The influence of geographic mobility and gender on Indiana wages

The study examines wage differentials of individuals experiencing unemployment episodes using a multivariate analysis of wage and unemployment records. The focus is the wage effect of small distance geographic mobility (micro-mobility) during job seeking. The results identify limitations on geographic micro-mobility as a source of wage disparity in the re-employment market. The study isolates persistent gender differences in geographic mobility rates and hypothesizes this as a potential source of gender-wage disparity in both the re-employment and greater labor market. The data and methods are unique. The dataset is Indiana administrative wage records over a ten-year period for individuals that experience unemployment episodes. The study assesses unemployment as an exogenous shock on wages to determine underlying influences in the labor market. The novel approach is unconstrained by limitations associated with aggregated or proxy data.

Autonomous vehicles for micro-mobility

Autonomous vehicles have been envisioned for more than 100 years. One of the first suggestions was a front cover of Scientific America back in 1916. Today, it is possible to get cars that drive autonomously for extended distances. We are also starting to see micro-mobility solutions, such as the Nuro vehicles for pizza delivery. Building autonomous cars that can operate in urban environments with a diverse set of road-users is far from trivial. Early 2018 the Contextual Robotics Institute at UC San Diego launched an effort to build a full stack autonomous vehicle for micro-mobility. The motivations were diverse: i) development of a system for operation in an environment with many pedestrians, ii) design of a system that does not rely on dense maps (or HD-maps as they are sometimes named), iii) design strategies to build truly robust systems, and iv) a framework to educate next-generation engineers. In this paper, we present the research effort of design, prototyping, and evaluation of such a vehicle. From the evaluation, several research directions are explored to account for shortcomings. Lessons and issues for future work are additionally drawn from this work.

Exploring Performances of Electric Micro-Mobility Vehicles and Behavioural Patterns of Riders for In-Depth Accident Analysis

(1) Background: Electric micro-mobility vehicles (i.e., e-bikes and e-scooters) represent a fast-growing portion of the circulating fleet, leading to a multiplication of accident cases also attributable to risky behaviours adopted by the riders. Still, data on vehicle performance and rider behaviour are sparse and difficult to interpret (if not unavailable). Information regarding the overall accident dynamics can, however, aid in identifying users’ risky riding behaviour that actually led to a harmful event, allowing one to propose efficient strategies and policies to reduce the occurrence of road criticalities. (2) Methods: Speed and acceleration data of six cyclists of traditional and electric bikes were extracted from six closed-circuit experiments and real road tests performed in the city of Florence (Italy) to derive their behavioural patterns in diverse road contexts. (3) Results: The application of analysis of variance and linear regression procedures to such data highlights differences between men and women in terms of performance/behaviour in standing start; additionally, the use of e-bikes favours a higher speed ride in correspondence to roundabouts and roads with/without the right of way. To thoroughly assess the rider’s responsibilities in an eventual accident, an ancillary procedure was highlighted to evaluate whether a micro-mobility vehicle complies with the applicable regulations. (4) Conclusion: With these results, the prospective recognition of rider behaviour was facilitated during the investigation process, and the abilities to extract such relevant information from in-depth accident data wereconsequently enhanced.