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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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