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This paper presents the results of an evaluation study of the behavioral impacts of a high-occupancy toll (HOT) lane project in Minnesota. The I-394 MnPASS Express Lane Project is the fifth HOT lane project implemented in the United States. HOT lanes remain a new enough concept that there is little empirical information on methods for evaluating them or on their impacts on travel behavior for transportation planners and policy makers to use when making decisions about future facilities. The MnPASS evaluation study is significant not only because it uses a panel design but also because it involved multiple waves of stated preference (SP) experiments. These waves were conducted before and after project implementation. This paper uses information from the evaluation study to examine two significant issues: (a) How applicable is a panel design to evaluating road pricing projects? and (b) How does willingness to pay vary on the basis of before-and-after iterations of the SP experiments?

Emerging big data resources and practices provide opportunities to improve transportation safety planning and outcomes. However, researchers and practitioners recognise that big data from mobile phones, social media, and on-board vehicle systems include biases in representation and accuracy, related to transportation safety statistics. This study examines both the sources of bias and approaches to mitigate them through a review of published studies and interviews with experts. Coding of qualitative data enabled topical comparisons and reliability metrics. Results identify four categories of bias and mitigation approaches that concern transportation researchers and practitioners: sampling, measurement, demographics, and aggregation. This structure for understanding and working with bias in big data supports research with practical approaches for rapidly evolving transportation data sources.

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Emerging big data resources and practices provide opportunities to improve transportation safety planning and outcomes. However, researchers and practitioners recognise that big data from mobile phones, social media, and on-board vehicle systems include biases in representation and accuracy, related to transportation safety statistics. This study examines both the sources of bias and approaches to mitigate them through a review of published studies and interviews with experts. Coding of qualitative data enabled topical comparisons and reliability metrics. Results identify four categories of bias and mitigation approaches that concern transportation researchers and practitioners: sampling, measurement, demographics, and aggregation. This structure for understanding and working with bias in big data supports research with practical approaches for rapidly evolving transportation data sources.

In May 2016, Uber and Lyft suspended services following the defeat of Proposition 1 in Austin, TX. In response, several ridesourcing services entered the market. This paper provides information on the individuals most likely to switch back to Uber or Lyft after service restoration using estimated indifference bands. We hypothesize that people will not switch back unless a given trip's cost saving is beyond a positive threshold due to boundedly rational behavior. The study revealed, among others, that those who use TNC for social purposes, have lower indifference bands and are more likely to switch to Uber or Lyft.

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A 31-year-old woman developed increasing signs of congestive heart failure 15 years following placement of a mitral Starr-Edwards mechanical valve. A preoperative transesophageal echocardiogram (TEE) demonstrated a large, obstructive valvular thrombus and the patient underwent successful reoperative valve replacement.