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Cited article:

An Advanced Machine Learning Approach to Predicting Pedestrian Fatality Caused by Road Crashes: A Step toward Sustainable Pedestrian Safety

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Sustainability 14 (4) 2436 (2022)
https://doi.org/10.3390/su14042436

Pedestrian injury outcomes in the developing urban metropolis: Econometric models for assessing risk factors

Shahnewaz Hasanat-E-Rabbi, Md Asif Raihan, S.M. Sohel Mahmud and Md. Shamsul Hoque
IATSS Research 46 (2) 269 (2022)
https://doi.org/10.1016/j.iatssr.2022.01.002

Road Safety in the Romanian Cities. A Study on Urban Road Traffic Crashes

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https://doi.org/10.24193/JSSP.2021.2.06

Day-of-the-week variations and temporal instability of factors influencing pedestrian injury severity in pedestrian-vehicle crashes: A random parameters logit approach with heterogeneity in means and variances

Yang Li, Li Song and Wei (David) Fan
Analytic Methods in Accident Research 29 100152 (2021)
https://doi.org/10.1016/j.amar.2020.100152

Modelling the severity of pedestrian injury in pedestrian—vehicle crashes in North Carolina: A partial proportional odds logit model approach

Yang Li and Wei (David) Fan
Journal of Transportation Safety & Security 12 (3) 358 (2020)
https://doi.org/10.1080/19439962.2018.1483989

Pedestrian Injury Severities in Pedestrian-Vehicle Crashes and the Partial Proportional Odds Logit Model: Accounting for Age Difference

Yang Li and Wei (David) Fan
Transportation Research Record: Journal of the Transportation Research Board 2673 (5) 731 (2019)
https://doi.org/10.1177/0361198119842828