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

Abstract

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.

Impact of Driver’s Age and Gender, Built Environment, and Road Conditions on Crash Severity: A Logit Modeling Approach

“…They suggest that over the middle age range, crash probabilities do not change much and hence the literature is correct in focusing on the young and the old, for which severe crash probability differs. Further, the literature suggests that male drivers are at a greater risk of BI accidents and women are more involved in non-fatal crashes [49,50]. This is confirmed by this study.…”

A Clustering-Based Approach to Detecting Critical Traffic Road Segments in Urban Areas

“…Clustering Approach [1] traffic load analysis improved k-means clustering algorithm [2] traffic congestion analysis self-organizing maps neural network [3] traffic state classification k-medoids algorithm [4] road network level identification k-means algorithm [5] traffic congestion analysis grey relational clustering model [6] traffic accidents and pattern extraction ROCK algorithm [7] traffic accident pattern identification COOLCAT algorithm [8] traffic accident factor analysis k-means algorithm [9] road traffic accident modeling a comparative study of machine learning classifiers [10] traffic accident black spots identification HDBSCAN algorithm [11] traffic congestion analysis k-means algorithm [12] driving behavior risk analysis k-means algorithm [13] optimal path routing a modified K-medoids algorithm [14] analysis of pedestrian crash fatalities and severe injuries KDE method [15] traffic-management system DBSCAN agorithm [16] severity of traffic accident analysis DBSCAN algorithm [17] highway safety assessment k-means algorithm [18] pedestrian crash severity analysis KDE method [19] detection of road segments of spatially prolonged and high traffic accident risk a clustering algorithm based on the Gestalt principle of proximity…”

Analysis of traffic accidents in the conurbation of Viña del Mar and Valparaíso using the spatial tool KDE+

“…En Chile, en el año 2019, según datos distribuidos por la Comisión Nacional de Seguridad de Tránsito (CONASET), se registraron 1.617 fallecidos de un total de 89.983 accidentes viales. Esta cifra en mortalidad por accidentes vehiculares tiene una tendencia a seguir aumentando a pesar de la promulgación de diferentes leyes en Chile que sancionan drásticamente a los conductores [3]. Además, en el mismo año, se constataron 45.731 (50.8%) accidentes de tránsito a causa de la imprudencia del conductor dejando 415 fallecidos y 24.257 lesionados.…”