Inferring mobility of care travel behavior from transit smart fare card data by Awad Abdelhalim, Daniela Shuman, Anson F. Stewart, Kayleigh B. Campbell, Mira Patel, Gabriel L. Pincus, Inés Sánchez de Madariaga, Jinhua Zhao

“…We apply this framework to data from the Washington Metropolitan Area Transit Authority (WMATA). Analyzing data from millions of journeys conducted in the first quarter of 2019, the results of this study show that our proposed workflow can identify mobility of care travel behavior, both in terms of (1) detecting times and places of interest where the share of women travelers in an equally-sampled subset (on basis of inferred gender) of transit users is 10 %–15 % higher than that of men, and (2) finding women significantly more likely to exhibit a consistent accompaniment patterns with riders who are children, elderly, or people with disabilities. …”
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MetRoBERTa: Leveraging Traditional Customer Relationship Management Data to Develop a Transit-Topic-Aware Language Model by Leong, Michael, Abdelhalim, Awad, Ha, Jude, Patterson, Diane, Pincus, Gabriel L, Harris, Anthony B, Eichler, Michael, Zhao, Jinhua

“…First, we utilize semi-supervised learning to engineer a training dataset of 11 broad transit topics detected in a corpus of 6 years of customer feedback provided to the Washington Metropolitan Area Transit Authority (WMATA). We then use this dataset to train and thoroughly evaluate a language model based on the RoBERTa architecture. …”
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