Infrastructure and spatial effects on the frequency of cyclist-motorist collisions in the Copenhagen Region

Prato, Carlo Giacomo; Kaplan, Sigal; Rasmussen, Thomas Kjær; Hels, Tove

doi:10.1080/19439962.2015.1055414

Cited by 25 publications

(14 citation statements)

References 47 publications

(53 reference statements)

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“…Prato et al (2014) found a statistically significant positive coefficient for heavy vehicle volume using bicycle accidents as dependent variable. When a heavy vehicle is involved, injuries to cyclists and pedestrians are likely to be more severe than when it is not involved.…”

Section: Discussionmentioning

confidence: 87%

Safety-in-numbers: Estimates based on a sample of pedestrian crossings in Norway

Elvik

2016

Accident Analysis & Prevention

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Safety-in-numbers denotes the tendency for the risk of accident for each road user to decline as the number of road users increases. Safety-in-numbers implies that a doubling of the number of road users will be associated with less than a doubling of the number of accidents. This paper investigates safety-in-numbers in 239 pedestrian crossings in Oslo and its suburbs. Accident prediction models were fitted by means of negative binomial regression. The models indicate a very strong safety-in-numbers effect. In the final model, the coefficients for traffic volume were 0.05 for motor vehicles, 0.07 for pedestrians and 0.12 for cyclists. The coefficient for motor vehicles implies that the number of accidents is almost independent of the number of motor vehicles. The safety-in-numbers effect found in this paper is stronger than reported in any other study dealing with safety-in-numbers. It should be noted that the model explained only 21% of the systematic variation in the number of accidents. It therefore cannot be ruled out that the results are influenced by omitted variable bias. Any such bias would, however, have to be very large to eliminate the safety-in-numbers effect.

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Section: Discussionmentioning

confidence: 87%

Safety-in-numbers: Estimates based on a sample of pedestrian crossings in Norway

Elvik

2016

Accident Analysis & Prevention

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“…Of the macrolevel research studies that have focused on cyclist safety, some have studied the bike infrastructure (7-10), whereas others have studied various zonal characteristics, such as the built environment, land use, and the road facility (10)(11)(12)(13)(14). Although these studies have been useful in investigating factors affecting cyclist safety, almost all the studies used proxies for bike exposure, such as the number of bike lane kilometers (12), the modeled bike mode share (13), and the length of roads (11).…”

mentioning

confidence: 99%

Evaluating the Impact of Socioeconomics, Land Use, Built Environment, and Road Facility on Cyclist Safety

Osama

Sayed

2017

Transportation Research Record

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With the increasing demand for sustainability, the use of cycling as an efficient active mode of transportation is being encouraged. However, the vulnerability of cyclists to severe injuries in crashes can discourage road users from cycling. Therefore, the study of the factors that affect the safety of cyclists is important. This paper describes an investigation of the relationship between cyclist–motorist crashes and various traffic zone characteristics in Vancouver, British Columbia, Canada. The goal was to assess the impacts of socioeconomics, land use, the built environment, and the road facility on cyclist safety through the use of macrolevel collision prediction models. The models were developed by generalized linear regression and full Bayesian techniques. An actual bike exposure indicator (the number of bike kilometers traveled) and the number of vehicle kilometers traveled were used as exposure variables in the models. The safety models showed that cyclist–motorist crashes were nonlinearly associated with an increase in bike, vehicle, and transit traffic as well as socioeconomic variables (i.e., population, employment, and household densities), variables related to the built environment (transit stop, traffic signal, and light pole densities), commercial area density, and the proportion if arterial–collector roads. The models revealed, however, a decline in cyclist–motorist crashes in association with an increase in the proportions of local roads and off-street bike links and an increase in recreational and residential area densities. The spatial effects were accounted for in the full Bayes models and were found to be significant; such a finding implies the importance of consideration of the spatial correlation in the development of macrolevel cyclist safety models.

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“…Bicycle-vehicle accidents in intersections involve various factors related to road users and the built environment [46]. For the purposes of this study, we collected candidate risk factors in traffic and environmental dimensions based on the literature [11][12][13][14][15][16].…”

Section: Candidate Risk Factorsmentioning

confidence: 99%

“…Researchers have identified various risk factors in this context, among which exposure is known to be most important [11,12]. Other risks can mainly be divided into three dimensions: infrastructural, traffic-related, and environmental.…”

Section: Introductionmentioning

confidence: 99%

Bicycle-Vehicle Conflict Risk Based on Cyclist Perceptions: Misestimations of Various Risk Factors

Chen

2020

Sustainability

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Cycling is a sustainable but vulnerable mode of transportation. Intersections’ bicycle-vehicle crashes are particularly dangerous. This paper explores the discordance between empirical evidence and cyclists’ perceptions of the various risk factors of cycling. Ridge regression was adopted to identify risk factors from bicycle-vehicle conflict data. A questionnaire was distributed to assess cyclists’ perceptions of safety and danger over the same candidate risk factors. There was indeed discordance between the data and the questionnaire results. Cyclists appear to misestimate risk in certain factors such as bus stops and subway stations. Understanding these misestimations can provide a foundation for safety improvements and for promoting cycling as a sustainable mode of transportation.

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Infrastructure and spatial effects on the frequency of cyclist-motorist collisions in the Copenhagen Region

Cited by 25 publications

References 47 publications

Safety-in-numbers: Estimates based on a sample of pedestrian crossings in Norway

Safety-in-numbers: Estimates based on a sample of pedestrian crossings in Norway

Evaluating the Impact of Socioeconomics, Land Use, Built Environment, and Road Facility on Cyclist Safety

Bicycle-Vehicle Conflict Risk Based on Cyclist Perceptions: Misestimations of Various Risk Factors

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