Active transportation modes are increasingly being acknowledged for their individual and societal benefits. As a result, funding for pedestrian and bicycle infrastructure in the United States has increased substantially in recent years. However, pedestrian and bicycle equity impacts often go overlooked, which has resulted in an inequitable distribution of active transportation costs and benefits. The paper contributes to this small but growing field by developing an enhanced understanding of active transportation equity, identifying limitations in research and in practice, and providing a set of recommendations for planners and researchers. These recommendations include considering other transportation-disadvantaged groups beyond low-income and minority populations in equity analysis, adopting new performance measures, increasing inter-agency coordination, the need for stronger guidance from the federal government, and more representation from transportation-disadvantaged groups in the public participation and decision-making process. If the costs and benefits of active transportation are to be fairly shared among all users, equity will need to be meaningfully addressed in the planning process.

Monitoring bicycle trips is no longer limited to traditional sources, such as travel surveys and counts. Strava, a popular fitness tracker, continuously collects human movement trajectories, and its commercial data service, Strava Metro, has enriched bicycle research opportunities over the last five years. Accrued knowledge from colleagues who have already utilised Strava Metro data can be valuable for those seeking expanded monitoring options. To convey such knowledge, this paper synthesises a data overview, extensive literature review on how the data have been applied to deal with drivers’ bicycle-related issues, and implications for future work. The review results indicate that Strava Metro data have the potential—although finite—to be used to identify various travel patterns, estimate travel demand, analyse route choice, control for exposure in crash models, and assess air pollution exposure. However, several challenges, such as the under-representativeness of the general population, bias towards and away from certain groups, and lack of demographic and trip details at the individual level, prevent researchers from depending entirely on the new data source. Cross-use with other sources and validation of reliability with official data could enhance the potentiality.

Bike sharing can play a role in providing access to transit stations and then to final destinations, but early implementation of these systems in North America has been opportunistic rather than strategic. This study evaluates local intermodal plan goals using trip data and associated infrastructure such as transit stops and bike share station locations in Austin, Texas, and Chicago, Illinois. Bike sharing use data from both cities suggest a weak relationship with existing rail stations that could be strengthened through collaborative, intermodal planning. The study suggests a planning framework and example language that could be tailored to help address the linkage between bike sharing and transit. Rather than an exhaustive study of the practice, this study provides evidence from these two cities that identify opportunities to improve intermodal planning. Cities that are planning or expanding a bike sharing system should consider carefully how to leverage this mode with existing modes of transport. Regardless of a city’s status in implementing a bike sharing system, planners can leverage information on existing transport systems for planning at regional and local levels.

Traditional methods of travel data collection are often limited by high costs, infrequent updates, or small sample sizes. Several emerging technologies, such as mobile phone positioning, global positioning system tracking, and Bluetooth re-identification, now allow for easier acquisition of long-term continuous trip data with little to no interaction with subjects. Growing interest in the use of these passive data collection techniques for travel modeling necessitates an evaluation of trends and concerns among researchers and practitioners. This paper contributed to this growing discussion in the field by first conducting a literature review, and then developing a web-based survey and interviews with transportation professionals to identify current applications and limitations of passive travel data. Based on the insights gained from these efforts, a set of research recommendations was developed, which will support the continued emergence of travel data collection technologies. The results revealed that several innovative sources of travel data are receiving increased attention and acceptance. On the other hand, more work will need to be done to address the concerns of travel demand modelers and help bridge the gap between the research and practice communities.