Ride-hailing services, which are provided by companies such as Uber and Lyft, are increasingly common in many American cities. Several cities are now regulating or considering regulation of these services. However, regulation has not been well documented across jurisdictions, and city planners and policy makers often want to understand what regulations are being put in place in comparable metropolitan areas. Therefore, the objective of this research was to provide a qualitative comparison of the regulations of ride-hailing companies between major American cities. This goal was accomplished through the evaluation of five driver-related and three company-related types of ride-hailing regulations in 15 major American cities. The driver-related regulations included requirements for background checks, driver’s licenses, vehicle registrations, special licenses such as business licenses, and external vehicle displays. The company-related regulations included requirements for the number of ride-hailing vehicles operating in a metropolitan area, providing a list of drivers to the city, and sharing trip data with the city. The results of this qualitative analysis reveal significant variation in the number of driver-related regulations imposed in the cities that were evaluated. Another key finding is that ride-hailing companies may be less likely to operate in cities where fingerprint-based background checks are mandatory. As ride-hailing regulations continue to evolve, this research can help planners and policy makers better understand the current state of local regulations by providing a systematic comparison across major metropolitan areas in the United States.
One of the fundamental components of transit planning is understanding passenger demand, which is commonly represented with origin–destination (O-D) matrices. However, manual collection of detailed O-D information through surveys can be expensive and time-consuming. Moreover, data from automated fare collection systems, such as smart cards, often include only entry information without tracking where passengers exit the transit network. New mobile ticketing systems offer the opportunity to prompt riders about their specific trips when they purchase a ticket, and this information can be used to track O-D patterns during the ticket activation phase. Therefore, the objective of this research is to use back-end mobile ticketing data to generate passenger O-D matrices and compare the outcome with O-D matrices generated with traditional onboard surveys. Iterative proportional fitting was used to create O-D matrices with both mobile ticketing and onboard survey data. These matrices were compared using Euclidean distance calculations. This work was done for the East River Ferry in New York City, and the results show that during peak periods, mobile ticketing data closely match survey data. However, in the off-peak period and during weekends, when travelers are more likely to be noncommuters and tourists, matrices developed from mobile ticketing and survey data have greater differences. The impact of occasional riders making noncommute trips is the likely cause for these differences, because commuters are familiar with using the mobile ticketing product and occasional riders are more likely to use paper tickets on the ferry service.
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