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Collecting effective data is a fundamental step in developing transport networks and related research. Social media have become an emerging source of data for traffic analyses. In this paper, we demonstrate that the function of a city influences the utility of social media data in travel demand models by generating models for eight US cities with different functions. Data from Twitter and Foursquare, as well as other socio-demographic information, are considered as independent variables in Origin-Destination trip regression models generated via a Random Forest regression technique. Model performance with and without use of social media data are compared via 10-fold cross-validation. The results indicate that the accuracy of the models for all eight cities improved when independent variables based on social media data were included. The performance was most improved in metropolitan areas, followed by rural and tourist areas. Inspired by this finding, we conclude that the city function influences the utility of social media data in travel demand models. Meanwhile, we create models based on trip purpose and transport mode to explore other factors that may impact the efficiency of applying social media data in transport research. [ABSTRACT FROM AUTHOR]

Growing interest in performance reliability and improving data availability is motivating a shift toward probabilistic treatments of travel time across a number of intelligent transportation system applications. Hazard-based analysis supports the development of probabilistic travel time models and latent-class-style methodologies capture how the mechanisms affecting travel time are expected to differ based on congestion status. Benefiting from rich data available for metropolitan freeway travel times in the San Francisco Bay Area, this paper studies how congestion state, traffic demand, roadway variables, and weather impact travel-time performance in a probabilistic regime. Congestion state is captured as an inferred yet unobserved segmentation in the data using latent segmentation, and hazard-based models of travel times are developed for the congested and uncongested classes. The final model represents an intuitive description of the factors that probabilistically influence travel time on freeways. The predicted aggregation shows excellent agreement with the data. With opportunities for improvement in the data sources and complexity of the latent segmentation, the final model nevertheless represents a simple yet flexible solution for understanding the relationships between travel time, traffic state, and relevant behavioral, geometric, and environmental factors. [ABSTRACT FROM PUBLISHER]