Equity implications of electric bikesharing in Philadelphia.

Bikesharing is an affordable mode of transportation and a potential tool to reduce car usage in cities. However, in many cities, bikesharing seems to be used mostly by affluent populations. Indego, Philadelphia's bikeshare, embraced the promotion of equity as part of its primary goals. While previous measures were not adequate for that cause, Indego decided to integrate e-bikes into its system to promote usage among current non-users. In this study, I examine how the integration of e-bikes influences Indego's usage in disadvantaged areas. For that purpose, I combined official publicly available data using spatial analysis methods. Furthermore, I used random forest and spatial negative binomial regression to examine factors associated with shared bicycle and e-bike usage in Philadelphia. The findings show that e-bikes increase the overall usage of Indego, specifically in disadvantaged areas. In these regions, the users use shared e-bikes for commute, leisure, and other utilitarian purposes, while in the rest of the city, users use e-bikes mainly for commuting. I conclude that the integration of e-bikes was successful in promoting bikesharing usage in disadvantaged areas.

Spatial associations of dockless shared e-scooter usage.

In this study, we explore the usage of e-scooter sharing services in Austin, Texas over about a six-month period. The study is based on trip records of all the shared e-scooter operators in Austin and includes trip start and end locations. We use both analysis of trip patterns and spatial regression techniques to examine how the built environment, land use, and demographics affect e-scooter trip generation. Our findings show that people use e-scooters almost exclusively in central Austin. Commuting does not seem to be the main trip purpose, and usage of e-scooters is associated with areas with high employment rates, and in areas with bicycle infrastructure. People use e-scooter sharing regardless of the affluence of the neighborhood, although less affluent areas with high usage rates have large student populations, suggesting that students use this mode of travel. Implications for planners suggest that better bicycle infrastructure will facilitate e-scooter usage, college towns are a ready market for e-scooter sharing services, and e-scooters may be a substitute for some short non-work trips, reducing car usage, and benefiting the environment.

The Relationships between Adolescents' Obesity and the Built Environment: Are They City Dependent?

There is evidence that the built environment can promote unhealthy habits which may increase the risk for obesity among adolescents. However, the majority of evidence is from North America, Europe and Australia, and less is known about other world regions. The purpose of this study was to examine how the number of overweight and obese adolescents may vary in relation to the built environment, area socioeconomic status (SES), physical activity (PA) and nutritional home environment. We performed a telephone survey of 904 adolescents ages 15-18 from three different cities in Israel. The questionnaire included: reported PA, sedentary behaviors and nutritional home environment. Body Mass Index (BMI) was attained from records of Maccabi Healthcare Services (MHS). The built environment measures were calculated by Geographic Information System (GIS). Multivariable logistic regression analysis was performed to identify variables associated with adolescents' overweight and obesity. The highest level of overweight and obese adolescents was in Beer Sheva (29.2%). The three cities did not differ in built environment characteristics, PA and sedentary behaviors. In Haifa, a more positive nutritional home environment was reported (p = 0.001). Boys, in all three cities presented higher rates of overweight and obesity (29%). After adjusting for covariates, adolescents' overweight and obesity was associated with built environment measures only in a low SES peripheral city (OR = 0.72; 95% CI: 0.56-0.92), and positively associated with higher level of sedentary behavior in the total sample (OR = 1.23; 95% CI:1.03-1.47). This may imply a much more complex causal pathway between the built environment, SES and obesity than suggested in previous literature.