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Bike-sharing data have been a valuable source for urban transport research. While most studies focus on Origin-Destination (OD) data of users' bike trips in bike-sharing researches, little has been investigated on mobility patterns from the perspective of bikes. Bike mobility can not only reflect human mobility patterns but also provide practical insights for understanding the bike-sharing system and assisting bike management. In particular, continuous bike movement information can provide abundant spatiotemporal connection information for spatial context analysis in data without user ID information. This paper studies bike mobility by shifting the perspective from OD analysis to the new analysis primitive of bike mobility chain (BMC). First, bike-sharing OD trip data are reconstructed to obtain BMCs. Second, the reconstructed BMCs are feature-augmented, based on which the spatiotemporal characteristics of bike mobility are analyzed. Finally, the word embedding model and the clustering algorithm are applied to the BMCs, which embody rich spatiotemporal inter-station connectivity, to facilitate mining stations with similar bike mobility. The methodology is applied for the comparative study of bike-sharing activities on workdays and holidays using data collected from New York Citi-Bike. The experimental results manifest locality patterns of bike mobility in both mobility chain indicators and multi-scale station embedding analysis results, and reveal disparities in BMC statistic indicators and spatial distribution mode of stations with similar bike mobility. These results can provide a new empirical reference for urban bike management in different time periods. As a more general implication, this study broadens the perspective for OD-based data analysis and paves the path to leverage the thriving trajectory-based research for further investigations. [ABSTRACT FROM AUTHOR]

This paper develops an analytical framework to estimate commute accessibility and adoption of various ridehailing service concepts across the US by synthesizing individual commute trips using national Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics (LODES) data. Focusing on potential improvements in cost and time that could be enabled by vehicle automation, we use this modeling framework to simulate a lower-price autonomous service (e.g., 50% or 75% lower) with variable wait times and implementation levels (solo, pooled, and first/last mile transit connections services, alone or in combination) to determine how they might affect adoption rates. These results are compared across metrics of accessibility and trip density, as well as socioeconomic factors such as household income. We find – unsurprisingly – that major cities (e.g. New York, Los Angeles, and Chicago) support the highest adoption rates for ridehailing services. Decreases in price tend to increase market share and accessibility. The effect of a decrease in price is more drastic for lower income groups. The proposed method for synthesizing trips using the LODES contributes to current travel demand forecasting methods and the proposed analytic framework can be flexibly implemented with any other mode choice model, extended to non-commute trips, or applied to different levels of geographic aggregation. [ABSTRACT FROM AUTHOR]

Using the example of domestic connections departing from the New York Metropolitan Area, this paper contributes to research on airports' catchment areas in Multi Airport Regions by exploring their spatio-temporal dynamics. Given that previous research has consistently shown that airport accessibility and different elements of airport utility (fare, connectivity characteristics, on-time performance) are key drivers of airport choice, we draw the analogy with Huff models to calculate airport attractiveness to passengers in different census block groups. We marshal data sources that allow for an assessment of the spatio-temporal variability in the accessibility and utility of airports, which allows us to compare catchment areas for different times of the day, days of the week, and quarters of the year, and this for different utilities as well as overall utility. Results reveal different types of dynamics, and can be used as the input to follow-up research. We argue that such a model-based approach holds major potential in comparative research and research on MAR dynamics, but should be fine-tuned through the use of other information sources, refined model specifications, and state preference data. [ABSTRACT FROM AUTHOR]