Analysis of Roadside Accident Severity on Rural and Urban Roadways

Fan, Wei; Cai, Zhenggan; Guo, Yongqing; Liu, Pan; Wang, Zhenyu; Li, Zhibin

doi:10.32604/iasc.2021.014661

Cited by 7 publications

(8 citation statements)

References 49 publications

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“…In addition, there is a significant positive correlation between FS injuries and the time of 21:00-7:00 (21:00-24:00 and 0:00-7:00), which is consistent with previous research [15].…”

Section: Other Characteristicssupporting

confidence: 92%

“…In the whole-dataset, cluster 3, and cluster 4, the probability of FS injuries caused by a motorcycle increased by 12.8%, 23.8%, and 12.6%, respectively. Wei and Cai [15] obtained a similar finding and pointed out that motorcycles cannot provide superior protection for riders, and high mortality is inevitable. Meanwhile, judgment related to the driving environment is influenced by low-visibility conditions, which increase the probability of serious crashes [75].…”

Section: Vehicle Characteristicsmentioning

confidence: 70%

“…This phenomenon can be confirmed by the characteristic variables of cluster 1. As is known, the use of a seatbelt/helmet can provide superior protection for drivers, which will significantly reduce the probability of a serious crash [15]. Moreover, the percentage of FS injuries in cluster 3 is 41.4%, which is significantly higher than that in other clusters.…”

Section: Analysis Of Latent Class Clustering Modelmentioning

confidence: 94%

“…As visibility and weather are estimated by traffic police, some errors may exist; thus a real-time meteorological dataset was collected from the Shandong Climate Center to calibrate row data. Crash data related to low visibility-less than 100 m-was extracted because the rate of fatal crashes in rural areas greatly increases when visibility is less than 100 m [15]. After excluding abnormal data, 19,187 LVR-SV crashes were sorted.…”

Section: Datamentioning

confidence: 99%

“…Visibility conditions were divided into four categories-greater than 200 m, 100 -200 m, 50 -100 m, and less than 50 m-to evaluate the safety performance of rural areas. It was found that the probability of fatal crashes is substantially increased in visibility less than 100 m [15].…”

Section: Introductionmentioning

confidence: 99%

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Modeling of Low Visibility-Related Rural Single-Vehicle Crashes Considering Unobserved Heterogeneity and Spatial Correlation

et al. 2021

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Accident analysis and prevention are helpful to ensure the sustainable development of transportation. The aim of this research was to investigate the factors associated with the severity of low-visibility-related rural single-vehicle crashes. Firstly, a latent class clustering model was implemented to partition the whole-dataset into a relatively homogeneous sub-dataset. Then, a spatial random parameters logit model was established for each dataset to capture unobserved heterogeneity and spatial correlation. Analysis was conducted based on the crash data (2014–2019) from 110 two-lane road segments. The results show that the proposed method is a superior crash severity modeling approach to accommodate the unobserved heterogeneity and spatial correlation. Three variables—seatbelt not used, motorcycle, and collision with fixed object—have a stable positive correlation with crash severity. Motorcycle leads to a 12.8%, 23.8%, and 12.6% increase in the risk of serious crashes in the whole-dataset, cluster 3, and cluster 4, respectively. In the whole-dataset, cluster 2, and cluster 3, the risk of serious crashes caused by seatbelt not used increased by 5.5%, 0.1%, and 30.6%, respectively, and caused by collision with fixed object increased by 33.2%, 1.2%, and 13.2%, respectively. The results can provide valuable information for engineers and policy makers to develop targeted measures.

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“…In addition, there is a significant positive correlation between FS injuries and the time of 21:00-7:00 (21:00-24:00 and 0:00-7:00), which is consistent with previous research [15].…”

Section: Other Characteristicssupporting

confidence: 92%

Section: Vehicle Characteristicsmentioning

confidence: 70%

Section: Analysis Of Latent Class Clustering Modelmentioning

confidence: 94%

Section: Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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