Guohua Liang

Dr. Guohua Liang


Research Focus & Metrics

Academic Career Length

14Years (2013-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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27 November 2018 in Sustainability
Verified
Journal Article
Open access
PDF file
YDYajuan Deng
Yajuan Deng
XRXiaolei Ru
Xiaolei Ru
ZDZiqi Dou
Ziqi Dou
GLGuohua Liang
Guohua Liang
Bus bridging has been widely used to connect stations affected by urban rail transit disruptions. This paper designs bus bridging routes for passengers in case of urban rail transit disruption. The types of urban rail transit disruption between Origin-Destination stations are summarized, and alternative bus bridging routes are listed. First, the feasible route generation method is established. Feasible routes for each pair of the disruption Origin-Destination stations include urban rail transit transfer, direct bus bridging, and indirect bus bridging. Then the feasible route generation model with the station capacity constraint is established. The k-short alternative routes are generated to form the bus bridging routes. Lastly, by considering the bus bridging resource constraints, the final bus bridging routes are obtained by merging and filtering the initial bridging routes. Numerical results of an illustrative network show that the bus bridging routes generated from the proposed model can significantly reduce travel delay of blocked passengers, and it is necessary to maintain the number of passengers in the urban rail transit below the station capacity threshold for ensuring a feasible routing design. One more important finding of this work is that the direct bridging route is preferred for short travel distances, while the indirect bridging route is preferred for longer travel distances. After the bridging bus routes are taken, the passenger’s total travel time is significantly lower than when no measures are taken. However, after the capacity constraint of a station is considered, the passenger’s total travel time will be increased by 3.49% compared with not considering a capacity constraint.

14 June 2024 in Accident Analysis & Prevention
Verified
Journal Article
Closed access
ZCZiyu Chen
Ziyu Chen
GLGuohua Liang
Guohua Liang
YCYixin Chen
Yixin Chen
XYXiaoyao Yang
Xiaoyao Yang
YLYue Liu
Yue Liu


18 February 2025 in Sustainability
Verified
Journal Article
Open access
PDF file
HWHongxiao Wang
Hongxiao Wang
GLGuohua Liang
Guohua Liang
Traffic violations are among the leading causes of accidents and significantly compromise urban road safety. This study analyzed traffic violation and incident data collected by automated enforcement systems in urban Beijing from 2019 to 2023, consisting of 3264 traffic accident records and 147,876 traffic violation records. Through a spatiotemporal data association method, 2126 violations directly associated with accidents were identified. The FP-growth algorithm was then applied to derive 18 robust association rules encompassing five categories of accidents and four categories of violations. The findings indicate that the correlation between traffic accidents and violations displays clear peak periods during the morning (8:00–9:00) and evening (17:00–18:00). Violations such as red light running, stopping beyond the stop line during a red light, and ignoring prohibitions strongly correlate with traffic accidents under specific spatiotemporal conditions. Illegally parked vehicles not only reduce road transport efficiency but also significantly elevate the risk of traffic accidents in the surrounding area. The association rules identified in this study can assist traffic managers in formulating more effective measures to mitigate traffic violations, tackle traffic accidents at their source, enhance urban traffic safety, and promote the long-term sustainability of urban transportation systems.

9 March 2023 in Applied Sciences
Verified
Journal Article
Open access
PDF file
RLRui Li
Rui Li
GLGuohua Liang
Guohua Liang
YCYixin Chen
Yixin Chen
DZDong Zhang
Dong Zhang
BWBaojie Wang
Baojie Wang
Roundabouts are a common traffic infrastructure, which are supposed to facilitate safe and smooth traffic flow. Electric bikes as a common traffic tool in a lot of cities in China play an important role in relieving traffic congestion due to the rapid increase of motor vehicles on roads. However, compared with cyclists, e-bikers are more vulnerable because of their higher speed when colliding with motor vehicles. In this research, the spatial-temporal distribution characteristics of conflicts between motor vehicles and electric bikes (e-bikes) at signalized roundabouts are explored. First, the time distance method was used to identify conflicts, and the time to collision (TTC) was selected as the discrimination index we proposed for two representative conflict types. On conflict heat maps and conflict distribution during one signal cycle, we found a series of spatial-temporal conflict distribution regularities. Spatially, the proposed two representative types of conflicts were mainly distributed at exit areas and near the outermost circulatory lanes. Conflict Type 1 was mainly distributed around the outermost motor vehicle lanes, and Type 2 was mainly distributed inside the outermost vehicle lanes and behind the second stop line. In time span, both types of conflicts showed rapid increase before reaching a peak at 30 percentile green time and then decreasing gradually after that peak. Type 1 presented a sharp increasing range during 0–10 percentile green time, while the sharp increasing range for Type 2 presented during the 10–20 percentile. The conclusions developed by this article could provide a theoretical basis for improving traffic safety at roundabouts.

10 September 2025 in Applied Sciences
Verified
Journal Article
Open access
PDF file
HWHongxiao Wang
Hongxiao Wang
GLGuohua Liang
Guohua Liang
With declining physical functions, elderly pedestrians face a significantly higher risk of severe injuries and fatalities in traffic accidents. This study investigates the factors influencing injury severity among elderly pedestrians using traffic accident reports collected by the Shaanxi Chang’an University Traffic Accident Evidence Identification Center, covering nationwide cases from 2023 to 2024. By analyzing 2351 accident reports involving pedestrians aged 60 and above, 31 feature variables closely related to accident severity were selected to build a predictive model based on the XGBoost algorithm. Additionally, the SHAP method was employed to perform feature attribution analysis on the model’s key variables. The experimental results show that: (1) the model achieved 86% accuracy, 83% precision, 87% recall, and an F1 score of 85%, demonstrating the reliability of XGBoost in predicting injury severity among elderly pedestrians. (2) Global analysis identified collision speed, injury location, and driver awareness as the main factors influencing injury severity. However, the key factors differ across accidents of different severity levels. (3) The effect of the same factor also varies by severity level. For example, driver awareness reduces the likelihood of minor injuries but has less impact on severe injuries or fatalities. This study provides a theoretical foundation for developing traffic safety policies targeting elderly pedestrians and contributes to effectively reducing the severity of injuries in elderly pedestrian traffic accidents.