Parametric and Non-Parametric Analyses for Pedestrian Crash Severity Prediction in Great Britain

Maria Rella Riccardi, Filomena Mauriello, Sobhan Sarkar, Francesco Galante, Antonella Scarano and Alfonso Montella
Additional contact information
Maria Rella Riccardi: Department of Civil, Architectural and Environmental Engineering, University of Naples Federico II, 80125 Naples, Italy
Filomena Mauriello: Department of Civil, Architectural and Environmental Engineering, University of Naples Federico II, 80125 Naples, Italy
Sobhan Sarkar: Information Systems & Business Analytics, Indian Institute of Management Ranchi, Ranchi 834 008, India
Francesco Galante: Department of Civil, Architectural and Environmental Engineering, University of Naples Federico II, 80125 Naples, Italy
Antonella Scarano: Department of Civil, Architectural and Environmental Engineering, University of Naples Federico II, 80125 Naples, Italy
Alfonso Montella: Department of Civil, Architectural and Environmental Engineering, University of Naples Federico II, 80125 Naples, Italy

Sustainability, 2022, vol. 14, issue 6, 1-44

Abstract: The study aims to investigate the factors that are associated with fatal and severe vehicle–pedestrian crashes in Great Britain by developing four parametric models and five non-parametric tools to predict the crash severity. Even though the models have already been applied to model the pedestrian injury severity, a comparative analysis to assess the predictive power of such modeling techniques is limited. Hence, this study contributes to the road safety literature by comparing the models by their capabilities of identifying the significant explanatory variables, and by their performances in terms of the F-measure, the G-mean, and the area under curve. The analyses were carried out using data that refer to the vehicle–pedestrian crashes that occurred in the period of 2016–2018. The parametric models confirm their advantages in offering easy-to-interpret outputs and understandable relations between the dependent and independent variables, whereas the non-parametric tools exhibited higher classification accuracies, identified more explanatory variables, and provided insights into the interdependencies among the factors. The study results suggest that the combined use of parametric and non-parametric methods may effectively overcome the limits of each group of methods, with satisfactory prediction accuracies and the interpretation of the factors contributing to fatal and serious crashes. In the conclusion, several engineering, social, and management pedestrian safety countermeasures are recommended.

Keywords: random parameter multinomial logit; ordered logit; association rules; classification trees; random forests; artificial neural networks; support vector machines; pedestrian crashes (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://www.mdpi.com/2071-1050/14/6/3188/pdf (application/pdf)
https://www.mdpi.com/2071-1050/14/6/3188/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:14:y:2022:i:6:p:3188-:d:766810

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().