Pedestrian and bicycle volume data is one of the most fundamental types of data for active transportation planning. However, they are not yet well developed. This paper explores an innovative method that uses drone technology to collect pedestrian and bicycle volume data. It confirms the feasibility of the technology as an alternative method to collect complex movements of pedestrians and bicycles. Furthermore, this paper presents a method that converts the video footage to a spatiotemporal dataset. The dataset includes not only the pedestrian and bicycle count data, but also their behavior and characteristics. The spatiotemporal data can become a valuable resource for a variety of active transportation planning practices and research, including the collection of pedestrian and bicycle volume data in parks and recreational areas, the study of collisions between pedestrians and bicycles, and the analysis of the social path. Since it is possible to envision that the development of technologies can overcome current technical difficulties, such as battery lifespan, it is worth considering the application of drone technology to active transportation planning practice and research.

Given the concerns about urban mobility, traffic congestion, and greenhouse gas (GHG) emissions, extensive research has explored the relationship between the built environment and transit ridership. However, the nature of aggregation and the cross-sectional approach of the research rarely provide essential clues on the potential of a transit system as a sustainable mobility option. From the perspective of longitudinal sustainability, this paper develops regression models for rail transit stations in the Los Angeles Metro system. These models attempt to identify the socio-demographic characteristics and land use features influencing longitudinal transit ridership changes. Step-wise ordinary least square (OLS) regression models are used to identify factors that contribute to transit ridership changes. Those factors include the number of dwelling units, employment-oriented land uses such as office and commercial land uses, and land use balance. The models suggest a negative relationship between job and population balance with transit ridership change. They also raise a question regarding the 0.4 km radius commonly used in transit analysis. The models indicate that the 0.4 km radius is too small to capture the significant influence of the built environment on transit ridership.

This paper aims to investigate the role of neighborhood tree and greenspace on asthma morbidity, especially asthma emergency room visits. We employed advanced spatial data which allow for precisely capturing both the quantity and the features of tree and greenspace within a neighborhood environment. The results from the spatial regression models in Los Angeles County revealed that the features of trees and greenspace, such as the configuration of the tree canopy, the level of tree clustering, and private neighborhood greenspaces contribute to decreasing asthma morbidity, in addition to the quantity of tree and greenspace acreages. Notably, however, large scale greenspace, such as golf courses, school playgrounds, and parks fails to reduce the number of asthma emergency room visits at the statistically significant level. These findings imply that the creation of dense or clustered tree patches and small-scale neighborhood greenspaces might play a substantial role in mitigating air quality and consequently reducing asthma emergency room visits.

This study examines how built environment factors at trip destinations influence nonmotorized travel behavior in the City of Long Beach, California. Using 2008–2009 National Household Travel Survey with California Add-Ons, we found that nonmotorized users tend to choose more clustered destinations than motorized users, and that density, diversity, and design at destinations significantly affect mode choice decisions. Transportation networks and nonmotorized facilities at trip destinations are especially important factors for nonmotorized mode choice. Future policy and research need to consider built environment factors at trip destinations to effectively accommodate nonmotorized travel within a city.