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  • Globalization, platform work, and wellbeing—a comparative . . .
    Drivers in the three cities were older and few did other “gig-work” indicating that asset driven jobs such as Uber might be in practice at the “top of the line” of on-location platform work This would fit to the arguments on hierarchies between platforms [ 21 ], even if Uber would not be “top of the line” in the USA
  • Research on the big data of traditional taxi and online car . . .
    Between the top and last 1% taxi drivers' difference, an operation pattern comparison was conducted in Shenzhen (Hu et al , 2014), indicating that high-earning and roaming drivers are more inclined to cruise in areas with high-density land use and optimal traffic conditions, whereas low-earning drivers tend to cruise in areas with more previous
  • Qualitative Analysis of Ride-Hailing Regulations in Major . . .
    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 a systematic comparison across
  • Impacts of remote work on vehicle miles traveled and transit . . .
    In comparison, the fixed-effect model yields a smaller estimate (that is, 0 54 as indicated by column 4 in Table 1), suggesting that there might be omitted variables exerting a directional impact
  • The impact of Uber and Lyft on vehicle ownership, fuel . . .
    Effects of TNCs on fleet fuel economy are also ambiguous a priori, as drivers motivated by lower operating costs could plausibly migrate to newer, more efficient vehicles (Uber and Lyft require newer vehicles in some cities (Uber, 2020)), just as drivers motivated by the potential for large-party-trip price premiums may shift to larger, less
  • Can sharing a ride make for less traffic? Evidence from Uber . . .
    Results tended to be similar across major cities, with differences noted below The suburban case shows differences that would be expected for a lower density, auto-oriented context VMT impacts of ride-hail growth depends primarily on three factors: the percentage of trips that are pooled; modal shifts; and deadhead miles
  • Potential effects of automated driving on vehicle travel . . .
    This study empirically confirmed the distinctly increased levels of accessibility for the elderly in view of the results that the largest increase in average VKT across the three cities has been reported in the 61–80 age group, and the average VKT growth rate for the 51–60 age group is the second or third highest among the age groups in





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