An Integrated Model for the Geohazard Accident Duration on a Regional Mountain Road Network Using Text Data

Abstract

A mountainous road network with special geological and meteorological characteristics is extremely vulnerable to nonrecurring accidents, such as traffic crashes and geohazard breakdowns. Geohazard accidents significantly impact the operation of the road network. Timely and accurate prediction of how long geohazard accidents will last is of significant importance to regional traffic safety management and control schemes. However, none of the existing studies focus on the topic of predicting geohazard accident duration on regional large-scale road networks. To fill this gap, this paper proposes an approach integrated with the Kaplan–Meier (K-M) model and random survival forest (RSF) model for geohazard accident duration prediction based on text data collected from mountainous road networks in Yunnan, China. The results indicate that geohazard accidents in road networks have a strong aggregation in tectonically active, steep mountainous, and fragmented areas. Especially the time of the rainy season, and the morning peak, brings high incident occurrences. In addition, accident type, secondary accidents, impounded vehicles or personnel, morning rush hour, closed roads, and accident management level significantly affect the duration of road geohazards. The RSF model was 0.756 and 0.867 in terms of the C-index and the average area under the curve, respectively, outperforming the traditional hazard model (Cox proportional hazards regression) and other survival machine learning models (survival support vector machine). Without censored data, the mean absolute error and mean squared error of the RSF model were 11.32 and 346.99, respectively, which were higher than the machine learning models (random forest and extreme gradient boosting model).

Comparative Study of Survival Support Vector Machine and Random Survival Forest in Survival Data

“…In contrast to the results obtained in scenarios with binary predictor variables, in this case, the performance of Random Survival Forest is superior compared to the Survival Support Vector Machine with 3 kinds of Kernel Functions and applies to all n used in this study, then in terms of performance in proceed by SSVM(ADD), SSVM(RBF) and SSVM(LIN) methods. This is also in accordance with research that was conducted by [19] & [20] in real case with mixed predictor variables, the result was that RSF was better at predicting compared to CPH and SSVM.…”

Research on Mortality Risk of Chinese Older Adults from the Perspective of Social Health

“…Unlike Kaplan-Meier curves and logrank tests, which are commonly used for monovariate analysis, Cox can be used for multifactor survival analysis. Moreover, Cox can include categorical variables (e.g., gender) and also numerical variables (e.g., age), whereas Kaplan-Meier curves and logrank tests can only include categorical variables [28,29]. Cox has a wider application since it extends survival analysis to simultaneously evaluate the effect of numerous risk factors on survival time [30].…”