Exposure-Based Crash Rates for Bicycle and Motorized Transport in Central Lane Metropolitan Planning Organization

Roll, Josh

doi:10.1016/j.jth.2016.05.066

Cited by 2 publications

(5 citation statements)

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“…Kothuri et al (2022) had similar findings. Roll (2018) found that their models with Strava bicycle trip count perform better in terms of R 2 . Kwigizile et al (2019) applied both statistical and machine learning models to construct models with and without Strava bicycle trip counts.…”

Section: The Role Of Crowdsourced Variablesmentioning

confidence: 96%

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The use of crowdsourced mobile data in estimating pedestrian and bicycle traffic: A systematic review

Tao,

Lindsey,

Stern

et al. 2024

JTLU

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To address the need for better non-motorized traffic data, policymakers and researchers are collaborating to develop new approaches and methods for estimating pedestrian and bicyclist traffic volumes. Crowdsourced mobile data, which has higher spatial and temporal coverage and lower collection costs than data collected through traditional approaches, may help improve pedestrian and bicyclist traffic estimation despite their limitations or biases. This systemic literature review documents how researchers have used crowdsourced mobile data to estimate pedestrian and bicyclist traffic volumes. We find that one source of commercial fitness application data (i.e., Strava) has been used much more frequently than other crowdsourced mobile data, and that most studies have used crowdsourced mobile data to estimate bicyclist volumes. Comparatively few studies have estimated pedestrian volumes. The most common approach to the use of crowdsourced counts is as independent variables in direct demand models. Variables constructed from crowdsourced mobile data not only have significant correlations with observed counts in statistical models but also have larger relative importance than other factors in machine learning models. Studies also show that including crowdsourced mobile data can significantly improve estimation performance. Future research directions include application of crowdsourced mobile data in more pedestrian traffic estimations, comparison of the performance of different crowdsourced mobile data, incorporation of multiple data sources, and expansion of the methods using crowdsourced mobile data for non-motorized traffic estimation.

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Section: The Role Of Crowdsourced Variablesmentioning

confidence: 96%

“…One possible explanation is the relatively small sample size but large variance of the observed bicyclist counts. In addition, Proulx (2016), Roll (2018), and Munira (2021) did not provide information of significance level.…”

Section: The Role Of Crowdsourced Variablesmentioning

confidence: 99%

The use of crowdsourced mobile data in estimating pedestrian and bicycle traffic: A systematic review

Tao,

Lindsey,

Stern

et al. 2024

JTLU

View full text Add to dashboard Cite

show abstract

“…A study in Miami found that Strava sampling rates were higher on streets than trails (26), while another found areas with more bike lanes to have lower sampling rates (25). However, these third-party data sources, of which most have focused on Strava thus far, have usually improved bicycle volume and safety performance models significantly, after controlling for built environment and sociodemographic factors (1,(28)(29)(30)(31)(32)(33)(34). Despite consistent findings of association, a few details are worth noting: Correlation does not necessarily track sampling ratio.…”

Section: Data Sources For Bicycle Volumesmentioning

confidence: 99%

“…Several studies have combined environmental data with counts from emerging sources to improve bicycle volume estimates. These have included factoring in roadway characteristics such as functional classification ( 28 , 30 ), speed limit, on-street parking presence, and slope ( 27 ), sociodemographic factors such as income ( 28 ), as well as seasonal adjustment factors ( 27 ). Proulx and Pozdnukhov ( 11 ) included bicycle volume estimates from travel demand models, as well as bike share usage.…”

Section: Introductionmentioning

confidence: 99%

“…Proulx and Pozdnukhov ( 11 ) included bicycle volume estimates from travel demand models, as well as bike share usage. Roll ( 30 ) also included bicycle facility types, local accessibility and design measures, and a measure of network centrality. Sanders et al ( 32 ) included the number of bike lanes and proximity to the university alongside Strava count data.…”

Section: Introductionmentioning

confidence: 99%

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Evaluating the Potential of Crowdsourced Data to Estimate Network-Wide Bicycle Volumes

Broach

Kothuri

Miah

et al. 2023

Transportation Research Record: Journal of the Transportation R

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This research integrated and evaluated emerging user data sources (Strava Metro, StreetLight, and hybrid docked/dockless bike share) of bicycle activity data with conventional “static” demand determinants (land use, built environment, sociodemographics) and measures (permanent and short-duration counts) to estimate annual average daily bicycle traffic (AADBT). We selected six locations (Boulder, Charlotte, Dallas, Portland, Bend, and Eugene) covering varied urban and suburban contexts and specified three sets of Poisson regression models: city-specific models, an Oregon pooled model, and all cities pooled. Static variables, Strava, and StreetLight complemented one another, with each additional data source tending to improve the model performance. Sites with lower volumes were more difficult to predict, with considerable error in even the best-performing models. City-specific models in general exhibited improved fit and prediction performance. Expected prediction error increased by a factor of about 1.4 when using Strava or StreetLight alone, but without static adjustment variables, to predict AADBT. Combining Strava plus StreetLight, but without static variables, increased error slightly less; by 1.3 times. We also found that transferring the model specifications from one year to the next without re-estimating the model parameters resulted in a 10% to 50% increase in error rates across models, so such transfer is not recommended. The findings from this study indicate that rather than replacing conventional bike data sources and count programs, old “small” data sources will likely be very important for big data sources like Strava and StreetLight to achieve their potential for predicting AADBT.

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Exposure-Based Crash Rates for Bicycle and Motorized Transport in Central Lane Metropolitan Planning Organization

Cited by 2 publications

References 0 publications

The use of crowdsourced mobile data in estimating pedestrian and bicycle traffic: A systematic review

The use of crowdsourced mobile data in estimating pedestrian and bicycle traffic: A systematic review

Evaluating the Potential of Crowdsourced Data to Estimate Network-Wide Bicycle Volumes

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