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

Arsalan Esmaili, Kayvan Aghabayk () and Nirajan Shiwakoti
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Arsalan Esmaili: School of Civil Engineering, College of Engineering, University of Tehran, Tehran 1417935840, Iran
Kayvan Aghabayk: School of Civil Engineering, College of Engineering, University of Tehran, Tehran 1417935840, Iran
Nirajan Shiwakoti: School of Engineering, RMIT University, Melbourne 3001, Australia

Sustainability, 2022, vol. 15, issue 1, 1-29

Abstract: 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.

Keywords: pedestrian; traffic crashes; injury severity analysis; unobserved heterogeneity; latent class cluster analysis; mixed logit model; developing country (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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