Looking ahead to the future-stage autonomous transportation system (ATS), personal mobility service (PMS) aims to provide the recommended travel options based on both microscopic individual travel demand and the macroscopic supply system objectives. Such a goal relies on massive heterogeneous data to interpret and predict user travel intentions, facing the challenges caused by prevalent centralized approaches, such as an unbalanced utilization rate between cloud and edge, and data privacy. To fill the gap, we propose a federated logit model (FMXL), for estimating user preferences, which integrates a discrete choice model—the mixed logit model (MXL), with a novel decentralized learning paradigm—federated learning (FL). FMXL supports PMS by (1) respectively performing local and global estimation at the client and server to optimize the load, (2) collaboratively approximating the posterior of the standard mixed logit model through a continuous interaction mechanism, and (3) flexibly configuring two specific global estimation methods (sampling and aggregation) to accommodate different estimation scenarios. Moreover, the predicted rates of FMXL are about 10% higher compared to a flat logit model in both static and dynamic estimation. Meanwhile, the estimation time has been reduced by about 40% compared to a centralized MXL model. Our model can not only protect user privacy and improve the utilization of edge resources but also significantly improve the accuracy and timeliness of recommendations, thus enhancing the performance of PMS in ATS.

Providing equal geographical access to hospitals, either in the public or private healthcare sector, is vital and will benefit public health in general. Against the background of the partial privatization of the healthcare sector, the impact of private hospitals on equal healthcare access has been a highly neglected issue. We have applied an assessment methodology to study this situation by comparing the status quo scenario with one without private hospitals, based on accessibility analysis and spatial equality measurements. The case study of Beijing, China revealed a double-sided impact. With the presence of private hospitals, the Gini coefficient of spatial accessibility in urban districts was reduced from 0.03391 to 0.03211, while it increased from 0.1734 to 0.1914 in suburban districts. Thus, private hospitals improved spatial equality in urban districts in Beijing but jeopardized it in suburban districts. These research findings should enlighten policymakers to promote healthcare equality but would also need to be repeated in some other big cities.