Liping Fu
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Liping Fu has a total of 3 co-authors. Here are the top co-authors by number of shared publications:
- Qiangqiang Shangguan
Tongji University
1 shared publication - Yubo Jiao
Department of Civil Engineering, McGill University, Montréal, QC, Canada
1 shared publication - Yubo Wang
Department of Civil and Environmental Engineering, University of Waterloo, Waterloo, ON N2L3G1, Canada
1 shared publication
Latest Publications
- Open Access
- 18 August 2025
- Published by MDPI AG in Infrastructures
- Vol. 10 (8) , 216
Exploring Causal Factor in Highway–Railroad-Grade Crossing Crashes: A Comparative Analysis
Identification of causal factors in traffic crashes has always been a significant challenge in road safety studies. Traditional crash prediction models are limited in elucidating the underlying causal mechanisms in road crashes. This research explores the application of three graphic models, namely, the Gaussian graphical model (GGM), causal Bayesian network (CBN) and graphic extreme gradient boosting (XGBoost), through a case study using highway–railroad-grade crossing (HRGC) inventory and collision data from Canada. The three modelling approaches have generally yielded consistent findings on various risk factors such as crossing control type, track angle, and exposure, showing their potential for identifying causal relationships through the interpretation of causal graphs. With the ability to make better causal inferences from crash data, the effectiveness of safety countermeasures could be more accurately and reliably estimated.
- 18 August 2025
- Published by MDPI AG in Infrastructures
- Vol. 10 (8) , 216
Open AccessExploring Causal Factor in Highway–Railroad-Grade Crossing Crashes: A Comparative Analysis
Identification of causal factors in traffic crashes has always been a significant challenge in road safety studies. Traditional crash prediction models are limited in elucidating the underlying causal mechanisms in road crashes. This research explores the application of three graphic models, namely, the Gaussian graphical model (GGM), causal Bayesian network (CBN) and graphic extreme gradient boosting (XGBoost), through a case study using highway–railroad-grade crossing (HRGC) inventory and collision data from Canada. The three modelling approaches have generally yielded consistent findings on various risk factors such as crossing control type, track angle, and exposure, showing their potential for identifying causal relationships through the interpretation of causal graphs. With the ability to make better causal inferences from crash data, the effectiveness of safety countermeasures could be more accurately and reliably estimated.