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

Shumin Bai, Xiaofeng Ji (), Bingyou Dai, Yongming Pu and Wenwen Qin
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Shumin Bai: School of Traffic Engineering, Kunming University of Science and Technology, Kunming 650500, China
Xiaofeng Ji: School of Traffic Engineering, Kunming University of Science and Technology, Kunming 650500, China
Bingyou Dai: School of Traffic Engineering, Kunming University of Science and Technology, Kunming 650500, China
Yongming Pu: School of Traffic Engineering, Kunming University of Science and Technology, Kunming 650500, China
Wenwen Qin: School of Traffic Engineering, Kunming University of Science and Technology, Kunming 650500, China

Sustainability, 2022, vol. 14, issue 19, 1-19

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).

Keywords: traffic safety management; road geohazard incident duration; mountain road network; Kaplan–Meier regression; random survival forest; text data (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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