A Hierarchical Modeling Approach to Predict Pedestrian Crash Severity

Jahangeer, Aafreen Asma; Anjana, S.; Das, Vivek R.

doi:10.1007/978-981-32-9042-6_28

Cited by 6 publications

(4 citation statements)

References 8 publications

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“…The severity of pedestrian injuries when the driver leaves the crash scene (hit and run) was investigated in the present study. In correspondence with previous studies, the results confirmed that injuries are more severe when the crash is hit and run [31,40,97]. As presented in Table A1, crashes where drivers leave the crash scene without reporting tend to have more likelihood of major injuries in the overall sample, and clusters 1-4 by 4.31%, 1.36%, 2.04%, 3.81%, and 2.63%, respectively.…”

Section: Involved Party Characteristicssupporting

confidence: 89%

“…It was found in previous literature that higher volume can increase the frequency and risk of pedestrianvehicle crashes [22,120]. However, for injury severity, opposing results were reported by previous studies, and the effect of higher traffic volumes was found to both aggravate [121] and alleviate [97] pedestrian injury severities.…”

Section: Roadway and Built-environment Characteristicsmentioning

confidence: 80%

“…Furthermore, hit and run crashes have a higher probability of fatal injuries in the overall sample and clusters 2-4 by 0.48%, 0.32%, 0.20%, and 0.21%, respectively. The reason could be that, in these types of crashes, the muchneeded crucial medical attention can be delayed [97]. This variable has a random effect through observations specific to major injuries in the overall sample and clusters 1 and 2.…”

Section: Involved Party Characteristicsmentioning

confidence: 99%

See 2 more Smart Citations

Latent Class Cluster Analysis and Mixed Logit Model to Investigate Pedestrian Crash Injury Severity

2022

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Traffic crashes involving pedestrians have a high frequency in developing countries. Among road users, pedestrians are the most vulnerable, as their involvement in traffic crashes is usually followed by severe and fatal injuries. This study aims to identify pedestrian crash patterns and reveal the random parameters in the dataset. A three-year (2015–2017) pedestrian crash dataset in Mashhad, Iran, was employed to investigate the influence of a rich set of factors on pedestrian injury severity, some of which have been less accounted for in previous studies (e.g., the vicinity to overpasses, the existence of vegetated buffers, and park lanes). A two-step method integrating latent class cluster analysis (LCA) and the mixed logit model was utilized to consider unobserved heterogeneity. The results demonstrated that various factors related to the pedestrian, vehicle, temporal, environmental, roadway, and built-environment characteristics are associated with pedestrian injuries. Furthermore, it was found that integrated use of LCA and mixed logit models can considerably reduce the unobserved heterogeneity and uncover the hidden effects influencing severity outcomes, leading to a more profound perception of pedestrian crash causation. The findings of this research can act as a helpful resource for implementing effective strategies by policymakers to reduce pedestrian casualties.

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Section: Involved Party Characteristicssupporting

confidence: 89%

Section: Roadway and Built-environment Characteristicsmentioning

confidence: 80%

Section: Involved Party Characteristicsmentioning

confidence: 99%

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Latent Class Cluster Analysis and Mixed Logit Model to Investigate Pedestrian Crash Injury Severity

2022

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“…Different risk factors (such as demographics, crash, and built environment characteristics) that affect pedestrian crash injury severity and casualty have been identified by several studies [ 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 ]. From the demographic perspective, males and the elderly are found to be more prone to incur serious injuries and fatalities than other age groups when involved in pedestrian crashes [ 16 , 17 , 29 , 30 ]. Other studies have concluded that more severe pedestrian injuries and fatalities are associated with temporal factors, such as time of day, day of the week, and season [ 15 , 19 , 31 , 32 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Investigating the Risk Factors Associated with Injury Severity in Pedestrian Crashes in Santiago, Chile

Rampinelli

Calderón

Blázquez

et al. 2022

IJERPH

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Pedestrians are vulnerable road users that are directly exposed to road traffic crashes with high odds of resulting in serious injuries and fatalities. Therefore, there is a critical need to identify the risk factors associated with injury severity in pedestrian crashes to promote safe and friendly walking environments for pedestrians. This study investigates the risk factors related to pedestrian, crash, and built environment characteristics that contribute to different injury severity levels in pedestrian crashes in Santiago, Chile from a spatial and statistical perspective. First, a GIS kernel density technique was used to identify spatial clusters with high concentrations of pedestrian crash fatalities and severe injuries. Subsequently, partial proportional odds models were developed using the crash dataset for the whole city and the identified spatial clusters to examine and compare the risk factors that significantly affect pedestrian crash injury severity. The model results reveal higher increases in the fatality probability within the spatial clusters for statistically significant contributing factors related to drunk driving, traffic signage disobedience, and imprudence of the pedestrian. The findings may be utilized in the development and implementation of effective public policies and preventive measures to help improve pedestrian safety in Santiago.

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How effective are pedestrian crash prevention systems in improving pedestrian safety? Harnessing large-scale experimental data

Mahdinia

Khattak

Haque

2022

Accident Analysis & Prevention

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A Hierarchical Modeling Approach to Predict Pedestrian Crash Severity

Cited by 6 publications

References 8 publications

Latent Class Cluster Analysis and Mixed Logit Model to Investigate Pedestrian Crash Injury Severity

Latent Class Cluster Analysis and Mixed Logit Model to Investigate Pedestrian Crash Injury Severity

Investigating the Risk Factors Associated with Injury Severity in Pedestrian Crashes in Santiago, Chile

How effective are pedestrian crash prevention systems in improving pedestrian safety? Harnessing large-scale experimental data

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