Active travel (AT) has the potential to integrate with, or in some cases substitute for, trips taken by motorized transportation. In this paper we review relevant research on AT outcomes to address the potential of AT and emerging data sources in supporting the transport paradigm shift toward AT. Our analysis identifies physical, mental, built and physical environmental, monetary, and societal outcomes. Traditional methods used to acquire AT data can be divided into manual methods that require substantial user input and automated methods that can be employed for a lengthier period and are more resilient to inclement weather. Due to the proliferation of information and communication technology, emerging data sources are prevailing and can be grouped into social fitness networks, in-house developed apps, participatory mapping, imagery, bike sharing systems, social media, and other types. We assess the emerging data sources in terms of their applications and potential limitations. Furthermore, we identify developing policies and interventions, the potential of imagery, focusing on non-cycling modes and addressing data biases. Finally, we discuss the challenges of data ownership within emerging AT data and the corresponding directions for future work.

In the era of ubiquitous technology, crowdsourced data is an emerging frontier for active travel (AT) studies. In this work, we utilize accrued knowledge from interviews and previous literature regarding crowdsourced data strengths, challenges, usefulness and reliability for future informants who seek to embrace crowdsourced data. We review four main types of crowdsourced data: social fitness networks, in-house developed apps, bike sharing systems and participatory mapping. The strengths of crowdsourced data include providing fine data coverage, precision, details, immediacy and empowering users to participate in decision-making. Potential challenges that might arise from adopting this data are related to technical, privacy, proprietorship, financial and data fragmentation factors. In terms of usefulness, crowdsourced data lend themselves to before and after analysis, assessing current infrastructure, and investment prioritization. Reliability issues that may undermine the credibility of crowdsourced data are also discussed, as well as remedies for these concerns.

Given the numerous benefits of active travel (human-powered transportation), in this paper, we argue that using crowdsourced data and a spatial heterogeneity treatment enhances the predictive performance of data modelling. Using such an approach thus increases the amount of insight that can be obtained to improve active travel decision-making. In particular, we model cyclists’ route choices using data on cycling trips and street network centralities obtained from Strava and OSMnx, respectively. It was found that: i) the number of cyclist trips is spatially clustered; and ii) the spatial error model exhibits a better predictive performance than spatial lag and ordinary least squares models. The results demonstrate the ability of the fine-grained resolution of crowdsourced data to provide more insights on active travel compared to traditional data.