Influence of Socioeconomic Conditions on Crash Injury Severity for an Urban Area in a Developing Country

Mesa-Arango, Rodrigo; Alaix, Víctor Gabriel Valencia; Pineda-Mendez, Raul; Eissa, Taleb

doi:10.1177/0361198118758684

Cited by 16 publications

(8 citation statements)

References 33 publications

(92 reference statements)

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“…So, considering the skewed distribution of these three variables, this cluster can be identified as motorcycle-involved crashes in densely populated areas on roadways with park lanes. Cluster 3 is overrepresented in terms of young pedestrians (15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), while other clusters have a more balanced distribution over this variable. Moreover, most crashes in this cluster occurred in places with no traffic control.…”

Section: Latent Class Clustering Resultsmentioning

confidence: 99%

“…For more than 30 years, considerable research has been conducted on pedestrian safety in developed countries. In contrast, in developing countries, the literature on vulnerable road users is at an early stage, and the number of studies in this field is limited [15,16]. Moreover, the focus of researchers and authorities is usually narrowed to motorized traffic instead of pedestrians in developing countries [15,17].…”

Section: Introductionmentioning

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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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: Latent Class Clustering Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

2022

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“…Statistical models that predict crash frequency and severity have not been commonly used in developing countries. For instance, there are two studies that estimated crash severity using the Medellin database, one by Mesa-Arango et al that used a multinomial logit model (MNL) and one by Lizarazo and Valencia that used a spatial macroscopic approach for studying pedestrian-car collisions [7,8].…”

Section: Literature Reviewmentioning

confidence: 99%

Estimation of Safety Performance Functions (SPF) at signalized intersections in Medellín Colombia

Alaix

Betancur

Jimenez³

et al. 2020

DYNA

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One of the objectives of road infrastructure sustainability is to ensure that users are treated equally and their quality of life is improved by providing better mobility and traffic safety. When designing roads, it is important to evaluate different design criteria alternatives - in this case, we look at traffic safety principles. For this, we used the Safety Performance Functions (SPF) tool to obtain the expected crash frequency. The data used were Medellín’s crash records from 2012 to 2016, as well as the geometric features and traffic conditions at signalized intersections. A negative binomial model was fitted to estimate the SPF. Exposure, geometry, and traffic volume were found to be statistically significant in determining the expected crash frequency for collisions where there was property damage only (PDO) and fatal or injury (FI). It was found that accidents were less likely on T-junctions compared to four-leg junctions, one-way approaches were found to be safer whereas right turns were found to increase collisions causing FI.

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“…The shares of the three categories are quite unbalanced—property damage and injury crash records account for 60.87% and 38.66% of the whole dataset, respectively, and fatal crash records only account for 0.47%. Although the small share of fatal crashes may lower the model performance, it was decided not to combine fatal crashes into injury crashes because (1) most crash severity scales separate fatal crashes from others ( 2 , 7 , 24 ); (2) a low fatal crash share is a common feature of crash datasets ( 2 , 16 , 25 ) and the authors want to reflect this feature in the models; and (3) the authors want to identify contributing factors of fatal crashes to prevent future fatal crashes.…”

Section: Data Preparationmentioning

confidence: 99%

“…In crash severity analysis, the dependent variable is discrete, with multiple categories. Many studies ( 1 , 2 ) have used three crash severity levels: property damage only (PDO), injury, and fatality. Crash severity levels can be considered as either nominal or ordinal variables and thus different types of modeling techniques can be applied ( 3 ).…”

mentioning

confidence: 99%

Prediction and Factor Identification for Crash Severity: Comparison of Discrete Choice and Tree-Based Models

Wang

Kim

2019

Transportation Research Record

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Crash severity is one of the most widely studied topics in traffic safety area. Scholars have studied crash severity through various types of models. Using the publicly available 2017 Maryland crash data from the Department of Maryland State Police, the authors develop a multinomial logit (MNL) model and a random forest (RF) model, which belong to discrete choice and tree-based models, respectively, to (1) identify factors contributing to crash severity and (2) compare prediction performances and interpretation abilities between the two models. Based on the model results, major contributing factors of crash severity are identified, including collision type, occupant age, and speed limit. For the given dataset, RF has a higher prediction accuracy than MNL based on multiple measures (precision, recall, and F1 score), even though the differences are not dramatic. Sensitivity analysis results show that RF is less sensitive than MNL. RF can automatically capture the non-linear effects of continuous variables and reduce the influence of collinearity relationships existing among explanatory variables. This study shows the possibility of conducting sensitivity analysis to enhance understanding of MNL and RF results, and uncovers unique characteristics of the discrete choice and tree-based models.

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Influence of Socioeconomic Conditions on Crash Injury Severity for an Urban Area in a Developing Country

Cited by 16 publications

References 33 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

Estimation of Safety Performance Functions (SPF) at signalized intersections in Medellín Colombia

Prediction and Factor Identification for Crash Severity: Comparison of Discrete Choice and Tree-Based Models

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