to redefine how transportation system (i.e. design, planning and operations) is done. While researchers in both academia and industry are making advances in using this data to transportation system ends (e.g. information inference from collected data), little attention has been paid to four larger scale challenges that will need to be overcome if the potential for Big Transportation Data is to be harnessed for transportation decision-making purposes. This paper aims to provide awareness of these large-scale challenges and provides insight into how we believe these challenges are likely to be met.
Smartphone based travel data collection has become an important tool for the analysis of transportation systems. Interest in sharing travel survey data has gained popularity in recent years as “open data initiatives” by governments seek to allow the public to use these data, and hopefully to contribute their findings and analysis to the public sphere. The public release of such precise information, particularly location data such as place of residence, opens the risk of privacy violation. At the same time, in order for such data to be useful, as much spatial resolution as possible is desirable for utility in transportation applications and travel demand modeling. This paper evaluates the efficacy of two geographic random perturbation methods—geo-indistinguishability and donut geomask—in protecting the privacy of respondents whose residential location may be published. The study measures the performance of location privacy methods, preservation of utility, and randomness in the distribution of perturbation distances with varying parameters. It is found that both methods produce distributions of spatial perturbations that conform closely to common probability distributions and, as a result, that the original locations can be inferred with little information and a high degree of precision. It is also found that while achieved k -estimate anonymity increases linearly with desired anonymity for the donut geomask, geo-indistinguishability is highly dependent upon its privacy budget factor and is not very effective at assuring desired achieved k -estimate anonymity.
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