Multiuse trail intersection safety analysis: A crowdsourced data perspective

Jestico, Ben; Nelson, Trisalyn A.; Potter, J; Winters, Meghan

doi:10.1016/j.aap.2017.03.024

Cited by 16 publications

(20 citation statements)

References 32 publications

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“…An interesting aspect of the BikeMaps.org data is reporting along multi-use paths. Most official reporting does not capture incidents that happen along multi-use paths, given existing biases toward reporting incidents that involve vehicles (Jestico et al, 2017). However, multi-use paths can have high ridership and potential for safety issues (Ferster et al, 2021).…”

Section: Discussionmentioning

confidence: 99%

“…However, multi-use paths can have high ridership and potential for safety issues (Ferster et al, 2021). While people often perceive multi-use paths as low risk, it has been suggested that risks are much higher due to conflicts with other path users (Winters et al, 2012), and interaction with vehicles when multi-use paths cross the road network (Jestico et al, 2017). In our analysis, half of the incidents on multi-use paths involving vehicles require medical treatment, which suggests that both the frequency and magnitude of these incidents should be addressed if aiming to improve safety for bicyclists.…”

Section: Discussionmentioning

confidence: 99%

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Crowdsourced bicycling crashes and near misses: trends in Canadian cities

Laberee

Nelson

Branion-Calles

et al. 2021

Urban, Planning and Transport Research

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Safety concerns are a barrier to increasing bicycling. BikeMaps.org, a tool for crowdsourcing bicycling collisions, near misses, and falls, offers rich data on local bicycling safety concerns. Our goal is to characterize dominant bicycling safety issues reported in nine Canadian cities. We analyzed 2,513 BikeMaps.org reports (522 collisions, 151 falls, 1840 near misses), and summarized the types of incidents reported, ratios of near misses to collisions by incident type and by city, and injuries resulting from various types of crashes. Incidents categorized as a 'dangerous pass, overtake at midblock', were most commonly reported and had the highest ratio of near misses to collision reports (9:1). Cities with a high commute mode share for bicycling had lower near miss to collision reporting ratios. Overall, 40.3% of reported collisions or falls required medical treatment. Incident types with the most severe outcomes were 'left cross at an intersection' (58.4% reported needing medical treatment); 'vehicles failing to stop at intersection or yield to bike' (54.0%); and 'multi-use paths, vehicle conflicts at intersection' (48.5%). Mitigating conditions leading to real or perceived concerns over dangerous passes by vehicles should improve bicycling comfort. Bicycling injuries will be reduced by safety improvements at intersections including those with multi-use paths.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Crowdsourced bicycling crashes and near misses: trends in Canadian cities

Laberee

Nelson

Branion-Calles

et al. 2021

Urban, Planning and Transport Research

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“…Risks associated with features like multi-use paths, regional trails, and micro barriers (e.g. railroad tracks and loose surfaces) are more likely to emerge in analyses of crowdsourced data (Jestico, Nelson, Potter, & Winters, 2017). Other apps, similar to BikeMaps.org in functionality, have been developed but tend to be more regionally specific, like ORcycle (Blanc & Figliozzi, 2017).…”

Section: Safetymentioning

confidence: 99%

Crowdsourced data for bicycling research and practice

et al. 2020

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Cities are promoting bicycling for transportation as an antidote to increased traffic congestion, obesity and related health issues, and air pollution. However, both research and practice have been stalled by lack of data on bicycling volumes, safety, infrastructure, and public attitudes. New technologies such as GPS-enabled smartphones, crowdsourcing tools, and social media are changing the potential sources for bicycling data. However, many of the developments are coming from data science and it can be difficult evaluate the strengths and limitations of crowdsourced data. In this narrative review we provide an overview and critique of crowdsourced data that are being used to fill gaps and advance bicycling behaviour and safety knowledge. We assess crowdsourced data used to map ridership (fitness, bike share, and GPS/accelerometer data), assess safety (web-map tools), map infrastructure (OpenStreetMap), and track attitudes (social media). For each category of data, we discuss the challenges and opportunities they offer for researchers and practitioners. Fitness app data can be used to model spatial variation in bicycling ridership volumes, and GPS/accelerometer data offer new potential to characterise route choice and origin-destination of bicycling trips; however, working with these data requires a high level of training in data science. New sources of safety and near miss data can be used to address underreporting and increase predictive capacity but require grassroots promotion and are often best used when combined with official reports. Crowdsourced bicycling infrastructure data can be timely and facilitate comparisons across multiple cities; however, such data must be assessed for consistency in route type labels. Using social media, it is possible to track reactions to bicycle policy and infrastructure changes, yet linking attitudes expressed on social media platforms with broader populations is a challenge. New data present opportunities for improving our understanding of bicycling and supporting decision making towards transportation options that are healthy and safe for all. However, there are challenges, such as who has data access and how data crowdsourced tools are funded, protection of individual privacy, representativeness of data and impact of biased data on equity in ARTICLE HISTORY

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“…The researchers used NB regression to model the relationship between the number of incidents and the infrastructure features. A higher proportion of incidents reported at multiuse trail-road intersections were collisions (versus near misses), with a higher proportion of incidents that resulted in injuries (35% versus 21%) ( 21 ). The major contributing factors were cycling volumes, vehicle volumes, and trail sight distance.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The literature review found multiple applications that demonstrate the usefulness of both approaches of interest for this research. When large databases are available, the preference seems to be for disaggregating the data by crash severities and modeling the frequency of each subset of crashes separately ( 18 , 20 , 21 ). However, some researchers seem to prefer the development and use of SDFs even on larger datasets ( 2 , 3 , 16 ).…”

Section: Literature Reviewmentioning

confidence: 99%

A Comparative Analysis on Performance of Severe Crash Prediction Methods

Avelar

Dixon

Ashraf

2018

Transportation Research Record: Journal of the Transportation R

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The objective of this paper is to compare the performance and tradeoffs between two alternative analysis methods for developing crash prediction models for severe crashes: a direct estimation of severe crashes using frequency models, and an indirect but popular approach of combining frequency of total crashes models and some form of severity distribution functions (SDFs). The researchers conducted a comprehensive comparison of these modeling methods to illustrate the strengths and weaknesses of each alternative, and to inform future research that intends to develop such models. An examination of the theoretical characteristics of the modeling approach is presented and discussed. The performance of the two modeling alternatives is compared using two different datasets. The results of those comparisons showed very similar performances by both techniques. Finally, a sensitivity analysis is presented to explore how the performance of these techniques vary by degree of dispersion and observed correlation levels of total and severe injury crashes (KAB; injury scale in which K = fatal [killed], A = incapacitating injury, B = nonincapacitating injury) with potential explanatory variables. The results from these analyses tended to favor the use of SDFs in combination with total crashes safety performance functions (SPFs), as the prediction tended to show reduced dispersion under most conditions. However, performance of the KAB SPF model outperformed the combination of SDF and SPF for total crashes when KAB and non-KAB crashes had a common predictor but with effects in opposite directions.

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Multiuse trail intersection safety analysis: A crowdsourced data perspective

Cited by 16 publications

References 32 publications

Crowdsourced bicycling crashes and near misses: trends in Canadian cities

Crowdsourced bicycling crashes and near misses: trends in Canadian cities

Crowdsourced data for bicycling research and practice

A Comparative Analysis on Performance of Severe Crash Prediction Methods

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