Sources and Applications of Emerging Active Travel Data : A Review of the Literature

Peer reviewedPublisher PD

Exploring informants’ perspectives on the role of crowdsourced active travel data

Peer reviewedPostprin

E-scooter and bike-share route choice and detours : modelling the influence of built environment and sociodemographic factors

Unidad de excelencia María de Maeztu CEX2019-000940-MMicromobility is often presented as a sustainable, affordable, and active urban transport option, in comparison to motorised modes. Understanding users routing preferences could help policymakers adapt and design facilities that attract a myriad of micromobility users. Whereas previous research largely focused solely on the built infrastructure, the ways in which sociodemographic factors affect micromobility route choice and infrastructure preferences are unclear. This study examines how elements of the built environment and sociodemographic attributes influence the route selection of 115 e-scooter and bike-share users in Barcelona, Spain. We also compare participants' GPS-tracked trips to the shortest path that they could have followed and develop a multilevel model to estimate how urban and sociodemographic factors affect the decision to deviate from the shortest path. The findings show that micromobility users rarely choose the shortest path since urban elements related to safety, accessibility and aesthetics seem to shape their wayfinding decisions. Results help us comprehend cyclists' and e-scooter riders' distinct route preferences and further illustrate how the gender identity of micromobility users influences route choice and detour. The models indicate that, on average, women take shorter detours than men. We observe gender differences in the way cyclists and e-scooter riders favour certain elements in their trips, such as parked cars and cycling infrastructure. Our findings offer valuable insights into how sociodemographic factors interact with infrastructure and built environment conditions to influence micromobility users' route choice and open up the potential to use these results to manage micromobility flows within cities

Urban Visual Intelligence: Studying Cities with AI and Street-level Imagery

The visual dimension of cities has been a fundamental subject in urban studies, since the pioneering work of scholars such as Sitte, Lynch, Arnheim, and Jacobs. Several decades later, big data and artificial intelligence (AI) are revolutionizing how people move, sense, and interact with cities. This paper reviews the literature on the appearance and function of cities to illustrate how visual information has been used to understand them. A conceptual framework, Urban Visual Intelligence, is introduced to systematically elaborate on how new image data sources and AI techniques are reshaping the way researchers perceive and measure cities, enabling the study of the physical environment and its interactions with socioeconomic environments at various scales. The paper argues that these new approaches enable researchers to revisit the classic urban theories and themes, and potentially help cities create environments that are more in line with human behaviors and aspirations in the digital age

Effects of Bicycle Facility Characteristics and the Built Environment on Bicycle Use: Case Study of Fargo-Moorhead

This study developed a level of traffic stress (LTS) map for Fargo-Moorhead and used crowdsourced bicycle use data from Strava to show relationships between the built environment and bicycle use. The LTS map is useful for showing how friendly and encouraging areas are toward bicycle use, as well as for showing the connectivity of low-stress pathways, and the bicycle ridership model shows how the development of bicycle facilities and other changes to the built environment are associated with bicycle use, as measured using Strava count data. The results of the bicycle use model show that the existence of bicycle facilities is positively associated with bicycle use. This suggests that bicyclists are using the roadway design features that are meant to accommodate them, including shared-use paths, bike lanes, buffered lanes, shared-lane markings, signed-only routes, and shoulders. Other significant predictors of bicycle use included industrial employment density, which was negative, proximity to downtown or to water, low-stress connectivity, traffic volume and speed, which had unexpected positive effects, and median age

CyclingNet: Detecting cycling near misses from video streams in complex urban scenes with deep learning

Cycling is a promising sustainable mode for commuting and leisure in cities. However, the perception of cycling as a risky activity reduces its wide expansion as a commuting mode. A novel method called CyclingNet has been introduced here for detecting cycling near misses from video streams generated by a mounted frontal camera on a bike regardless of the camera position, the conditions of the built environment, the visual conditions and without any restrictions on the riding behaviour. CyclingNet is a deep computer vision model based on a convolutional structure embedded with self-attention bidirectional long-short term memory (LSTM) blocks that aim to understand near misses from both sequential images of scenes and their optical flows. The model is trained on scenes of both safe rides and near misses. After 42 hours of training on a single GPU, the model shows high accuracy on the training, testing and validation sets. The model is intended to be used for generating information that can draw significant conclusions regarding cycling behaviour in cities and elsewhere, which could help planners and policy-makers to better understand the requirement of safety measures when designing infrastructure or drawing policies. As for future work, the model can be pipelined with other state-of-the-art classifiers and object detectors simultaneously to understand the causality of near misses based on factors related to interactions of road users, the built and the natural environments

Sociotechnical co-production of planning information : opportunities and limits of crowdsourcing for the geography and planning of bicycle transportation

Urban planners deploy civic technologies to engage publics with digital tools in a relative vacuum of theory, understanding of challenges, or benefits. The issue, Lewis Mumford might have framed, could be of authoritarian and democratic technics—whether the technology contributes more to top-down control or bottom-up understanding. Building from collaborative planning theory, co-production suggests ways people can leverage technologies to build urban solutions with or without professional planners. Empirical research shows that crowdsourcing to address planning questions with digital civic platforms can help fill or mitigate information gaps, including support for bicycling as a safe and comfortable travel mode. However, no research has addressed how crowdsourced information for bicycle planning offers new insights for safety, the geography of participation, or how its social construction impacts its representation of bicycling in a community. A new framework for evaluating co-productive planning is proposed, considering legitimacy, accessibility, social learning, transparency, and representation (LASTR). This dissertation addresses these concerns of safety, geography, and social construction through the LASTR framework using mixed-methods case studies in Portland, Oregon, and Austin, Texas. Bicycle volumes and street ratings through the crowdsourcing platform, along with geographic information system environmental data, and interviews with thirty-three informants form the basis for evaluating these issues. Viewed from pragmatism and social construction of technology, the social processes of planning and technological developments are intertwined and traced in tandem. The first three chapters frame the problems, build a background in theory, and describe the research questions, planning contexts, and data for analysis. The next three chapters are empirical, evaluating the use of crowdsourced information for bicycle safety, comparing the geography of crowdsourced participation with in-person meetings from both cities’ most recent bicycle planning process, and tracing the sociotechnical representation of crowdsourcing bicyclist information through interviews and case materials. The final chapter summarizes the findings and implications for practice and research. This dissertation shows that the biased representation of bicycling in these two crowdsourcing cases pose opportunities to identify safer bicycling routes and expand public participation geographies, but could exacerbate problems with aligning public improvements with the users of a specific technological approach. Further, the construct of crowdsourcing for urban planning remains flexible and therefore merits further study and knowledge transfer for practitioners and students.Community and Regional Plannin