Quantifying relation between mobility patterns and socioeconomic status of dockless sharing-bike users

Abstract

Bikes are among the healthiest, greenest, and most affordable means of transportation for a better future city, but mobility patterns of riders with different income were rarely studied due to limitation on collecting data. Newly emergent dockless bike-sharing platforms that record detailed information regarding each trip provide us a unique opportunity. Attribute to its better usage flexibility and accessibility, dockless bike-sharing platforms are booming over the past a few years worldwide and reviving the riding fashion in cities. In this work, by exploiting massive riding records in two megacities from a dockless bike-sharing platform, we reveal that individual mobility patterns, including radius of gyration and average travel distance, are similar among users with different income, which indicates that human beings all follow similar physical rules. However, collective mobility patterns, including average range and diversity of visitation, and commuting directions, all exhibit different behaviors and spatial patterns across income categories. Hotspot locations that attract more cycling activities are quite different over groups, and locations where users reside are of a low user ratio for both higher and lower income groups. Lower income groups are inclined to visit less flourishing locations, and commute towards the direction to the city center in both cities, and of a smaller mobility diversity in Beijing but a larger diversity in Shanghai. In addition, differences on mobility patterns among socioeconomic categories are more evident in Beijing than in Shanghai. Our findings would be helpful on designing better promotion strategies for dockless bike-sharing platforms and towards the transition to a more sustainable green transportation.

Unveiling Latent Structure of Venture Capital Syndication Networks

“…As for the VC partition task, in order to objectively partition them based on their investment performance indicators, we devise an iterative nonparametric Loubar method [ 32 , 33 , 34 , 50 ] based on the derivative of Lorenz curves [ 35 ].…”

“…Failure is also an important characteristic of the entrepreneurial reality though it is also been viewed from a negative perspective [ 31 ]. In this paper, we propose an iterative Loubar method [ 32 , 33 , 34 ] based on Lorenz curve [ 35 ] to make objective classification of successful as well as unsuccessful VC institutions based on their principled features, which do not require setting arbitrary thresholds to separate different categories and the number of categories can be automatically determined. The features fed to our method include the number of IPO (Initial Public Offering)&MA (Merge and Acquisition) and the number of investments of VC institutions, which are important indicators of investment performance [ 12 ].…”