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An Advanced Machine Learning Approach to Predicting Pedestrian Fatality Caused by Road Crashes: A Step toward Sustainable Pedestrian Safety
Wenlong Tao, Mahdi Aghaabbasi, Mujahid Ali, Abdulrazak H. Almaliki, Rosilawati Zainol, Abdulrhman A. Almaliki and Enas E. Hussein Sustainability 14(4) 2436 (2022) https://doi.org/10.3390/su14042436
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
Road Safety in the Romanian Cities. A Study on Urban Road Traffic Crashes
Vasile ZOTIC, Diana-Elena ALEXANDRU and István Oliver EGRESI Journal of Settlements and Spatial Planning 12(2) 131 (2021) https://doi.org/10.24193/JSSP.2021.2.06
Modelling the severity of pedestrian injury in pedestrian—vehicle crashes in North Carolina: A partial proportional odds logit model approach
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
Big Data Analytics in Traffic and Transportation Engineering