Identification of contributing factors and hotspots of pedestrian-vehicle collisions in urban areas: An alternative hybrid method for small cities.
TRAFFIC INJURY PREVENTION 2022;
23:416-421. [PMID:
35913842 DOI:
10.1080/15389588.2022.2104838]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Revised: 07/06/2022] [Accepted: 07/15/2022] [Indexed: 06/15/2023]
Abstract
OBJECTIVE
Traffic safety numbers in developing countries show a serious global problem that requires studies and actions to promote safety for the most vulnerable users, such as pedestrians, as a way of contributing to sustainable urban mobility. Therefore, this research proposes a hybrid method to identify contributing factors and critical points according to the profile of crashes involving pedestrians being run over by motor vehicles in small cities.
METHODS
The proposed method uses multivariate data analysis, specifically Exploratory Factor Analysis (EFA) and Cluster Analysis (CA), combined with geospatial tools. The study scenario corresponds to pedestrian-vehicle collisions that occurred in the urban area of Cachoeira do Sul, a small city in Rio Grande do Sul, Brazil, recorded in police reports between 2015 and 2018. Additionally, Geographic Information Tools (GIS) were used to collect characteristics of the road environment.
RESULTS
According to EFA, a set of attributes configured as contributing factors are discussed according to the components of the traffic system: user, road, vehicle, and environment. Subsequently, a CA was applied with the road-environmental factors, and three pedestrian-vehicle collision profiles were extracted, with clustering according to road segments with homogeneous characteristics. Moreover, a Kernel Density Estimate (KDE) was used to obtain heat maps of the pedestrian collisions profiles. The results allowed interventions to be proposed according to the contributing factors of each profile.
CONCLUSIONS
The multivariate statistical analysis supported the discussion of the factors contributing to the occurrence of pedestrian-vehicle collisions in small cities in developing countries. The spatial visualization of the results showed urban corridors with a high risk for pedestrians and the identification of hotspots in the city. In addition, the study demonstrated that the hybrid method tested is an alternative for the government of cities with few resources to promote safety mobility.
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