Accounting for mediation in cyclist-vehicle crash models: A Bayesian mediation analysis approach

Kamel, Mohamed Bayoumi; Sayed, Tarek; Osama, Ahmed

doi:10.1016/j.aap.2019.06.009

Cited by 28 publications

(8 citation statements)

References 47 publications

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“…The same data set was used previously to demonstrate the importance of accounting for mediation in safety models and to develop a comprehensive zone-based index to represent both biking attractiveness and bike crash risk (Kamel et al., 2019, 2020), modeling Bike Kilometers Traveled as a function of network characteristics (Osama et al., 2017), and accounting for measurement error in traffic exposure variables (e.g. BKT) in bike–vehicle crash modeling (Kamel and Sayed, 2020).…”

Section: Data Collectionmentioning

confidence: 99%

The impact of bike network indicators on bike kilometers traveled and bike safety: A network theory approach

Kamel

Sayed

2020

Environment and Planning B: Urban Analytics and City Science

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There has been recent interest in the use of network analysis to quantify bike network features and their impact on biking levels and safety. However, limited bike network indicators have been evaluated. This study introduces a list of network indicators to quantify the bike network and study its effect on bike kilometers traveled and bike–vehicle crashes. Data from the city of Vancouver, Canada, are used as a case study. Full Bayesian modeling incorporating spatial effects is employed to develop Bike Kilometers Travelled (BKT) and bike–vehicle crash models. The developed BKT models show that the bike network centrality, assortativity, and weighted slope have negative associations with BKT, while the bike network directness, length, complexity and development, and connectivity have positive associations with BKT. The developed crash models show that the bike network length, centrality, assortativity, and continuity have negative associations with bike–vehicle crashes. On the other hand, the bike network complexity and development, connectivity, and linearity have positive associations with bike–vehicle crashes. The models provide insights that can be useful for planning bike networks to increase bike traffic and improve bike safety. The models also show that some changes to a bike network to increase bike traffic should be accompanied by crash risk-mitigating measures. As well, the models can be used to identify zones within a city that require safety improvements.

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Section: Data Collectionmentioning

confidence: 99%

The impact of bike network indicators on bike kilometers traveled and bike safety: A network theory approach

Kamel

Sayed

2020

Environment and Planning B: Urban Analytics and City Science

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“…Buses interfere with cycling routes when they stop as well, and in certain cases force passengers to cross over the bicycle lane when deboarding. The number of bus stops is indeed an important risk factor for cycling [13,15,17,60,61]. In our investigated intersections, either few buses pass by or the bus transit lane is separate; it is possible that buses have relatively little influence on cycling safety in these cases.…”

Section: Discussionmentioning

confidence: 89%

“…Vehicle traffic, pedestrian traffic, and commercial land density are the most important risk factors for cycling as reflected in both accident data [13,17,60,64] and cyclists' self-reported perceptions [19,25]. Bicycle traffic, interestingly, is not significantly related to bicycle-vehicle conflict probability.…”

Section: Discussionmentioning

confidence: 94%

“…Employment, number of schools, subway stations, land use mixtures, commercial retail properties, and the presence and proximity of bicycle facilities have positive effects on risk while average street length and the presence of parking entrances have negative effects [14][15][16]. Network coverage and recreational density have negative, direct effects on cycling accidents while their total, indirect effect is positive [17].…”

Section: Introductionmentioning

confidence: 99%

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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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“…In traffic accident safety analysis, BN is widely used to analyze the causes of maritime traffic accidents and road traffic accidents [32][33][34]. In algorithm solving, the genetic algorithm is introduced into BN's incremental learning, which alleviates the local extremum problem in the searching process [35]. A loop deletion algorithm considering KL spacing is also used to learn the structure of BN, eliminating the dependency on node order in the modeling process [36].…”

Section: Introductionmentioning

confidence: 99%

Critical Factors Analysis of Severe Traffic Accidents Based on Bayesian Network in China

Chen

Yang

2020

Journal of Advanced Transportation

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The purpose of this study is to minimize the negative influences of the severe traffic accidents in China by profoundly analyzing the complex coupling relations among accident factors contributing to the single-vehicle and multivehicle traffic accidents with the Bayesian network (BN) crash severity model. The BN model was established by taking the critical factors identified with the improved grey correlation analysis method as node variables. The severe traffic accident data collected from accident reports published in China were used to validate this model. The model’s efficiency was validated objectively by comparing the conditional probability obtained by this model with the actual value. The result shows that the BN model can reflect the real relations among factors and can be seen as the target network for the severe traffic accidents in China. Besides, based on BN’s junction tree engine, five-factor combination sequences for the number of deaths and three-factor combination sequences for the number of injuries were ranked according to the severity degree to reveal the critical reasons and reduce the massive traffic accidents damage.

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Accounting for mediation in cyclist-vehicle crash models: A Bayesian mediation analysis approach

Cited by 28 publications

References 47 publications

The impact of bike network indicators on bike kilometers traveled and bike safety: A network theory approach

The impact of bike network indicators on bike kilometers traveled and bike safety: A network theory approach

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

Critical Factors Analysis of Severe Traffic Accidents Based on Bayesian Network in China

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