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• Propose a methodological framework for assessing the equity performance of bike sharing systems. • Model Individual accessibility to activity locations using bike-sharing as the mode of transportation. • Analyze equity performance from the perspective of both horizontal and vertical equity through a series of equity measures. • Carry out a case study with real data from the Coast Bike Share system in southern Tampa. The rising adoption of bike-sharing systems brings significant benefits to individuals and society as a whole. However, whether these benefits are distributed throughout society in a fair manner is still an open question. This study presents a methodological framework for assessing the equity performance of bike-sharing systems, with Coast Bike Share system in southern Tampa as a case study. The framework integrates three different datasets: bike-sharing infrastructure, individual travel itineraries and individual sociodemographic attribute data. With these datasets, we model individual accessibility to activity locations using bike-sharing as the mode of transportation by analyzing the "walking-cycling-walking" process of a bike-sharing trip and the trip-chaining behavior in individuals' travel itineraries. Further, a series of equity measures are used to analyze equity performance from the perspective of both horizontal and vertical equity. To assess horizontal equity, we apply the Lorenz curve, Gini index and mapped geographic differences. For vertical equity, we compare descriptive statistics by group, perform analysis of variance, and calculate the subgroup inequality index using disaggregated data. Results reveal that accessibility to bike-sharing is not evenly distributed among individuals in the population, within the geographic space, and among different sociodemographic groups of southern Tampa. Further, our results also highlight the need to incorporate high-resolution disaggregated data and tour-based analysis to address the equity performance of bike-sharing systems. [ABSTRACT FROM AUTHOR]

Assessing the equity impacts of transportation systems/policies has become a crucial component in transportation planning. Existing statistical modeling approaches for transportation equity analysis have typically assumed that parameter estimates are constant across all observations and used data aggregated to certain geographic units for the analysis. Such methods cannot capture unobserved factors that are not contained in the dataset, i.e., unobserved heterogeneity , which is likely to be present in the increasingly popular disaggregated datasets. To investigate whether there is unobserved heterogeneity in transportation equity impacts, this study carries out an empirical study focusing on the distribution of individual accessibility to activity locations via bike-sharing in southern Tampa. A disaggregated dataset containing information on individual bike-sharing accessibility and socio-economic factors is modeled with a random parameters logit model that allows for the investigation of possible unobserved heterogeneity. Further, models are estimated using data aggregated to parcel- and TAZ-levels to explore the impacts of data aggregation on model estimation results. The models unveil the unobserved heterogeneity in bike-sharing accessibility among populations in different groups defined by different sociodemographic factors in southern Tampa. These results shed insights into how the inconsistent disparity direction of transportation outcomes across individuals in a population group can be measured from the heterogeneity effects. Finally, a comparison between different models show that to capture such inconsistency, the use of disaggregated data with heterogeneity models is highly recommended for transportation equity analysis. [ABSTRACT FROM AUTHOR]