How much space do drivers provide when passing cyclists? Understanding the impact of motor vehicle and infrastructure characteristics on passing distance

Beck, Ben; Chong, Derek; Olivier, Jake; Perkins, Monica; Tsay, Anthony; Rushford, Adam; Li, Lingxiao; Cameron, Peter; Fry, Richard; Johnson, Marilyn

doi:10.1016/j.aap.2019.03.007

Cited by 62 publications

(61 citation statements)

References 26 publications

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“…"Dooring" injuries were found to be infrequent for cyclists in the present cohort, which is consistent with previous studies in Australia [12,38]. However, we recognise that cyclists may avoid dooring injuries by riding closer to traffic on roads with parked cars than they do on roads without parked cars [39]. Road safety may be enhanced with traffic calming strategies to reduce traffic volume and speed near intersections and collision hotspots for road users faced with turning or merging traffic, especially cyclists [34].…”

Section: Implications For Road Safetysupporting

confidence: 92%

Classification of road traffic injury collision characteristics using text mining analysis: Implications for road injury prevention

2021

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Road traffic injuries are a leading cause of morbidity and mortality globally. Understanding circumstances leading to road traffic injury is crucial to improve road safety, and implement countermeasures to reduce the incidence and severity of road trauma. We aimed to characterise crash characteristics of road traffic collisions in Victoria, Australia, and to examine the relationship between crash characteristics and fault attribution. Data were extracted from the Victorian State Trauma Registry for motor vehicle drivers, motorcyclists, pedal cyclists and pedestrians with a no-fault compensation claim, aged > = 16 years and injured 2010–2016. People with intentional injury, serious head injury, no compensation claim/missing injury event description or who died < = 12-months post-injury were excluded, resulting in a sample of 2,486. Text mining of the injury event using QDA Miner and Wordstat was used to classify crash circumstances for each road user group. Crashes in which no other was at fault included circumstances involving lost control or avoiding a hazard, mechanical failure or medical conditions. Collisions in which another was predominantly at fault occurred at intersections with another vehicle entering from an adjacent direction, and head-on collisions. Crashes with higher prevalence of unknown fault included multi-vehicle collisions, pedal cyclists injured in rear-end collisions, and pedestrians hit while crossing the road or navigating slow traffic areas. We discuss several methods to promote road safety and to reduce the incidence and severity of road traffic injuries. Our recommendations take into consideration the incidence and impact of road trauma for different types of road users, and include engineering and infrastructure controls through to interventions targeting or accommodating human behaviour.

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Section: Implications For Road Safetysupporting

confidence: 92%

Classification of road traffic injury collision characteristics using text mining analysis: Implications for road injury prevention

2021

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“…Therefore, it would be preferable to place less emphasis on the development of bike lanes and shared bike lanes. It has also been widely shown that the risks of accidents involving cyclists are greater on these types of bike lanes because cyclists share the road with motor vehicles [48,49]. Therefore, cyclists' exposure to noise is an important issue in Montreal and Paris that might discourage bicycle use.…”

Section: Implications For Policymentioning

confidence: 99%

Cyclists′ Exposure to Road Traffic Noise: A Comparison of Three North American and European Cities

Apparicio

Gelb

2020

Acoustics

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According to the World Health Organization, high levels of exposure to road traffic noise are associated with adverse health effects. Earlier studies suggest that cyclists are exposed to higher noise levels than motorists. Other studies have demonstrated that cyclists' exposure to noise could vary significantly according to their routes. The aim of this study is to compare cyclists' exposure to noise and their determinants in three cities. Three participants cycled equipped with noise dosimeters and GPS watches: 1823, 967, and 1362 km in Copenhagen, Paris, and Montreal, respectively. We fitted three generalized additive mixed model with an autoregressive term models to predict the cyclists' exposure to noise according to the type of route and bicycle infrastructure after controlling for the day of the week, as well as spatial and temporal trends. The overall noise means were 73.4, 70.7, and 68.4 dB(A) in Paris, Montreal, and Copenhagen, respectively. The exposure to road traffic noise is strongly associated with the type of bicycle infrastructure taken by the cyclist; riding on a cycleway significantly decreases it, while riding in a shared lane has no impact. Our findings demonstrate that it is possible to achieve a substantial reduction in cyclists exposure by adopting new practices that include noise exposure in the planning of future cycling infrastructure.

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“…Cyclist overtaking maneuvers can be split into four phases [6], [27], [30], which may help to prioritize and develop safety systems that support the driver in reducing crash risks in those phases: 1) approaching phase: when the ego vehicle approaches the cyclist, and the driver has to decide between a flying or an accelerative maneuver, 2) steering away phase: when the driver steers away to achieve a lateral distance to the cyclist, 3) passing phase: when the ego vehicle passes the cyclist while driving in parallel, 4) returning phase: when the driver has passed the cyclist and steers back into the lane. Several recent studies investigated driver behavior in these phases by leveraging different test environments: simulator studies [31], [32], test-track studies [30], [33], field test studies [27] and naturalistic driving studies [6], [34]- [36]. While simulator and test-track studies have lower ecological validity than field tests or naturalistic driving studies, they can offer more controlled data with higher resolution, which facilitates the development of predictive driver models [5].…”

Section: Existing Research On Driver-cyclist Interaction In Overtamentioning

confidence: 99%

Modeling Drivers’ Strategy When Overtaking Cyclists in the Presence of Oncoming Traffic

Rasch

Dozza

2022

IEEE Trans. Intell. Transport. Syst.

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How much space do drivers provide when passing cyclists? Understanding the impact of motor vehicle and infrastructure characteristics on passing distance

Abstract: How much space do drivers provide when passing cyclists? Understanding the impact of motor vehicle and infrastructure characteristics on passing distance. Accident Analysis & Prevention

Cited by 62 publications

References 26 publications

Classification of road traffic injury collision characteristics using text mining analysis: Implications for road injury prevention

Classification of road traffic injury collision characteristics using text mining analysis: Implications for road injury prevention

Cyclists′ Exposure to Road Traffic Noise: A Comparison of Three North American and European Cities

Modeling Drivers’ Strategy When Overtaking Cyclists in the Presence of Oncoming Traffic

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