Applying Bayesian hierarchical models to examine motorcycle crashes at signalized intersections (original) (raw)

Abstract

Motorcycles are overrepresented in road traffic crashes and particularly vulnerable at signalized intersections. The objective of this study is to identify causal factors affecting the motorcycle crashes at both four-legged and T signalized intersections. Treating the data in time-series cross-section panels, this study explores different Hierarchical Poisson models and found that the model allowing autoregressive lag 1 dependent specification in the error term is the most suitable. Results show that the number of lanes at the four-legged signalized intersections significantly increases motorcycle crashes largely because of the higher exposure resulting from higher motorcycle accumulation at the stop line. Furthermore, the presence of a wide median and an uncontrolled left-turn lane at major roadways of four-legged intersections exacerbate this potential hazard. For T signalized intersections, the presence of exclusive right-turn lane at both major and minor roadways and an uncontrolled leftturn lane at major roadways of T intersections increases motorcycle crashes. Motorcycle crashes increase on high-speed roadways because they are more vulnerable and less likely to react in time during conflicts. The presence of red light cameras reduces motorcycle crashes significantly for both four-legged and T intersections. With the red-light camera, motorcycles are less exposed to conflicts because it is observed that they are more disciplined in queuing at the stop line and less likely to jump start at the start of green.

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What key factors contribute to motorcycle crash frequency at signalized intersections?add

The study identifies significant factors such as number of lanes, presence of left-turn lanes, and speed limits that influence motorcycle crash occurrences. Specifically, additional lanes can increase crashes by 13% on major roads and 19% on minor roads.

How does the presence of red light cameras affect motorcycle safety?add

The inclusion of red light cameras at major roadways is associated with a 37% reduction in motorcycle crashes. This effect is attributed to improved compliance with traffic signals, leading to less exposure of motorcyclists.

What role does traffic volume play in motorcycle accident risk?add

Higher traffic volumes on major roadways correlate with an increase in motorcycle crashes, with an incidence rate ratio of 1.024 for each unit increase in traffic volume. This suggests that increased interactions among vehicles heighten crash risks, especially for motorcycles.

How do hierarchical models improve analysis of motorcycle crash data?add

Hierarchical models account for unobserved heterogeneities and serial correlations in collision data, yielding more accurate estimates. In this study, the Hierarchical Poisson (AR-1) model outperformed traditional count models based on DIC and Predictive Loss Criteria.

When are motorcycles most vulnerable at signalized intersections?add

Motorcycles are particularly vulnerable during the early green phase when they often accumulate at stop lines, increasing exposure to red-light running vehicles. The research indicates a significant over-representation of motorcycle crashes during these moments.

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