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The first- and last-mile (FM/LM) problem is a major deterrent to public transit use. With the rise of shared micromobility options such as shared e-scooters in recent years, there is a growing interest in understanding their potential to serve as a last-mile transit solution. However, empirical data regarding the integrated use of shared micromobility and public transit have been limited so far. As a result, much is unknown regarding the spatiotemporal patterns and characteristics of shared micromobility trips serving as an FM/LM connection to transit. This paper addresses these knowledge gaps by leveraging a novel dataset (i.e., the Spin post-ride survey dataset) that records thousands of transit-connecting shared e-scooter trips in Washington DC. Specifically, we used the dataset to reveal the spatiotemporal patterns of transit-connecting shared e-scooter trips in Washington DC, resulting in some major policy insights regarding the integral use of shared e-scooters and public transit. We further leveraged the dataset to validate if and to what extent a commonly applied buffer-zone approach can infer FM/LM micromobility trips accurately. Statistical tests showed that the actual FM/LM Spin e-scooter trips differ from inferred FM/LM Spin e-scooter trips in both spatial and temporal dimensions. This indicates that the common practice of inferring FM/LM micromobility trips with a buffer-zone approach can lead to inaccurate estimates of transit-connecting micromobility trips. [ABSTRACT FROM AUTHOR]

As public transit becomes increasingly personalized with services that can dynamically adapt to demand as it is realized over the course of a day, it is important to ensure that the services are equitable and do not grow on unintended or endemic biases, or create new biases. Such biases might arise from prioritizing cost-effectiveness or system efficiency. This paper explicitly integrates equity considerations in the design of public, microtransit services, i.e., shared-ride public services that typically uses vans or shuttles to move passengers along dynamically generated routes operating within predefined zones. To this end, a mathematical model and solution methodology for designing the microtransit zones with the specific aim of creating equitable services is proposed. Findings from the application of the proposed methodology on a case study of Washington, D.C. showcase how even well-intentioned designs created with equity in mind can result in poor service coverage for the same individuals most in need of a reliable and efficient form of transit. The findings also show that resulting service zones developed from a combination of two forms of equity, horizontal and vertical, exceled in serving Black, Indigenous, and People of Color populations, older adults, and low-income households with only a small reduction in the number of potential riders with services as compared with designs developed from more typical vertical or horizontal equity measures. [ABSTRACT FROM AUTHOR]

Abstract Bikesharing has gradually become an adopted form of mobility in urban area recent years as one sustainable transportation mode to bring us many social, environmental, economic, and health-related benefits and rewards. There is increased research toward better understanding of bikesharing systems (BSS) in urban environments. However, our comprehension remains incomplete on the patterns and characteristics of BSS. In this paper, aiming to help improving sustainability in multimodal transportation through BSS, we perform a systematic data analysis to examine underlying patterns and characteristics of the system dynamics in a bikeshare network and to acquire implications of the patterns and characteristics for decision making. As a case study, we use trip history data from the Capital Bikeshare system in the Washington DC area and some additional data sources. The study covers seven important aspects of bikeshare transportation systems, which are respectively trip demand and flow, operating activities, use and idle times, trip purpose, origin-destination flows, mobility, and safety. For these aspects, by using appropriate statistical methods and geographic techniques, we investigate travel patterns and characteristics of BSS from data to evaluate the qualitative and quantitative impacts of the inputs from key stakeholders on main measures of effectiveness such as trip costs, mobility, safety, quality of service, and operational efficiency, where key stakeholders include road users, system operators, and city. We also disclose some new patterns and characteristics of BSS to advance the knowledge on travel behaviors. Finally, we briefly summarize our findings and discuss the implications of the patterns and characteristics for data-driven decision supports from the relations between BSS and key stakeholders for promoting bikeshare utilization and transforming urban transportation to be more sustainable. [ABSTRACT FROM AUTHOR]